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FINAL PROGRAM and

BOOK OF ABSTRACTS

2019 11th CAA Symposium on Fault Detection,

Supervision, and Safety for Technical Processes

(CAA SAFEPROCESS 2019)

Xiamen, China

July 05 –07, 2019

Sponsored by

Technical Committee on Fault Detection, Supervision, and Safety

for Technical Processes, Chinese Association of Automation

IEEE Beijing Section

Locally Organized by

Xiamen University

Co-Sponsored by

Academy of Opto-Electronics, Chinese Academy of Science

Fujian Association of Automation

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I

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IEEE Catalog Number: CFP19S35-ART

ISBN: 978-1-7281-0681-6

II

CONTENTS

The committees……………………………………………………..………………………………...1

Welcome Message from General Chairs………………………………………………………......3

Message from Program Committee Chairs………………………………………………………...5

Keynote Address………………………………………………………………………………………7

Transportation and Location Information ………………………………………………………… 10

Program at a Glance…………………………………………………………………………………14

Technical Programmes………………………………………………………………………………15

Author Index………………………………………………………………..…………………………25

Book of Abstracts……………………………………...…………..…………………………………32

CAA SAFEPROCESS 2019

1

Organizing Committee

Sponsor

Organizer

Technical Co-Sponsors

Advisory Committee

General Chair

General Co-Chairs

Program Committee Chairs

Regional Chairs

Members:

Technical Committee on Fault Detection, Supervision and Safety for

Technical Process, Chinese Association of Automation

IEEE Beijing Section

Xiamen University

Academy of Opto-Electronics, Chinese Academy of Science

Fujian Association of Automation

Weihua Gui, Central South University, China

Guizeng Wang, Tsinghua University, China

Deyun Xiao, Tsinghua University, China

Tianyou Chai, Northeastern University, China

Jinshou Yu, East China University of Science and Technology, China

Hongcai Zhang, Northwestern Polytechnical University

Min Tan, Institute of Automation, Chinese Academy of Sciences

Zhaotong Wu, Zhejiang University, China

Xiping Chen, LanZhou University of Technology, China

Zhiquan Wang, Nanjing University of Science and Technology, China

Donghua Zhou, Shandong University of Science and Technology, China

Huajing Fang, Huazhong University of Science and Technology, China

Changhua Hu, Rocket Force University of Engineering, China

Bin Jiang, Nanjing University of Aeronautics and Astronautics, China

Chunhua Yang, Central South University, China

Guanghong Yang, Northeastern University, China

Hao Ye, Tsinghua University, China

Yinzhong Ye, Shanghai Urban Construction Vocational College, China

Maiying Zhong, Shandong University of Science and Technology, China

Zhibin Li, Academy of Opto-Electronics, Chinese Academy of Science, China

Baigen Cai, Beijing Jiaotong Univerisity, China

Canada: Youmin Zhang, Concordia University, Canada

USA: S. Joe Qin, University of Southern California, USA

Europe: Steven X. Ding, University of Duisburg-Essen, Germany

Hongkong: Furong Gao, The Hong Kong University of Science and Technology,

China

Cuimei Bo, Yi Chai, Maoyin Chen, Yong Chen, Yuhua Cheng, Steven X. Ding,

Haiying Dong, Hairong Dong, Hongli Dong, Jian Feng, Huajing Fang, Furong

Gao, Huijun Gao, Xuejin Gao, Zhiqiang Ge, Zhiqiang Geng, Weihua Gui, Limin

Guo, Cunwu Han, Kuangrong Hao, Xiao He, Yandong Hou, Changhua Hu,

Shaolin Hu, Qinglei Hu, Darong Huang, Daoping Huang, Deqing Huang, Bin

Jiang, Jin Jiang, Qingchao Jiang, Xiangyu Kong, Yaguo Lei, Juan Li, Ping Li,

Tao Li, Wei Li, Yuan Li, Zetao Li, Zhijun Li, Zhibin Li, Jun Liang, Yan Liang,

Yiqi Liu, Zhenxing Liu, Ningyun Lu, Shuxian Lun, Hao Luo, Linkai Luo, Chen

Lv, Feng Lv, Jie Ma, Zehui Mao, Yuguang Niu, Quan Pan, Zhonghua

Pang, Kaixiang Peng, Ruiyun Qi, S. Joe Qin, Qunli Shang, Hongbo Shi, Bo

Shen, Qikun Shen, Yi Shen, Li Sheng, Zhihuan Song, Dehui Sun, Guoxi Sun,

Jitao Sun, Qiuye Sun, Tianhao Tang, Xuemin Tian, Cong Wang, Fuli Wang,

Haibo Wang, Hong Wang, Jing Wang, Jiandong Wang, Limin Wang, Peiliang

Wang, Qing Wang, Qinglin Wang, Shaoping Wang, Tianzhen Wang, Xiuqing


2

Organizing Committee Chair

Organizing Committee Vice-Chair

Publication Chair

Publication Co-Chairs

Fang Chong-zhi Excellent Paper Award

Committee

Poster Session Chairs

Poster Session Co-Chairs

Invited Session Chairs

Secretariat

Wang, Youqing Wang, Zhanshan Wang, Zhuo Wang, Guoliang Wei, Chenglin

Wen, Zhengxin Wen, Huaining Wu, Lan Wu, Ligang Wu, Lei Xie, Linbai Xie,

Zhengguo Xu, Xiaobin Xu, Xuefeng Yan, Liping Yan, Chunhua

Yang, Guanghong Yang, Hao Yang, Shixi Yang, Xu Yang, Ying Yang, Lina

Yao, Hao Ye, Dan Ye, Yinzhong Ye, Shen Yin, Haiwen Yuan, Jiusun

Zeng, Qingjun Zeng, Bangcheng Zhang, Beike Zhang, Dengfeng Zhang,

Huaguang Zhang, Ke Zhang, Ping Zhang, Mingjun Zhang, Qi Zhang, Qinghua

Zhang, Yingwei Zhang, Youming Zhang, Zhenwei Zhang, Chunhui

Zhao, Ying Zheng, Maiying Zhong, Chunjie zhou, Zhijie Zhou, Donghua

Zhou, Yuewen Zhou, Daqi Zhu, Fanglai Zhu, Qing Zhao, Qun Zong, James

Lam

Weiyao Lan, Xiamen University, China

Linkai Luo, Xiamen University, China

Delin Luo, Xiamen University, China

Mengqi Zhou, IEEE Beijing Section, China

Youqing Wang, Shandong University of Science and Technology, China

Huiyu Jin, Xiamen University, China

Chair,Changhua Hu, Rocket Force University of Engineering, China

The Award Committee comprises of 5-7 invited internationally renowned

experts in the areas of the symposium.

Xiao He, Tsinghua University, China

Jianping Zeng, Xiamen University, China

Ying Yang, Peking University, China

Ming Gao, China University of Petroleum (Hua Dong), China

Darong Huang, Chongqing Jiaotong University, China

Bangcheng Zhang, Changchun University of Technology, China

Xiaomei Fu, Xiamen University, China


3

Welcome Message from General Chairs

Donghua Zhou

General Chair of CAA SAFEPROCESS 2019

Dear Friends and Colleagues,

2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes

(SAFEPROCESS 2019) will be held on July 5-7 2019 at Xiamen city, Fujian Province, China.

CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) is

a biennial forum of excellence, which was organized by the Technical Committee on SAFEPROCESS,

Chinese Association of Automation (CAA). This year, the IEEE Beijing Section joined us as a co-sponsor of

this symposium. Correspondingly, all accepted papers will be included into the IEEE Xplore Digital Library.

Furthermore, Xiamen University is the local organizer of SAFEPROCESS 2019.

The conference provides an up-to-date and comprehensive picture of safety methods and techniques, with

a wide coverage of application fields. This year, three world-class scholars will give plenary presentations

for audiences.

Xiamen is a coastal resort city and is recognized as one of the most scenic places to visit in China. There

are beautiful beaches, parks, museums and interesting cultural attractions. Xiamen is a major port city with

a long history of international commerce. Greater Xiamen consists of the Island of Xiamen, Gulangyu Islet,

several small islets and a portion of the mainland. Xiamen maintains the reputation as China’s cleanest

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4

city. It is also a good idea to visit Xiamen University, which has one of the most beautiful campuses in China.

Together with the Organizing Committee, we are trying our best to ensure a diverse and brilliant program.

We wish to have the pleasure to meet you at Xiamen in July.

Donghua Zhou

General Chair of CAA SAFEPROCESS 2019


5

Message from Program Committee Chairs

Zhibin Li

Program Committee Chair

Baigen Cai

Program Committee Chair

Dear Friends and Colleagues,

On behalf of the Program Committee, it is our great honor to welcome you to the 2019 CAA Symposium on

Fault Detection, Supervision and Safety for Technical Processes (CAA SAFEPROCESS 2019) in Xiamen,

China.

SAFEPROCESS is a biennial symposium which has proven to be one of the excellent forums for scientists,

researchers, engineers, and industrial practitioners to present and discuss the latest technological

advancements as well as future directions and trends in Fault Detection, Supervision and Safety for

Technical Processes. CAA SAFEPROCESS 2019 has received enthusiastic responses with a total of 209

submissions. After a rigorous peer-review process, 175 papers were accepted and included in the

conference proceedings, which are divided into 16 oral sessions and 2 poster sessions for presentation.

Along with the parallel technical sessions, we shall have three keynote addresses to be delivered by

internationally distinguished speakers including: Prof. Ron J. Patton (University of Hull, UK), Prof. Biao

Huang (University of Alberta, Canada), and Prof. Bin Jiang (Nanjing University of Aeronautics and

Astronautics, China). These lectures will address the state-of-the-art developments and leading-edge

research topics in both theory and applications in Fault Detection, Supervision and Safety for Technical

Processes.

To commemorate Prof. Fang Chong-zhi’s contribution and promote the development of Fault Detection,


6

Supervision and Safety for Technical Processes, we will present the “Fang Chong-zhi Excellent Paper

Award”. Based on reviewers' comments and nominations as well as the evaluations of Program Committee

members, 7 papers were selected as the finalists for the award. During the conference, the oral

presentations of the 7 finalists will be further assessed and the winner will be determined by the Fang Chong-

zhi Excellent Paper Award Committee after the oral presentations.

A U-disk containing the PDF files of all papers scheduled in the program and an Abstract Book will be

provided at the conference to each registered participant as part of the registration material. The official

conference proceedings will be published by the IEEE and included in the IEEE Xplore Database.

On behalf of the Program Committee, we would like to thank all reviewers for giving time and expertise to

provide comments, which are important to the Committee in making a fair decision. We sincerely thank the

invited session organizers and all the members of the Program Committee for their dedicated work and

thorough effort. We also convey our heartfelt thanks to the authors, plenary speakers, the session chairs,

and the volunteers, without whose participation and contribution the CAA SAFEPROCESS 2019 program is

impossible.

We do hope you enjoy the Symposium as well as Xiamen.

Zhibin Li Baigen Cai

Program Committee Chair Program Committee Chair


7

Keynote Address

Keynote Address 1

An Application-Focused Approach to Fault Tolerant Control

Prof. Ron J. Patton, University of Hull, UK Room 312, 9:10-9:50, Saturday, July 06, 2019

Abstract

The purpose of Fault tolerant control is to maintain control system performance in the presence of faults in

actuators, sensors or changes of system parameters. The latter can be viewed either as “parametric faults” or

unusual/uncertain system changes. By appropriate feedback design the system can be made to have “fault

tolerant” properties. Related to this is the idea that the closed-loop system can be made to be robust against

parametric changes. One can see very quickly that robustness and fault tolerance are closely related concepts, i.e.

that parametric changes and faults are probably unwanted effects in closed-loop system behaviour. It follows

therefore that a fault tolerant controller is a way of implementing robust control using a variety of powerful

methods. An important strategy is to estimate uncertain parameters or faults in a robust way and then compensate

their system effect within the control system. Both robustness to uncertainty and fault tolerance can be handled

via a joint or integrated problem of estimation and control. Closely related to this is the Separation Principle, well-

known in control theory for 45 years. Against this background it is important to understand the “joint robustness”

that comes about because of the need to consider robustness of the estimation to uncertain control variations and

also uncertainty of the control, given that parameters or faults are being estimated to achieve fault tolerance. The

lecture will outline these principles, making definitions and outlining the background topics leading to strategies

for achieving good fault tolerant control and robustness for real application problems. Case studies chosen from

flight control and wind turbine systems will be outlined to illustrate the concepts.

Ron J. Patton was born in Peru in 1949. He graduated at Sheffield University with

BEng, MEng, PhD degrees in Electrical & Electronic Engineering and Control

Systems. Ron holds the Chair in Control & Intelligent Systems Engineering at Hull

University and has made a substantial contribution during 38 years to the field of

modelling and Robust methods for FDI/FDD (fault detection and isolation/fault

detection and diagnosis) and Fault-Tolerant Control (FTC) in dynamic systems. He

has Hirsch Index h_58 and is author of 428 papers, including 158 archival journal

papers and 5 books. Ron is Subject Editor of the Wiley Journal of Adaptive

Control & Signal Processing. He has served on editorial boards of several other

Journals in Control Engineering. During 1996-2002 Ron chaired the IFAC

Safeprocess Technical Committee providing the mechanism for running of the 2006

IFAC Safeprocess 2006 Symposium held at Tsinghua University. Ron coordinated

the EU research projects IQ2FD [1997-2000] and DAMADICS [2000-2004] and contributed to FP6 NeCST

[2004-2007] and FP7 ADDSAFE [2010-2013], as well as to 13 UK research council grants. Current research

interests are: Robust multiple-model and de-centralized strategies for FDI/FDD & FTC and Renewable Energy

including mitigation of unbalanced loads in wind turbines and wave to wire control of wave energy conversion.

He is Life Fellow of IEEE, Senior Member of AIAA and Fellow of the Institute of Measurement and Control.


8

Keynote Address 2

Machine Learning - Introduction and Application in Fault-tolerant Control

Prof. Biao Huang, University of Alberta, Canada Room 312, 10:20-11:00, Saturday, July 06, 2019

Abstract

Modern industries are awash with a large amount of data. Extraction of information and knowledge discovery

from data for process design, control and optimization, from day by day routine process operating data, is

especially interesting but also challenging. Data analytics has played an important role in the safe operation of

process systems, particularly in traditional data-based failure prediction. On the other hand, modern machine

learning technologies, especially deep learning and reinforcement learning techniques, have gained significant

progress, attracting significant interests from engineering communities. This presentation will introduce some of

advanced machine learning technologies along with their illustrative examples, followed by an exploratory study

on fault-tolerant reinforcement learning control.

Biao Huang received his Ph.D. degree in Process Control from the University of

Alberta, Canada, in 1997. He held MSc degree (1986) and BSc degree (1983) in

Automatic Control from the Beijing University of Aeronautics and Astronautics. He

joined the University of Alberta in 1997 as an Assistant Professor in the Department

of Chemical and Materials Engineering and is currently a Full Professor, and NSERC

Senior Industrial Research Chair in Control of Oil Sands Processes. He is an IEEE

Fellow, Fellow of the Canadian Academy of Engineering, and Fellow of the

Chemical Institute of Canada. He is a recipient of many awards including Alexander

von Humboldt Research Fellowship from Germany, Best Paper award from IFAC

Journal of Process Control, APEGA Summit Award in Research Excellence, and

Bantrel Award in Design and Industrial Practice, etc. He has published five books

and over 350 peer-reviewed SCI journal papers. His research interests include process

control, data analytics, machine learning, Bayesian inference. He is currently the Editor-in-Chief for IFAC Journal

Control Engineering Practice, Subject Editor for Journal of the Franklin Institute, and Associate Editor for Journal

of Process Control.


9

Keynote Address 3

Fault Diagnosis and Fault-tolerant control for High-speed Train Traction Systems

Prof. Bin Jiang, Nanjing University of Aeronautics and Astronautics, China Room 312, 11:00-11:50, Saturday, July 06, 2019

Abstract

High-speed train with its reliable, fast and high loading capacities, has attracted more and more attention in

the recent years. The traction system generating the traction/breaking force consists of rectifiers, inverters, PWMs

(pulse width modulations), traction motors, and mechanical drives, etc., which may occur faults with the long

time (distance) operation that leads to the delay and even stop of the train. Thus, it is critical to study fault diagnosis

and fault-tolerant control (failure compensation) problem for traction systems in high-speed trains. In this talk,

new fault diagnosis and prognosis methods for incipient and composite faults are discussed, aiming at solving the

key scientific problems, such as fault modeling, diagnosability analysis, incipient fault diagnosis in presence of

disturbances, early diagnosis and prognosis of incipient faults, and real-time diagnosis of composite faults. Also

the experiments results on the semi-physical platform are shown to verify the potential applications of the

proposed methods in the real trains.

Bin Jiang received the Ph.D. degree in Automatic Control from Northeastern

University, Shenyang, China, in 1995. He had ever been postdoctoral fellow, research

fellow, invited professor and visiting professor in Singapore, France, USA and Canada,

respectively. Now he is a Chair Professor of Cheung Kong Scholar Program in Ministry

of Education and Vice President of Nanjing University of Aeronautics and Astronautics,

China. He serves as Associate Editor or Editorial Board Member for a number of

journals such as IEEE Trans. On Control Systems Technology; Int. J. Of Control,

Automation and Systems; Neurocomputing; Journal of Astronautics; Control and

Decision, Systems Engineering and Electronics Technologies, etc. He is a senior

member of IEEE, Chair of Control Systems Chapter in IEEE Nanjing Section, a

member of IFAC Technical Committee on Fault Detection, Supervision, and Safety of

Technical Processes. His research interests include fault diagnosis and fault tolerant

control and their applications to helicopters, satellites and high-speed trains.

He has been the principle investigator on several projects of National Natural Science Foundation of China.

He is the author of 8 books and over 200 referred international journal papers and conference papers. He won

Second Class Prize of National Natural Science Award of of China in 2018.


10

Transportation and Location Information

Local Information

Conference Venue

The 2019 11th CAA Symposium on Fault Detection, Supervision and Safety for Technical

Processes (CAA SAFEPROCESS 2019) will be held Friday through Sunday, July 5-7, 2019,

Xiamen, China. CAA SAFEPROCESS 2019 will take place at Xiamen Xing Lin Wan Hotel.

Address: NO.301 Xingbin Road, Jimei District, Xiamen 361022, Fujian, P. R. China

Transportation

AIR: Xiamen Gaoqi International Airport to Xiamen Xing Lin Wan Hotel (About 12.7 km/40 RMB for a taxi/18 to 27 minutes)


11

Train: Xiamen Railway Station to Xiamen Xing Lin Wan Hotel

(About 19 km/60 RMB for a taxi/25 to 31 minutes)

Train: Xiamen North Railway Station to Xiamen Xing Lin Wan Hotel

(About 14 km/45 RMB for a taxi/19 to 25 minutes)


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Floor Plan of Xiamen Xing Lin Wan Hotel

Distribution Map for Buildings in Xiamen Xing Lin Wan Hotel

厦门杏林湾大酒店楼宇分布图

#9 Building

Lobby

Xing Bing Road Exit

Seashore Entrance

Administrative


13

The 2nd Floor Map of #9 Building

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CAA SAFEPROCESS 2019 Technical Program

Technical Program

Saturday, July 6, 2019

SaA01 13:30-17:30 #9 Bld Room 201 SaA01Salute Session:Seminar on ”Intelligent Autonomous Safety Control ofAerospace and Motion System ” in Commemoration of Jiachi Yang ’sCentenary BirthdayChair: Li, Zhibin Academy of Opto-Electronics, CASCo-Chair: Liu, Wenjing Beijing Institute of Control EngineeringCo-Chair: Hu, Shaolin Xi’an University of Technology

ISaA01-A1 13:30-13:50Sensor fault diagnosis scheme design for spacecraft attitude controlsystem

Yuan, Li Beijing Institute of Control EngineeringWang, Shuyi Beijing Institute of Control EngineeringLiu, Wenjing Beijing Institute of Control EngineeringLiu, Chengrui Beijing Institute of Control Engineering

ISaA01-A2 13:50-14:10Design and Implementation of Autonomous Fault Detection and SafetyManagement for Geostationary Satellite Control System

Liu, Xiaoxiang Beijing Institute of Control EngineeringWang, Shuyi Beijing Institute of Control EngineeringYuan, Li Beijing Institute of Control EngineeringLiu, Yubai Beijing Institute of Control EngineeringGuo, Jianxin Beijing Institute of Control Engineering

ISaA01-A3 14:10-14:30Integrated Design of Fault Diagnosis and Fault Tolerant Control for S-pacecraft System

Zhang, Xiuyun Tianjin Univ.Zong, Qun Tianjin Univ.Liu, Wenjing Beijing Institute of Control Engineering

ISaA01-A4 14:30-14:50Direct Adaptive Fault-tolerant Control of Satellite Attitude based onBackstepping and Neural Network

Tian, Kefeng Beijing Institute of Control EngineeringShen, Shaoping Xiamen Univ.Li, Zhibin Academy of Opto-Electronics, CASQin, Huixian Academy of Opto-Electronics, CASZhang, Xiaojun Academy of Opto-Electronics, CASWang, Fan Academy of Opto-Electronics, CAS

ISaA01-A5 14:50-15:10A New Control Allocation Method Based on the Improved Grey WolfOptimizer Algorithm for Aircraft with Multiple Actuators

Gai, Wendong Shandong Univ. of Science and TechnologySun, Chengxian Shandong Univ. of Science and TechnologyZhou, Yecheng Shandong Univ. of Science and TechnologyZhang, Jing Shandong Univ. of Science and Technology

ISaA01-A6 15:10-15:30Research on the Flight Anomaly Detection During Take-off PhaseBased on FOQA Data

Jiang, YunPeng China Academy of Civil Aviation Science andTechnology

Le, NingNing China Academy of Civil Aviation Science andTechnology

Zhang, Yufeng China State Shipbuilding CorporationZheng, Yinger China Academy of Civil Aviation Science and

TechnologyJiao, Yang China Academy of Civil Aviation Science and

TechnologyISaA01-B1 15:50-16:10

Fault-tolerant control for a multi-propeller aerostat based on slidingmode control allocation method

Liang, kuankuan Shanghai Jiao Tong Univ.Chen, li Shanghai Univ. of Engineering ScienceLiu, Jinguo Shenyang Institute of Automation, CAS

ISaA01-B2 16:10-16:30Robust Fault Tolerant Control of Airship Residence Based on Lossless

Separation OperatorOuyang, Gaoxiang Techn. & Engine. Center for Space Utilization,

CASLin, Wenliang Capital Aerospace Machinery Co., LtdMiao, JingGang Academy of Opto-Electronics, CASZhang, Xiaojun Academy of Opto-Electronics, CAS

ISaA01-B3 16:30-16:50High Performance Control and Fault Tolerant Control Requirements ofAstronomical Observation Carrier Platform

Li, Zhibin Academy of Opto-Electronics, CASZhou, Jianghua Academy of Opto-Electronics, CASWu, Yunli Beijing Institute of Control EngineeringHu, Kang Academy of Opto-Electronics, CASWang, Baocheng Academy of Opto-Electronics, CASHuang, Wanning Academy of Opto-Electronics, CAS

ISaA01-B4 16:50-17:10Effect of Manned Submersible Operation on Structural Safety

Qin, Shengjie National Deep Sea CenterZhang, Yi National Deep Sea CenterLiu, Xiaohui National Deep Sea CenterYang, Lei National Deep Sea CenterLiu, Baohua National Deep Sea CenterTang, Miao National Deep Sea CenterZou, Xiangyi National Deep Sea Center

ISaA01-B5 17:10-17:30Safety Assessment of the JiaoLong Deep-sea Manned Submersiblebased on Bayesian Network

Liu, Chang Tsinghua Univ.Zhang, Yi Harbin Engineering Univ.He, Xiao Tsinghua Univ.

SaA02 13:30–17:00 Room 307 SaA02Award Session: Fang Chong-zhi Excellent Paper Award FinalChair: Hu, Changhua The Rocket Force University of EngineeringCo-Chair: He, Xiao Tsinghua University

ISaA02-1 13:30–13:55Remaining useful Life Prediction of Lithium Ion Battery Based on Im-proved Particle Filter Algorithm

Peng, Xi Xi’an Univ. of TechnologyXie, Guo Xi’an Univ. of TechnologyLi, Xin Xi’an Univ. of TechnologyHu, Shaolin Xi’an Univ. of TechnologyHei, Xinhong Xi’an Univ. of Technology

ISaA02-2 13:55–14:20A Geometric Approach to Fault Detection and Isolation of LinearDiscrete-time Systems

Zhang, Zhao Tsinghua Univ.Huang, Jie Tsinghua Univ.He, Xiao Tsinghua Univ.

ISaA02-3 14:20–14:45Fault diagnosis based on EEMD and key feature representation withseparation of stationary and nonstationary signals

Tian, Feng Zhejiang Univ.Zhao, Chunhui Zhejiang Univ.Fan, Haidong Zhejiang Ene. Group Resea. Inst.Zheng, Weijian Zhejiang Ene. Group Resea. Inst.Sun, Youxian Zhejiang Univ.

ISaA02-4 14:45–15:10Part Mutual Information Based Quality-related Component Analysis forFault Detection

Wang, Yanwen Tsinghua Univ.Chen, Maoyin Tsinghua Univ.Zhou, Donghua Shandong Univ. of Scie. and Techn.

ISaA02-5 15:10–15:35

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Final Program CAA SAFEPROCESS 2019

Multimode Process Monitoring with Mode Transition ConstraintsWu, Dehao Tsinghua Univ.Chen, Maoyin Tsinghua Univ.Zhou,Donghua Shandong Univ.of Scie. and Techn.

ISaA02-6 15:35–16:00Fault-tolerant Cooperative Formation Control for Multi-agent Systemswith Actuator Faults

Shi, Jiantao Nanjing Resea. Inst. of Elect. Techn.Zhou, Donghua Shandong Univ. of Scie. and Techn.Sun, Jun Nanjing Resea. Inst. of Elect. Techn.Yang, Yuhao Nanjing Resea. Inst. of Elect. Techn.

ISaA02-7 16:00–16:25Deep Learning for Quality Prediction of Nonlinear Dynamic Processeswith Variable Attention-Based Long Short-Term Memory Network

Yuan, Xiaofeng central south Univ.Li, Lin central south Univ.Wang, Yalin central south Univ.Yang, Chunhua central south Univ.Gui, Weihua central south Univ.

SaA03 13:30–15:30 Room 208SaA03Model based diagnosis-1Chair: Zhong, Maiying Shandong Univ. of Science and TechnologyChair: Mao, Zehui Nanjing Univ. of Aeron. and Astron.

ISaA03-1 13:30–13:50The parity space-based fault detection for linear discrete time systemswith integral measurements

Zhu, Xiaoqiang Shandong Univ. of Science and TechnologyFang, Jingzhong Shandong Univ. of Science and TechnologyZhong, Maiying Shandong Univ. of Science and TechnologyLiu, Yang Shandong Univ. of Science and Technology

ISaA03-2 13:50–14:10Open-Circuit Fault Diagnostic Method for Three-level Inverters Basedon Park’s Vector

Wen, Haoqiao Central South Univ.Peng, Tao Central South Univ.Tao, Hongwei Central South Univ.Yang, Chao Central South Univ.

ISaA03-3 14:10–14:30Application of Hybrid Integrated Model Based on Mechanism ModelResiduals in Fiber Production Melt Conveying

Gong, Longhao Donghua Univ.Hao, Kuangrong Donghua Univ.Ren, Lihong Donghua Univ.

ISaA03-4 14:30–14:50Actuator Fault Detection for Autonomous Underwater Vehicle Using In-terval Observer

Zhang, Chunming Shandong Univ. of Science and TechnologyWang, Xianghua Shandong Univ. of Science and TechnologyRen, Yanheng Shandong Univ. of Science and Technology

ISaA03-5 14:50–15:10Research on Fault Estimation and Fault-tolerant Control of HypersonicAircraft Based on Adaptive Observer

Fang, Yifan Nanjing Univ. of Aeron. and Astron.Jiang, Bin Nanjing Univ. of Aeron. and Astron.Lv, Xunhong Nanjing Univ. of Aeron. and Astron.Mao, Zehui Nanjing Univ. of Aeron. and Astron.

ISaA03-6 15:10–15:30Detecting and Estimating Intermittent Actuator Faults in Linear S-tochastic Systems

Yan, Rongyi Beijing Instit. of Elect. EngineeringXia, Hui Beijing Instit. of Elect. EngineeringLiang, Tong Beijing Instit. of Elect. EngineeringHe, Xiao Tsinghua Univ.

SaA04 13:30–15:30 Room 207SaA04DDD methods-1Chair: Song, Zhihuan Zhejiang Univ.Chair: Li, Yuan Shenyang Univ. of Chemical Techn.

ISaA04-1 13:30–13:50Back-propagation Based Contribution for nonlinear fault diagnosis

Qian, Jinchuan Zhejiang Univ.Jiang, Li Zhejiang Univ.

Song, Zhihuan Zhejiang Univ.Ge, Zhiqiang Zhejiang Univ.

ISaA04-2 13:50–14:10Industrial Process Fault Classification Based on Weighted Stacked Ex-treme Learning Machine

Gao, Kai China Univ. of PetroleumDeng, Xiaogang China Univ. of PetroleumCao, Yuping China Univ. of Petroleum

ISaA04-3 14:10–14:30Fault Diagnosis of Chemical Processes Based on k-NN Distance Con-tribution Analysis Method

Wang, Guozhu Henan Institute of Tech.Du, Zhiyong Henan Institute of Tech.Hu, Yongtao Henan Institute of Tech.Li, Yuan Shenyang Univ. of Chemical Tech.

ISaA04-4 14:30–14:50Bearing Fault Detection and Separation of Wind Turbine Based on Arti-ficial Immune System

Ren, Yanheng Shandong Univ. of Sci. and Tech.Wang, Xianghua Shandong Univ. of Sci. and Tech.Chunming, Zhang Shandong Univ. of Sci. and Tech.

ISaA04-5 14:50–15:10Improved transfer component analysis and it application for bearingfault diagnosis across diverse domains

Ma, Ping Xinjiang Univ.Zhang, Hongli Xinjiang Univ.Wang, Cong Xinjiang Univ.

ISaA04-6 15:10–15:30Batch Process Monitoring Using Multi-way Laplacian Autoencoders

Gao, Xuejin Beijing Univ. of Tech.Xu, Zidong Beijing Univ. of Tech.Wang, Pu Beijing Univ. of Tech.

SaA05 13:30–15:30 Room 205SaA05Condition monitoring and fault predictionChair: Fang, Huajing Huazhong Univ. of Science and TechnologyChair: Peng, Kaixiang University of Science and Technology Beijing

ISaA05-1 13:30–13:50An Improved LSTM Neural Network with Uncertainty to Predict Remain-ingUseful Life

Wu, Rui Beijing Information Sci. and Tech. Univ.Ma, Jie Beijing Information Sci. and Tech. Univ.

ISaA05-2 13:50–14:10A Novel Scheme for Remaining Useful Life Prediction and Safety As-sessment Based on Hybrid Method

Peng,Kaixiang University of Science and Technology BeijingJiao, Ruihua University of Science and Technology BeijingZhang, Kai University of Science and Technology BeijingMa, Liang University of Science and Technology BeijingPi, Yanting University of Science and Technology Beijing

ISaA05-3 14:10–14:30RUL Prediction: Reducing Statistical Model Uncertainty Via BayesianModel Aggregation

Jia, Chao China Elect. Standard. InstituteZhang, Hanwen Zhejiang Univ.

ISaA05-4 14:30–14:50Research on Measurement Methods of Transferability between Differ-ent Domains in Transfer Learning

Qin, Ruochen Beihang Univ.Lu, Chen Beihang Univ.

ISaA05-5 14:50–15:10Centrifugal pump fault diagnosis based on MEEMD-PE Time-frequencyinformation entropy and Random forest

Wang, Yihan Beihang Univ.Liu, Hongmei Beihang Univ.

ISaA05-6 15:10–15:30Safety-Oriented Fault Monitoring for Manned Deep-Sea Submersibles

Zhang, Yi Harbin Engineering Univ.Ding, Zhongjun National Deep Sea CenterLiu, Chang Tsinghua Univ.Qi, Haibin National Deep Sea CenterZhao, Qingxin National Deep Sea Center

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CAA SAFEPROCESS 2019 Technical Program: Saturday Sessions

Huang, Jie Tsinghua Univ.He, Xiao Tsinghua Univ.

SaA06 13:30-15:30 #9 Bld Room 301 SaA06Invited Session: Data-driven fault diagnosis and health management ofindustrial systemsChair: Zheng, Ying Huazhong Univ. of Science and TechnologyCo-Chair: Liu, Zhenxing WuHan Univ. of Science and TechnologyCo-Chair: Zhang, Yong WuHan Univ. of Science and Technology

ISaA06-1 13:30-13:50Research on Remaining Useful Life Prediction Based on Nonlinear Fil-tering for Lithium-ion Battery

Xiao, Zhouxiao Huazhong Univ. of Science and TechnologyFang, Huajing Huazhong Univ. of Science and TechnologyChang, Yang Huazhong Univ. of Science and Technology

ISaA06-2 13:50-14:10Diagnose of Sub-module Fault in Modular multilevel converters Basedon Moving Average Method

Li, Cui Huanggang Normal Univ.Liu, Zhenxing WuHan Univ. of Science and TechnologyZhang, Yong WuHan Univ. of Science and Technology

ISaA06-3 14:10-14:30Fault Detection of Modular Multilevel Converter with Kalman FilterMethod

Hu, Huanzhen Wuhan Univ. of Science and TechnologyZhang, Yong Wuhan Univ. of Science and TechnologyLiu, Zhenxing Wuhan Univ. of Science and TechnologyZhao, Min Wuhan Univ. of Science and Technology

ISaA06-4 14:30-14:50Fault Detection of Lithium-Ion Batteries Subject to Probabilistic Para-metric Uncertainties

Liu, Yuhao Huazhong Univ. of Science and TechnologyWan, Yiming Huazhong Univ. of Science and Technology

ISaA06-5 14:50-15:10A Fault Detection Method with Ensemble Empirical Mode Decomposi-tion and Support Vector Data Description

Wang, Yang Huazhong Univ. of Science and TechnologyLing, Dan Zhengzhou Univ. of Light IndustryYang, Weidong Huazhong Univ. of Science and TechnologyTao, Bo Huazhong Univ. of Science and TechnologyZheng, Ying Huazhong Univ. of Science and Technology

ISaA06-6 15:10-15:30Research on Reliable High-speed Train Axle Temperature MonitoringSystem based on Fluorescence Optical Fiber Temperature Sensor

Wang, Fei Xi’an Univ. of TechnologyZheng, Liangguang Southeast Univ.Zhao, Chengrui Ningbo CRRC Times Transducer Technology CO.,

LTD

SaA07 13:30-15:30 #Bld Room 308 SaA07Invited Session: Fault Diagnosis and Forecasting Method Fusing Qual-itative Knowledge and Quantitative InformationChair: Zhang, Bangcheng Changchun Univ. of TechnologyCo-Chair: Huang, Deqing Southwest Jiaotong Univ.Co-Chair: Hu, Guanyu Hainan Normal Univ.

ISaA07-1 13:30-13:50Navigation of Simultaneous Localization and Mapping by Fusing RGB-D Camera and IMU on UAV

Dai, Xi Southwest Jiaotong Univ.Mao, Yuxin Southwest Jiaotong Univ.Huang, Tianpeng Southwest Jiaotong Univ.Li, Binbin Southwest Jiaotong Univ.Huang, Deqing Southwest Jiaotong Univ.

ISaA07-2 13:50-14:10Neural-Network-Based State and Fault Estimation for a Discrete-TimeNonlinear System

Zhang, Xiaoxiao Shandong Univ. of Science and TechnologyWang, Youqing Shandong Univ. of Science and Technology

ISaA07-3 14:10-14:30Fault Prediction of High-speed Train Running Gears Based On HiddenMarkov Model and Analytic Hierarchy Process Method

Cheng, Chao CRRC Changchun Railway Vehicles Co.LTDQiao, Xinyu Changchun Univ. of Technology

Fu, Caixin CRRC Changchun Railway Vehicles Co.LTDWang, Weijun Changchun Univ. of TechnologyYin, Xiaojing Changchun Univ. of Technology

ISaA07-4 14:30-14:50A Survey of Fault Diagnosis Methods for White body Welding Produc-tion Line

Wang, Jidong Changchun Univ. of TechnologyGao, Siyang Changchun Univ. of TechnologyZhang, Bangcheng Changchun Univ. of TechnologyLv, Shiyuan Changchun Univ. of Technology

ISaA07-5 14:50-15:10Fault Prediction of Brightness Sensor based on BRB in Rail VehicleCompartment LED Lighting System

Yin, Xiaojing Changchun Univ. of TechnologyShi, Guangxu Changchun Univ. of TechnologyZhang, Bangcheng Changchun Univ. of TechnologyLv, Shiyuan Changchun Univ. of TechnologyShao, Yubo Changchun Univ. of Technology

ISaA07-6 15:10-15:30Fault Diagnosis Method of WSN Nodes Based on Wavelet Packet andBelief Rule Base

Zhang, Bangcheng Changchun Univ. of TechnologyZhang, Yang Changchun Univ. of TechnologyZhang, Aoxiang Changchun Univ. of TechnologyWu, Lihua Hainan Normal Univ.Hu, Guanyu Hainan Normal Univ.

SaA08 13:30-15:30 #9 Bld Room新闻厅 SaA08Invited Session: Fault diagnosis and Health Management of Rail TransitChair: Cai, Bai-gen Beijing Jiaotong Univ.Co-Chair: Luo, Linkai Xiamen Univ.Co-Chair: Jin, Huiyu Xiamen Univ.

ISaA08-1 13:30-13:45Vibration Signal Analysis For Rail Flaw Detection

Li, Bin CRSC Research & Design Institute Group Co. LtdChen, Xiaoguang CRSC Research & Design Institute Group Co.

LtdWang, Zhixin CRSC Research & Design Institute Group Co. LtdTan, Shulin CRSC Research & Design Institute Group Co. Ltd

ISaA08-2 13:45-14:00Text Mining Based Identification Model for Urban Rail Transit SystemInfrastructure Fault Analysis

Zhang, Bo Beijing National Railway Research & DesignInstitute of Signal & Communication Group Co. Ltd

Li, Qing Beijing National Railway Research & DesignInstitute of Signal & Communication Group Co. Ltd

ISaA08-3 14:00-14:15Failure Recognition for Switch Machines Based on Machine Learning

Hu, Enhua CASCO Signal Ltd.Zhu, Cunren CASCO Signal Ltd.Li, Chunmei CASCO Signal Ltd.

ISaA08-4 14:15-14:30Big Data Platform for Faults Prediction Diagnosis of CBI Conferences& Symposia

Chen, Yu CASCO SIGNAL LTD.Chen, Jiyu CASCO SIGNAL LTD.

ISaA08-5 14:30-14:45Data Acquisition and Transmission System for Tramcar Powered by Hy-drogen Cell

Li, Hui CRRC Tangshan Co.,Ltd.Sun, Jinghui CRRC Tangshan Co.,Ltd.Dong, Jingchao CRRC Tangshan Co.,Ltd.Lu, Yiming CRRC Tangshan Co.,Ltd.Du, Fei CRRC Tangshan Co.,Ltd.

ISaA08-6 14:45-15:00Prospect and Review on Deepen Diagnosis and Maintenance of Elec-trified Module for Rail Vehicles

Song, Liqun CRRC Tangshan co., LTDSun, Jian CRRC Tangshan co., LTD

ISaA08-7 15:00–15:15Train-level Fault Diagnosis based-on Feature Selection

Li, Zheng CRRC Tangshan Co.,Ltd

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Sun, Jinghui CRRC Tangshan Co.,LtdDong, Jingchao CRRC Tangshan Co.,Ltdli, Hui CRRC Tangshan Co.,LtdDu, Fei CRRC Tangshan Co.,Ltd

ISaA08-8 15:15–15:30Performance Monitoring and Analysis of Down-Link Signal in Balise-based Train Positioning Systems

Li, Zhengjiao Beijing Jiaotong Univ.Cai, Bagen Beijing Jiaotong Univ.Liu, Jiang Beijing Jiaotong Univ.Wei, Shangguan Beijing Jiaotong Univ.Lu, Debiao Beijing Jiaotong Univ.Zhu, Linfu Acade. of Railway Science

SaA09 13:30-15:30 #9 Bld Room 202SaA09Fault-tolerant control methodsChair: Zhang, Youmin Concordia Univ.Chair: Zhang, Dengfeng Nanjing Univ. of Sci. and Tech.

ISaA09-1 13:30-13:50Active Fault-Tolerant Control of A Class of Multi-Agent Systems Basedon Sliding Mode Technology

Ma, Renyue Nanjing Univ. of Aero. and Astr.Zhang, Ke Nanjing Univ. of Aero. and Astr.Jiang, Bin Nanjing Univ. of Aero. and Astr.Yan, Xing-gang Univ. of Kent

ISaA09-2 13:50-14:10Fault-tolerant Speed Synchronous Control of Multi-motor System a-gainst Inverter Faults

Zhang, Dengfeng Nanjing Univ. of Sci. and Tech.Zhuang, Hao Nanjing Univ. of Sci. and Tech.Lu, Baochun Nanjing Univ. of Sci. and Tech.Bo, Cuimei Nanjing Tech. Univ.Wang, Zhiquan Nanjing Univ. of Sci. and Tech.

ISaA09-3 14:10-14:30Adaptive Backstepping Fault Tolerant Controller Design for UAV withMultiple Actuator Faults

Qian, Moshu Nanjing Tech. UniversityZhai, Lixiang Nanjing Univ. of Aero. and Astr.Zhong, Guanghua Nanjing Tech. UniversityGao, Zhifeng Nanjing Univ. of Posts and Telecommunications

ISaA09-4 14:30-14:50Adaptive Fault-tolerant Controller for Hypersonic Flight Vehicle with S-tate Constraints Using Integral Barrier Lyapunov Function

Peng, Zhiyu Nanjing Univ. of Aero. and Astr.Qi, Ruiyun Nanjing Univ. of Aero. and Astr.

ISaA09-5 14:50-15:10Active Fault-Tolerant Tracking Control of an Unmanned Quadrotor Heli-copter under Sensor Faults

Zhong, Yujiang Northwestern Ploytech. Univ.Zhang, Wei Northwestern Ploytech. Univ.Zhang, Youmin Concordia Univ.Zhang, Lidong Fudan Univ.

ISaA09-6 15:10–15:302D constrained iterative learning predictive fault-tolerant control forbatch processes with state delay

Song, Jiang Hainan Normal Univ.Luo, Weiping Hainan Normal Univ.Zhang, Qiyuan Liaoning Shihua Univ.Wang, Limin Hainan Normal Univ.

SaB03 15:50–17:30 #9 Bld Room 208SaB03Model based diagnosis-2Chair: Hou, Yandong Henan Univ.Chair: Yao, Lina Zhengzhou Univ.

ISaB03-1 15:50–16:10A rapid diagnosis method of small faults based on adaptive synovialobserver

Liu, Chang Henan Univ.Huang, Ruirui Henan Univ.Hou, Yandong Henan Univ.Cheng, Qianshuai Henan Univ.

ISaB03-2 16:10–16:30

Sensor fault diagnosis and fault tolerant control for stochastic distribu-tion time-delayed control systems

Wang, Hao Zhengzhou Univ.Yao, Lina Zhengzhou Univ.

ISaB03-3 16:30–16:50Sensor Fault Estimation via Iterative Learning Scheme for LinearRepetitive System

Feng, Li Chongqing Jiaotong Univ.Deng, Meng Chongqing Jiaotong Univ.Xu, Shuiqing Hefei Univ. of Tech.Zhang, Ke Chongqing Univ.

ISaB03-4 16:50–17:10Interval Observer-based Fault Detection for UAVs Formation with Actu-ator Faults

Yin, Lei Nanjing Univ. of Aero. and Astr.Liu, Jianwei Nanjing Univ. of Aero. and Astr.Yang, Pu Nanjing Univ. of Aero. and Astr.

ISaB03-5 17:10–17:30Fault Estimation Observer Design for a Class of Nonlinear Multi-agentSystems in Finite Frequency Domain

Fan, Qian Hubei Indu. Cons. Group Co.,LtdChen, Xiaohui Wuhan Univ. of Sci. and Tech.Chen, Jianliang Wuhan Univ. of Sci. and Tech.

SaB04 15:50–17:30 #9 Bld Room 207SaB04DDD methods-2Chair: Wen, Chenglin Hangzhou Dianzi Univ.Chair: Wang, Tianzhen Shanghai Maritime Univ.

ISaB04-1 15:50–16:10An Imbalance Fault Detection Method for Marine Current Turbine UsingVoltage Signal

Li, Zhichao Shanghai Maritime Univ.Wang, Tianzhen Shanghai Maritime Univ.Zhang, Milu Shanghai Maritime Univ.Wang, Yide Nantes Univ.Diallo, Demba Univ. of Paris-Sud

ISaB04-2 16:10–16:30Deep Learning Fault Diagnosis Method Based on Feature GenerativeAdversarial Networks for Unbalanced Data

Zhou, Funa Henan Univ.Yang, Shuai Henan Univ.Chen, Danmin Henan Univ.Wen, Chenglin Hangzhou Dianzi Univ.

ISaB04-3 16:30–16:50Moving window abnormal-condition monitoring strategy based on su-pervised sample selection and its industrial application

He, Kaixun Shandong Univ. of Sci. and Tech.Su, Zhaoyang Shandong Univ. of Sci. and Tech.

ISaB04-4 16:50–17:10Composite Fault Diagnosis of Rotor Broken Bar and Air Gap Eccentric-ity Based on Park Vector Module and Decision Tree Algorithm

Mao, jiaqi Nanjing Univ. of Aeronautics and AstronauticsChen, Fuyang Nanjing Univ. of Aeronautics and AstronauticsJiang, Bin Nanjing Univ. of Aeronautics and AstronauticsWang, Li Nanjing Univ. of Aeronautics and Astronautics

ISaB04-5 17:10–17:30A Decision Tree Based Method for Treatment Therapy of HCC

Zhao, Yijie Tsinghua Univ.Ye, Hao Tsinghua Univ.Zhong, Jing WiseHealthcare TECLOGY (SHANGHAI) Co. LtdWang, Haiwei WiseHealthcare TECLOGY (SHANGHAI) Co. Ltd

SaB05 15:50–17:30 #9 Bld Room 205SaB05Invited Session: Fault diagnosis and health managementChair: Huang, Darong Chongqing Jiaotong Univ.Co-Chair: Zhao, Ling Chongqing Jiao-tong Univ.Co-Chair: Li, Wei Lanzhou Univ. of Technology

ISaB05-1 15:50-16:10Power load prediction method based on VMD and dynamic adjustmentBP cooperation

Kuang, Fengtian Chongqing Jiaotong Univ.Huang, Darong Chongqing Jiaotong Univ.

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ISaB05-2 16:10-16:30An improved LSSVM fault diagnosis classification method based oncross genetic particle swarm

Zhang, Xu Chongqing Jiao-tong Univ.Huang, Darong Chongqing Jiao-tong Univ.Zhao, Ling Chongqing Jiao-tong Univ.Mi, Bo Chongqing Jiao-tong Univ.Liu, Yang Chongqing Jiao-tong Univ.

ISaB05-3 16:30-16:50Cryptanalysis on A (k,n)-threshold Multiplicative Secret SharingScheme

Long, Ping Chongqing Jiaotong Univ.Mi, Bo Chongqing Jiaotong Univ.Huang, Darong Chongqing Jiaotong Univ.Pan, Hongyang Chongqing Jiaotong Univ.

ISaB05-4 16:50-17:10Health Supervision based on Low Rank Analysis for Aerospace Track-ing

Liu, An State Key Laboratory of Astronautic DynamicsHu, Shaolin Xi’an Satellite Control CenterWang, Ming Xi’an Satellite Control CenterSong, Jianguo Xi’an Satellite Control Center

ISaB05-5 17:10-17:30Research on Co-Design of Security Control and Communication for Cy-ber Physical System under Cyber-Attacks

Shi, Yahong Lanzhou Univ. of TechnologyLi, Wei Lanzhou Univ. of Technology

SaB06 15:50–17:50 #9 Bld Room 301 SaB06Risk estimation of control systemsChair: Yang, Guanghong Northeastern Univ.Co-Chair: Liu, Yi Zhejiang Univ. of Tech.

ISaB06-1 15:50–16:10A Dynamic Risk Analysis Method for High-speed Railway CatenaryBased on Bayesian Network

Ma, Mengbai Shandong Univ. of Sci. and Tech.Dong, Wei Tsinghua Univ.Sun, Xinya Tsinghua Univ.Ji, Xingquan Shandong Univ. of Sci. and Tech.

ISaB06-2 16:10–16:30A Method of Dynamic Risk Analysis and Assessment for Metro PowerSupply System Based on Fuzzy Reasoning

Guo, Lili Shandong Univ. of Sci. and Tech.Dong, Wei Tsinghua Univ.Sun, Xinya Tsinghua Univ.Ji, Xingquan Shandong Univ. of Sci. and Tech.

ISaB06-3 16:30–16:50A Dynamic Risk Analysis Method for Compound Faults of Traction Sys-tem of High Speed Train Based on Characteristic Variables

Yue, Yangtengfei Shandong Univ. of Sci. and Tech.Dong, Wei Tsinghua Univ.Sun, Xinya Tsinghua Univ.Ji, Xingquan Shandong Univ. of Sci. and Tech.

ISaB06-4 16:50–17:10A Dynamic Risk Analysis Method for Escalator of Rail Transit HubBased on Characteristic Quantity

Ding, Shige Shandong Univ. of Sci. and Tech.Dong, Wei Tsinghua Univ.Sun, Xinya Tsinghua Univ.Wang, Haixia Shandong Univ. of Sci. and Tech.

ISaB06-5 17:10–17:30A Dynamic Risk Analysis and Assessment Method for Traction Systemof Metro Train Based on Characteristic Quantity

Zhao, Zhenning Shandong Univ. of Sci. and Tech.Dong, Wei Tsinghua Univ.Sun, Xinya Tsinghua Univ.Wu, Na Shandong Univ. of Sci. and Tech.

ISaB06-6 17:30–17:50Orthogonal Locality Preserving Projections Thermography for Subsur-face Defect

Liu, Kaixin Zhejiang Univ. of Tech.Tang, Yuwei Zhejiang Univ. of Tech.

Yao, Yuan National TsingHua Univ.Liu, Yi Zhejiang Univ. of Tech.Yang, Jianguo Zhejiang Univ. of Tech.

SaB07 15:50–17:30 #9Bld Room 308 SaB07Invited Session:Advanced Alarm Systems for Complex Industrial Facil-itiesChair: Wang, Jiandong Shandong Univ. of Sci. and Tech.Co-Chair: Yang, Fan Tsinghua Univ.

ISaB07-1 15:50–16:10An Improved Intelligent Warning Method Based on MWSPCA and itsApplication in Drilling Process

Geng, Zhiqiang Beijing Univ. of Chemical Tech.Chen, Ning Beijing Univ. of Chemical Tech.Wang, Zhongkai Beijing Univ. of Chemical Tech.Han, Yongming Beijing Univ. of Chemical Tech.

ISaB07-2 16:10–16:30Energy Efficiency Recognition and Diagnosis of Complex IndustrialProcesses using Multivariate Nonlinear Regression Method

Geng, Zhiqiang Beijing Univ. of Chemical Tech.Cheng, Minjie Beijing Univ. of Chemical Tech.Han, Yongming Beijing Univ. of Chemical Tech.Wei, Qin Beijing Univ. of Chemical Tech.Ouyang, Zhi Beijing Univ. of Chemical Tech.

ISaB07-3 16:30–16:50On Industrial Alarm Deadbands for Univariate Analog Signals

Wang, Zhen Shandong Univ. of Sci. and Tech.Wang, Jiandong Shandong Univ. of Sci. and Tech.Bai, Xingzhen Shandong Univ. of Sci. and Tech.Yang, Zijiang Shandong Univ. of Sci. and Tech.

ISaB07-4 16:50–17:10A data driven method to detect first-out alarms based on alarm occur-rence events

Hu, Wenkai China Univ. of GeosciencesChen, Tongwen Univ. of Alberta

ISaB07-5 17:10-17:30Optimal Test Sequencing Method with Unreliable Tests based on Quasi-depth First Search Algorithm

Liao, Xiaoyan Nanjing Univ. of Aeronautics and AstronauticsLi, Yang Nanjing Univ. of Aeronautics and AstronauticsLu, Ningyun Nanjing Univ. of Aeronautics and AstronauticsJiang, Bin Nanjing Univ. of Aeronautics and Astronautics

SaB08 15:50–17:10 #9 Bld Room新闻厅 SaB08Invited Session: Fault Diagnosis Methods for High Speed Railway Sig-nal SystemsChair: Zhang, Jilie Southwest Jiaotong Univ.Chair: Wang, Zicheng China Railway Eryuan Engineering Group CO.

LTDISaB08-1 15:50-16:10

Fault Diagnosis of Track Circuit Compensation Capacitor Based on G-WO Algorithm

Wang, Zicheng China Railway Eryuan Engineering Group CO. LTDYi, Lifu China Railway Eryuan Engineering Group CO. LTDYu, Kai China Railway Eryuan Engineering Group CO. LTDGu, Guoxiang Southwest Jiaotong Univ.Wang, Jianqiang China Railway Eryuan Engineering Group CO.

LTDISaB08-2 16:10-16:30

Design of the Safety Control Logic for Railway Stations Based on PetriNets

Li, Yike Southwest Jiaotong Univ.Tong, Yin Southwest Jiaotong Univ.Guo, Jin Southwest Jiaotong Univ.

ISaB08-3 16:30-16:50Parameter Estimation and Fault Diagnosis for Compensation Capacita-tors in ZPW-2000 Jointless Track Circuit

Yang, Wu-dong Southwest Jiaotong Univ.Zhang, Ji-lie Southwest Jiaotong Univ.Gu, Guoxiang Louisiana State Univ.

ISaB08-4 16:50-17:10Research on Prediction of Time Between Failures for Onboard Subsys-tem of Train Control System

19


Yuan, Yahui Beijing Jiaotong Univ.Cai, Baigen Beijing Jiaotong Univ.Wei, Shangguan Beijing Jiaotong Univ.Shi, Xiyao Beijing Jiaotong Univ.

SaB09 15:50–17:30 #9 Bld Room 202SaB09Invited Session: Interval Estimation for Fault DiagnosisChair: Zhu, Fanglai Tongji Univ.Co-Chair: Guo, Shenghui Suzhou Univ. of Scie. and Techn.Co-Chair: Wang, Zhenhua Harbin Institute of Tech.

ISaB09-1 15:50–16:10Sensor fault detection for linear systems by multiple 𝐻2/𝐻∞ observers

Zhang, Wenhan Harbin Institute of Tech.Wang, Zhenhua Harbin Institute of Tech.Shen, Yi Harbin Institute of Tech.

ISaB09-2 16:10–16:30Interval observer design for nonlinear switched systems

Che, Haochi Soochow Univ.Huang, Jun Soochow Univ.Ma, Xiang Soochow Univ.

ISaB09-3 16:30–16:50Actuator Fault Detection for Uncertain Systems Based on the IntervalOutput Observer*

Zhang, Xiangming Tongji Univ.Zhu, Fanglai Tongji Univ.Shan, Yu Tongji Univ.Guo, Shenghui Suzhou Univ. of Scie. and Techn.

ISaB09-4 16:50–17:10Interval State and Fault Estimation Based on Unknown Input Observerand Interval Hull Computation

Zhou, Meng Beijing Univ. of Chemical Tech.Cao, Zhengcai Beijing Univ. of Chemical Tech.Wang, Jing Beijing Univ. of Chemical Tech.Wang, Chang Beijing Aerospace Automation Research Institute

ISaB09-5 17:10–17:30On functional interval observers for discrete-time linear systems

Guo, Shenghui Suzhou Univ. of Sci. and Tech.Jiang, Bin Nanjing Univ. of Aeron. and Astron.

Poster Session PSaAPSaAJuly 6, 13:40–15:40

Poster Area (9号楼3层外厅)Chair: Lei, Yaguo Xi’an Jiaotong UniversityCo-Chair: Peng, Tao Central South Univ.Co-Chair: Ge, Zhiqiang Zhejiang Univ.

◁ PSaA-01Fault Diagnosis Method Based on GA-IBP Neural Network

Li, Bo Air Force Engineering Univ.Zhang, Lin Air Force Engineering Univ.Zhang, Bo Air Force Engineering Univ.Wang, WenFeng Air Force Engineering Univ.Hao, Zhewei Air Force Engineering Univ.

◁ PSaA-02Penicillin Fermentation Process Fault Detection Based on Multi-rateSampling kNN

Li, Keqin Northeastern Univ.Feng, Jian Northeastern Univ.

◁ PSaA-03Dynamic Laplacian eigenmaps for process monitoring

Zhang, Jingxin Tsinghua Univ.Chen, Maoyin Tsinghua Univ.Zhou, Donghua Shandong Univ. of Scie. and Techn.

◁ PSaA-04Process fault detection based on skew Gaussian distribution transfor-mation and canonical variable analysis method

Guo, Xiaoping Shenyang Univ. of Chemical Techn.Gao, Jiajun Shenyang Univ. of Chemical Techn.Li, Yuan Shenyang Univ.of Chemical Techn.

◁ PSaA-05Fault Detection Based on Modified t-SNE

Liu, Decheng Tsinghua Univ.Guo, Tianxu Tsinghua Univ.Chen, Maoyin Tsinghua Univ.

◁ PSaA-06Detecting Oscillations via Adaptive Chirp Mode Decomposition

Chen, Qiming Zhejiang Univ.Lang, Xun Zhejiang Univ.Xie, Lei Zhejiang Univ.Su, Hongye Zhejiang Univ.

◁ PSaA-07Online open-circuit fault detection and location utilizing estimated in-stantaneous amplitudes

Tao, Songbing Chongqing Univ.Chai, Yi Chongqing Univ.Liu, Bowen Chongqing Univ.Jiang, Congmei Chongqing Univ.Xu, Shuiqing Hefei Univ.of Tech.

◁ PSaA-08A process fault diagnosis method using multi-time-scale dynamic fea-ture extraction based on convolutional neural network

Gao, Xinrui Tsinghua Univ.Yang, Fan Tsinghua Univ.Feng, Enbo China National Chemical Corporation Ltd.

◁ PSaA-09Dynamic Processes Modeling and Monitoring based on a Novel Dy-namic Latent Variable Model

Zhou, Le Zhejiang Univ. of Scie. and Techn.Ge, Zhiqiang Zhejiang Univ.Song, Zhihuan Zhejiang Univ.Qin, Sizhao Univ. of Southern California

◁ PSaA-10A Novel Recursive Data-Driven Realization of SIR in Closed-loop Sys-tem

Liu, Tianyu Harbin Institute of Techn.Luo, Hao Harbin Institute of Techn.Wang, Xuejiao Harbin Institute of Techn.Yin, Shen Harbin Institute of Techn.Okyay Kaynak Harbin Institute of Technology

◁ PSaA-11Fault Detection of Actuators via Extended State Observer

Hou, Yanze China Acad.of Space Techn.Zhang, Minjie China Acad. of Space Techn.Yang, Lei China Acad.of Space Techn.

◁ PSaA-12Dust Deposition Diagnosis of Photovoltaic Modules Using Similarity-Based Modeling (SBM) Approach

Wang, Zhonghao Zhejiang Univ.Xu, Zhengguo Zhejiang Univ.

◁ PSaA-13Remaining useful life prediction under multiple fault patterns for degra-dation processes with long-range dependence

Zhang, Hanwen Zhejiang Univ.Yang, Chunjie Zhejiang Univ.Sun, Youxian Zhejiang Univ.

◁ PSaA-14A Novel Redundant Information Elimination Aided Classification Ap-proach for Cervical Cancer Diagnosis

Peng, Hu Harbin Institute of Techn.Jiang, Yuchen Harbin Institute of Techn.Li, Xiang Harbin Institute of Techn.Luo, Hao Harbin Institute of Techn.Yin, Shen Harbin Institute of Techn.

◁ PSaA-15Fault Prediction Method of the On-board Equipment of Train ControlSystem Based on Grey-ENN

Meng, Yueyue Beijing Jiaotong Univ.Shangguan Wei Beijing Jiaotong Univ.Cai, Bai-gen Beijing Jiaotong Univ.Zhang, Junzheng Beijing Jiaotong Univ.

◁ PSaA-16Fault Diagnosis of ROV Propeller Based on VMD and AR

Ren, Feng Shanghai Institute of Techn.Ye, Yinzhong Shanghai Urban Construction Vocational CollgeMa, Xiang-hua Shanghai Institute Of Techn.

20


◁ PSaA-17Design of Guidance and Control System for Hypersonic Morphing Ve-hicle in Dive Phase

Bao, Cunyu National Univ. of Defense Techn.Wang, Peng National Univ. of Defense Techn.Tang, Guojian National Univ. of Defense Techn.

◁ PSaA-18Sensor Fault Detection and Isolation in Toolface Control of Rotary S-teerable Drilling System

Niu, Yichun China Univ. of PetroleumSheng, Li China Univ. of PetroleumWang, Weiliang China Univ.of PetroleumGeng, Yanfeng China Univ. of PetroleumZhou, Donghua Shandong Univ. of Scien. and Techn.

Poster Session PSaB PSaBJuly 6, 13:40–15:40

Poster Area (9号楼2层外厅)Chair: Zeng, Jianping Xiamen Univ.Co-Chair: Gao, Ming China Univ. of Petroleum (Huadong)Co-Chair: Luo, Delin Xiamen Univ.

◁ PSaB-01Remaining Useful Life Prediction for a Fractional Degradation Processwith Non-stationary Increments

Xi, Xiaopeng Tsinghua Univ.Chen, Maoyin Tsinghua Univ.Zhou, Donghua Shandong Univ. of Science and Technology

◁ PSaB-02Detection of Incipient Leakage Fault in EMU Braking System

Sang, Jianxue Tsinghua Univ.Zhang, Junfeng Tsinghua Univ.Guo, Tianxu Tsinghua Univ.Tai, Xiuhua CRRC Qingdao Sifang Rolling Stock Research

Institute Co.Chen, Maoyin Tsinghua Univ.Zhou, Donghua Shandong Univ. of Science and Technology

◁ PSaB-03An Improved Random Forest Algorithm of Fault Diagnosis for RotatingMachinery

Wang, Zilan Shandong Univ. of Sci. and Tech.Zhong, Maiying Shandong Univ. of Sci. and Tech.Liu, Yang Shandong Univ. of Sci. and Tech.

◁ PSaB-04Transient Fault Diagnosis of Track Circuit Based on MFCC-DTW

Yang, Jing Southwest Jiaotong Univ.Wang, Xiaomin Southwest Jiaotong Univ.Zhang, Wenfeng Southwest Jiaotong Univ.Zheng, Qiming Southwest Jiaotong Univ.Song, Ci Southwest Jiaotong Univ.

◁ PSaB-05Fault diagnosis for the planetary gearbox based on a hybrid dimensionreduction algorithm

Li, Ran Shandong Univ. of Science and TechnologyLiu, Yang Shandong Univ. of Science and Technology

◁ PSaB-06Mixture Probabilistic Linear Discriminant Analyzer for Process FaultClassification

Yi, Liu Zhejiang Univ.Zeng, Jiusun China Jiliang Univ.Xie, Lei Zhejiang Univ.Lang, Xun Zhejiang Univ..Luo, Shihua Jiangxi Univ. of Finance and EconomicsSu, Hongye Zhejiang Univ.

◁ PSaB-07Intermediate Observer-based Fault Estimation for Nonlinear Systemwith Input Disturbances

Wang, Yuan Northeastern Univ.Wang, Zhanshan Northeastern Univ.

◁ PSaB-08Learning observer based fault diagnosis and fault tolerant control formanipulators with sensor fault

Wu, Wei Zhengzhou Univ.

Yao, Lina Zhengzhou Univ.Kang, Yunfeng Zhengzhou Univ.

◁ PSaB-09Intelligent Fault Diagnosis Method for Coupling Rotating MachineryBased on Deep Convolutional Neural Network

Mu, Dawei China Univ. of Petroleum (East China)Sheng, Li China Univ. of Petroleum (East China)

◁ PSaB-10Fault Isolation Via Multiple-model Estimation for Traction Inverter withIGBT Open Circuit Fault

Yan, Yu Nanjing Univ. of Aeronautics and AstronauticsJiang, Bin Nanjing Univ. of Aeronautics and AstronauticsMao, Zehui Nanjing Univ. of Aeronautics and AstronauticsZhu, Hongyu Nanjing Engineering Institute of Aircraft SystemsLI, Han CSSC Systems Engineering Research Institute,

Oceanic Intelligent Technology Innovation Center

◁ PSaB-11Fault Diagnosis for Stator Inter-turn Short Circuit Fault of Traction Mo-tors under Closed-loop Structure

Wu, Xiao Nanjing Univ. of Aeronautics and AstronauticsZhang, Yufeng CSSC Systems Engineering Research Institute,

Oceanic Intelligent Technology Innovation CenterMao, Zehui Nanjing Univ. of Aeronautics and AstronauticsJiang, Bin Nanjing Univ. of Aeronautics and AstronauticsShi, Yongqian Nanjing Univ. of Aeronautics and Astronautics

◁ PSaB-12Fault Diagnosis of Aero-engine Gas Path Base on PSO-SVM

Chi, Jin Xiamen Univ.Liu, Yuanfang Xiamen Univ.Luo, Delin Xiamen Univ.Cao, Langcai Xiamen Univ.

◁ PSaB-13Research on the safety assessment of heavy trucks in transit based onthe Information entropy-based weighting and bi-level Belief Rule Base

Li, Gailing Army Military Transportation Univ.Zhou, Zhijie Rocket Force univ. of EngineeringHu, Changhua Rocket Force univ. of EngineeringHu, Guanyu Hainan Normal Univ.He, Wei Harbin Normal Unive.

◁ PSaB-14A comparison of OCMPM and OCSVM in motor and sensor fault detec-tion for traction control system

Chen, Zhiwen Central South Univ.Chen, Zhuo Central South Univ.Peng, Tao Central South Univ.Liang, Ketian Central South Univ.Yang, Chunhua Beijing Univ. of Sci. and Tech.

◁ PSaB-15Comparison of Several Data-driven Models for Remaining Useful LifePrediction

Chen, Zhiwen Central South Univ.Liang, Ketian Central South Univ.Yang, Chao Central South Univ.Peng, Tao Central South Univ.Chen, Zhuo Central South Univ.Yang, Chunhua Central South Univ.

◁ PSaB-16Optimized Neural Network by Genetic Algorithm and Its Application inFault Diagnosis of Three-level Inverter

Chen, Danjiang Zhejiang Wanli Univ.Liu, Yutian Zhejiang Wanli Univ.Zhou, Junwei State Grid Hangzhou Xiaoshan Power Supply

Company

◁ PSaB-17A Non-Greedy Algorithm Based L1-Norm LDA Method for Fault Detec-tion

Wang, Min Tsinghua Univ.Chen, Maoyin Tsinghua Univ.Zhang, Jingxin Tsinghua Univ.Zhou, Donghua Tsinghua Univ.

◁ PSaB-18

21


Fault Tolerant Control for High-Order Multi-agent Systems with Switch-ing Interaction Topologies

Wang, Qing Zhejiang Wanli Univ.Liu, Yu’ang Zhejiang Wanli Univ.Zhong, Kewei State Grid Hangzhou Xiaoshan Power Supply

CompanyDong, Chaoyang Beihang UniversityHou, Yanze CAST

◁ PSaB-19Research on Fault Diagnosis of Three Degrees of Freedom GyroscopeRedundant System

Shi, Haoqiang Xi’an Univ. of Tech.Hu, Shaolin Xi’an Univ. of Tech.Zhang, Jiaxu Xi’an Univ. of Tech.

◁ PSaB-20Fault Diagnosis and Tolerant Control for Sensors of PWM Rectifier Un-der High Switching Frequency

Gong, Zifeng Southwest Jiaotong Univ.Huang, Deqing Southwest Jiaotong Univ.Qin, Na Southwest Jiaotong Univ.Ma, Lei Southwest Jiaotong Univ.

◁ PSaB-21Understanding the Fault in EMU Braking System

Tai, Xiuhua CRRC Qingdao Sifang Rolling Stock ResearchInstitute Co.

Guo, Tianxu Tsinghua Univ.Chen, Maoyin Tsinghua Univ.Zhang, Junfeng Tsinghua Univ.Zhou Donghua Tsinghua Univ.

◁ PSaB-22Fault Detection and Estimation of Multi-Agent Systems: Neighborhood-Observer-Based Approach

Gong, Jianye Yangzhou Univ.Fu, Qilong Yangzhou Univ.Zheng, Xiaoxiao Yangzhou Univ.Sun, Xun Yangzhou Univ.Jiang, Bin Nanjing Univ. of Aero. and Astr.Shen, Qikun Yangzhou Univ.

◁ PSaB-23Batch Process Fault Diagnosis Based on The Combination of Deep Be-lief Network and Long Short-Term Memory Network

Liu, Fan Hangzhou Dian Zi Univ.Wang, Peiliang Huzhou Univ.Cai, Zhiduan Huzhou Univ.Zhou, Zhe Huzhou Univ.Wang, Yanfeng Huzhou Univ.Yang, Zeyu Zhejiang Univ.

◁ PSaB-24Safe Reconfigurability of a Class of Nonlinear Interconnected Systems

An, Zixi Nanjing Univ. of Aeronautics and AstronauticsYang, Hao Nanjing Univ. of Aeronautics and AstronauticsJiang, Bin Nanjing Univ. of Aeronautics and Astronautics

◁ PSaB-25Thruster Fault Tolerant Control Scheme for 4500-m Human OccupiedVehicle

Huang, Ming Shanghai Maritime Univ.Zhu Daqi Shanghai Maritime Univ.Chu, Zhenzhong Shanghai Maritime Univ.

◁ PSaB-26Design of Test Case for ATP Speed Monitoring Function Based onCause-effect Graph

Dou, Lei Southwest Jiaotong Univ.Yang, Wudong Southwest Jiaotong Univ.

◁ PSaB-27Simple adaptive control with anti-windup compensator for aircraft atti-tude control

Gai, WenDong Shandong Univ. of Sci. and Tech.Zhou Yecheng Shandong Univ. of Sci. and Tech.Sun, Chengxian Shandong Univ. of Sci. and Tech.Zhang, Jing Shandong Univ. of Sci. and Tech.

◁ PSaB-28

Multiphase and Multimode Monitoring of Batch Processes Based onDensity Peak Clustering and Just-in-time Learning

Fan, Saite Zhejiang Univ.Shen, Feifan Zhejiang Univ.Song, Zhihuan Zhejiang Univ.

◁ PSaB-29Data-driven RUL Prediction of High-speed Railway Traction SystemBased on Similarity of Degradation Feature

Zhu, Kaiqiang Nanjing Univ. of Aeron. and Astron.Zhang,Chuanyu Nanjing Univ. of Aeron. and Astron.Lu,Ningyun Nanjing Univ. of Aeron. and Astron.Jiang,Bin Nanjing Univ. of Aeron. and Astron.

◁ PSaB-30Wind Turbine Periodic Intermittent Fault Detection Based on FractionalOrder Chaotic System

Gao, Bingpeng Xinjiang Univ.Wang, Weiqing Xinjiang Univ.Ji, Xinru Xinjiang Univ.

◁ PSaB-31A Kernel Canonical Correlation Analysis-Based Fault Detection Methodwith Application to a Hot Tandem Rolling Mill Process

Qi, Tianjing Univ. of Scie. and Techn. BeijingZhang, Kai Univ. of Scie. and Techn. BeijingPeng, Kaixiang Univ. of Scie. and Techn. BeijingZhao, Shanshan Univ. of Scie. and Techn. Beijing

◁ PSaB-32A Fault Detection and Identification Method Based on Mixed Logic Dy-namic Model for Three-phase Inverter Using Single Current Sensor

Zhong, Ningfan Shandong Univ. of Scie. and Techn.Zhai, Yanqiang Shandong Univ. of Scie. and Techn.Zhang, Zhenhai Shandong Univ. of Scie. and Techn.

◁ PSaB-33Robust Control of Single Phase PWM Rectifier with Parametric Uncer-tainties

Motaz Musa Ibrahim Southwest Jiaotong Univ.Ma, Lei Southwest Jiaotong Univ.Qin, Na Southwest Jiaotong Univ.

◁ PSaB-34Research on co-design between security control and communication ofa class of nonlinear CPS under cyber attack

Zhao, li Lanzhou Univ. of Tech.Li, Wei Lanzhou Univ. of Tech.Li, Yajie Lanzhou Univ. of Tech.Shi, Yahong Lanzhou Univ. of Tech.

◁ PSaB-35A Real-Time Anomaly Detection Approach Based on Sparse Distribut-ed Representation

Wang,Weikai Donghua Univ.Zhao, Chenwei Donghua Univ.Hao, Kuangrong Donghua Univ.Tang, Xuesong Donghua Univ.Wang, Tong Donghua Univ.

◁ PSaB-36HAZOP quantitative analysis of the balise based on the improvedCUOWGA–Sharpley value

Chu, Xintong Southwest Jiaotong Univ.Tong, Yin Southwest Jiaotong Univ.Guo, Jin Southwest Jiaotong Univ.

◁ PSaB-37Thermodynamic Mechanism and Data Hybrid Driven Model Based Ma-rine Diesel Engine Turbocharger Anomaly Detection with PerformanceAnalysis

He, Xiao CSSCWang, Jia Beijing Univ. of Chemical Techn.Wei, Muheng CSSCQiu, Bohua CSSCYang, Ying Peking Univ.

◁ PSaB-38Fault-tolerant Control for Networked Control Systems with Partly Un-known Transition Probabilities

Wang, Yanfeng Harbin Engineering Univ.

22


Wang, Peiliang Harbin Engineering Univ.Li, Zuxin Harbin Engineering Univ.Ttalbi, Mohamed Harbin Engineering Univ.Wang, Yuling Harbin Engineering Univ.

◁ PSaB-39Research on fault classification method based on deep belief network

Wei, Yuqin Shanghai Jiao Tong Univ.Weng, Zhengxin Shanghai Jiao Tong Univ.

◁ PSaB-40Deep Convolution Neural Networks for the Classification of Robot Exe-cution Failures

Liu, Ying Naval Univ. of EngineeringWang, Xiuqing Naval Univ. of EngineeringRen, Xuemei Naval Univ. of EngineeringFeng Lyu Naval Univ. of Engineering

◁ PSaB-41Random-Sampling-Based Performance Evaluation Method of Fault De-tection and Diagnosis for Railway Traction System

Fang, Dikai Central South Univ.Peng, Tao Central South Univ.Yang, Chao Central South Univ.Chen, Zhiwen Central South Univ.Tao, Hongwei Central South Univ.

◁ PSaB-42Active Fault Tolerant Control for HVAC System Based on GIMC andFeedforward Compensation

Guo, Weijie Nantong Univ.Qiu, Aibing Nantong Univ.Li, Xue Nantong Univ.

◁ PSaB-43Data-driven based ToMFIR Design with Application to Incipient FaultDetection in High-speed Rail Vehicle Suspension System

Wu, Yunkai Jiangsu Univ. of Sci. & Tech.Jiang,Bin Nanjing Univ. of Aeron. & Astron.Zhu, Zhiyu Jiangsu Univ. of Sci. & Tech.Zeng,Qingjun Jiangsu Univ. of Sci. & Tech.

◁ PSaB-44CNN Based Process Monitoring of Spatially Distributed System

Ma,Fangyuan Beijing Univ.of Chem. Tech.Lin, Dexi Sinochem Quanzhou Petrochemical Co., Ltd,Zhong, Jingtao Sinochem Quanzhou Petrochemical Co., Ltd,Han, Xianyao Beijing Univ.of Chem. Tech.Wang, Jingde Beijing Univ.of Chem. Tech.Sun, Wei Beijing Univ.of Chem. Tech.

◁ PSaB-45Adaptive fuzzy control for a flexible air-breathing hypersonic vehiclebased on tracking differentiator

Feng, Cong Beihang Univ.Wang, Qing Beihang Univ.Zhang, Shen China Academy of Space Tech.

◁ PSaB-46Transistor Temperature Balancing Method for Three-level InvertersBased on FCS-MPC

Peng, Tao Central South Univ.Xie, Feiran Central South Univ.Yang, Chao Central South Univ.Yang, Chunhua Central South Univ.

◁ PSaB-47Finite Time Convergence Incremental Nonlinear Dynamic InversionBased Attitude Control for Flying-Wing Aircraft with Actuator Fault

Han, Wuhan Nanjing Univ. of Aeronautics and AstronauticsZhang, Shaojie Nanjing Univ. of Aeronautics and Astronautics

◁ PSaB-48Incipient Fault Diagnosis of Sucker Rod Pumping System Using Kull-back Leibler Divergence Based Improved Kernel Principal ComponentAnalysis

Cai, Peipei China Univ. of PetroleumDeng, Xiaogang China Univ. of PetroleumCao, Yu-ping China Univ. of Petroleum

◁ PSaB-49Disturbance-observer-based Adaptive H∞Fault-tolerant Control for

High-speed TrainsLin, Xue Beijing Jiaotong Univ.Gao, Shigen Beijing Jiaotong Univ.Dong, Hairong Beijing Jiaotong Univ.

◁ PSaB-50A Descriptor System Approach for False Data Injection Attacks TowardPower System

Ding, Zhou Nantong Univ.Qiu, Aibing Nantong Univ.Li, Xue Nantong Univ.

◁ PSaB-51Multiblock ICA-PCA and Bayesian Inference based Distributed ProcessMonitoring

Wang, Hongyang Beijing Univ. of Chemical Tech.Chen, Xiaolu Beijing Univ. of Chemical Tech.Wang, Jing Beijing Univ. of Chemical Tech.Liu, Qiang Northeastern Univ.

◁ PSaB-52Observer-based Sliding Mode Fault Tolerant Control for Spacecraft At-titude System with Actuator Faults

Wang, Qing Beihang UniversityLiang, Xiaohui Beihang UniversityRan, Maopeng Nanyang Technological UniversityDong, Chaoyang Beihang University

◁ PSaB-53Fault detection and diagnosis based on a new ensemble kernel princi-pal component analysis

Li, Xintong Tianjin Univ.Rui, Felizardo Tianjin Univ.Xue, Feng Tianjin Univ.Qin, Lida Tianjin Univ.Kai, Song Tianjin Univ.

◁ PSaB-54Sensor Fault Estimation for Lipschitz Nonlinear System with Distur-bance

Han, Jian Northeastern Univ.Liu, Xiuhua Northeastern Univ.Wei, Xinjiang Northeastern Univ.Hu, Xin Northeastern Univ.Zhang, Huifeng Northeastern Univ.

◁ PSaB-55Subspace Alignment based Adapted GLRT Detector and Its Applicationin Marine Current Turbine

Zhang, Milu Shanghai Maritime Univ.Wang, Tianzhen Shanghai Maritime Univ.Tang, Tianhao Shanghai Maritime Univ.

◁ PSaB-56Design of a Fault Diagnosis System for the ”JiaoLong” Deep-seaManned Vehicles

Chen, Xu Tsinghua Univ.He, Xiao Tsinghua Univ.

◁ PSaB-57Stacking Model-based Method for Traction Motor Fault Diagnosis

Peng, Tao Central South Univ.Ye, Chenglei Central South Univ.Chen, Zhiwen Central South Univ.

◁ PSaB-58A Semi-supervised Constraints Propagation Based Method for Fault Di-agnosis

Liao, Guobo Chongqing Univ.Zhou, Han Chongqing Univ.Li, Yanxia Chongqing Univ.Yin, Hongpeng Chongqing Univ.Chai, Yi Chongqing Univ.

◁ PSaB-59Turnout Fault Diagnosis Algorithms of Full-Electronic Interlocking Sys-tem Based on BP Adaboost

Chen, Guangwu Lanzhou Jiaotong Univ.Yu, Yijian Lanzhou Jiaotong Univ.Xing, Dongfeng Lanzhou Jiaotong Univ.Yang, Juhua Lanzhou Jiaotong Univ.

23


◁ PSaB-60A Small Leakage Detection Approach for Gas Pipelines based on CNN

Li, Jie Chongqing Univ. of Sci. and Tech.Liu, Yao Chongqing Univ. of Sci. and Tech.Chai, Yi Chongqing Qingshan Industry Co.He, Hongli Chongqing Univ. of Sci. and Tech.Gao, Min Chongqing Univ. of Sci. and Tech.

◁ PSaB-61Actuator Fault Detection Filter Design for Continuous-time SwitchedSystems in Finite Frequency Domain

Zhu, Dewen Lanzhou Jiaotong Univ.Du, Dongsheng Lanzhou Jiaotong Univ.Chen, Haoshuang Lanzhou Jiaotong Univ.Wen, Runting Lanzhou Jiaotong Univ.

24

CAA SAFEPROCESS 2019 Author Index

Author Index

(O=Organizer, C=Chair, CC=Co-Chair)

A

An, Zixin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-24 22

B

Bai, Xingzhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-3 19

Bo, Cuimei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-2 18

C

Cai, Baigen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-8 18

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-4 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-15 20

Cai, Peipei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-48 23

Cai, Zhiduan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-23 22

Cao, Langcai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-12 21

Cao, Yuping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-2 16

Cao, Zhengcai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-4 20

Chai, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-58 23

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-07 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-60 24

Chang, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06-1 17

Che, Haochi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-2 20

Chen, Danjiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-16 21

Chen, Danmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04-2 18

Chen, Fuyang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-4 18

Chen, Guangwu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-59 23

Chen, Haoshuang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-61 24

Chen, Jianliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB03-5 18

Chen, Jiyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-4 17

Chen, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B1 15

Chen, Maoyin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-5 16

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-03 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-05 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-01 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-02 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-17 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA02-4 15

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA02-5 16

Chen, Ning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-1 19

Chen, Qiming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-06 20

Chen, Tongwen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-4 19

Chen, Xiaoguang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-1 17

Chen, Xiaohui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-5 18

Chen, Xiaolu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-51 23

Chen, Xu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-56 23

Chen, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-4 17

Chen, Zhiwen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-14 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-15 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-41 23

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-57 23

Chen, Zhuo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-14 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-15 21

Cheng, Chao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-3 17

Cheng, Minjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-2 19

Cheng, Qianshuai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-1 18

Chi, Jin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-12 21

Chu, Xintong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-36 22

Chu, Zhenzhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-25 22

Bao, Cunyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-17 21

D

Dai, Xi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-1 17

Demba Diallo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04-1 18

Deng, Meng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-3 17

Deng, Xiaogang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-2 16

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-48 23

Ding, Shige . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-4 19

Ding, Zhongjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-6 16

Ding, Zhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-50 23

Dong, Chaoyang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-18 22

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-52 23

Dong, Hairong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-49 23

Dong, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-1 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-2 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-3 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-4 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-5 19

Dong, Jingchao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-5 17

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-7 18

Dou, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-26 22

Du, Dongsheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-61 24

Du, Fei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-7 17

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-5 17

Du, Zhiyong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-3 16

F

Fan, Qian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-5 18

Fang, Dikai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-41 23

Fang, Huajing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06 17

Fang, Jingzhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA03-1 16

Fang, Yifan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-5 16

Felizardo Rui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-53 23

Feng, Cong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-45 23

Feng, Enbo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-08 20

Feng, Jian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-02 20

Feng, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-3 18

Fu, Caixin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-3 17

Fu, Qilong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-22 22

G

Gai, Wendong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A5 15

25


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-27 22

Gao, Bingpeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-30 22

Gao, Jiajun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-04 20

Gao, Kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-2 16

Gao, Min . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-60 24

Gao, Ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-5 CC

Gao, Shigen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-49 23

Gao, Siyang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-4 17

Gao, Xinrui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-08 20

Gao, Xuejin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-6 16

Gao, Zhifeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-3 17

Ge, Zhiqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-09 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-1 16

Geng, Yanfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-18 21

Geng, Zhiqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-1 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-2 19

Gong, Jianye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-22 22

Gong, Longhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA03-3 16

Gong, Zifeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-20 22

Gu, Guoxing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-3 19

Gui, Weihua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-7 16

Guo, Jianxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A2 15

Guo, jin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-2 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-36 22

Guo, Lili . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-2 19

Guo, Shenghui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09 CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-3 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-5 20

Guo, Tianxu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-05 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-02 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-21 22

Guo, Weijie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-42 23

Guo, Xiaoping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-04 20

H

HABIB ULLAH KHAN JADOON . . . . . . . . . . . . . . . . . . . . PSaB-20 22

Han, Jian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-54 23

Han, Wuhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-47 23

Han, Xianyao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-44 23

Han, Yongming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-1 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-2 19

Hao, Kuangrong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-3 16

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-35 22

Hao, Zhewei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-01 20

He, Hongli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-60 24

He, Kaixun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-3 18

He, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-13 21

He, Xiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA02 CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B5 15

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA03-6 16

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA05-6 17

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-56 23

He, Xiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-37 22

Hei, Xinhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-1 15

Hou, Yandong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB03-1 18

Hou, Yanze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-11 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-18 21

Hu, Changhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA02 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-13 21

Hu, Enhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-11 20

Hu, GuanYu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07 CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-6 17

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-13 21

Hu, HuanZhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-3 17

Hu, Kang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B3 15

Hu, Shaolin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01 CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA02-1 15

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-4 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-19 15

Hu, Wenkai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-4 19

Hu, Xin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-54 23

Hu, Yongtao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-3 16

Huang, Darong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-1 18

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-2 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-3 19

Huang, Deqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07 CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-1 17

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-20 22

Huang, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-2 15

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA05-6 17

Huang, Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-2 20

Huang, Ming　. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-25 22

Huang, Ruirui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-1 18

Huang, Tianpeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-1 17

Huang, Wanning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B3 15

J

Ji, Xingquan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-1 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-2 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-3 19

Ji, Xinru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-30 22

Jia, Chao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA05-3 16

Jiang, Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-5 16

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-1 18

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04-4 18

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-5 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-5 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-10 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-11 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-22 22

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-24 22

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-29 22

Jiang, Congmei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-07 20

Jiang, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-1 16

Jiao, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A6 15

Jiang, Yuchen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-14 20

Jiang, Yunpeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A6 15

Jiao, Ruihua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA05-2 16

Jiaqi, Mao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-4 18

Jie Huang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-2 15

Jin, Huiyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08 CC

K

Kang, Yunfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-08 21

Kuang, Fengtian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-1 18

L

Lang, Xun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-06 20

26


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-06 21

Le, Ningning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01 15

Lei, Yaguo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA C

Li Cui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06-2 17

Li, Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-1 17

Li, Binbin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-1 17

Li, Bo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-01 20

Li, Chunmei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-3 17

Li, Gailing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-13 21

LI, Han . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-10 21

Li, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-5 17

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-7 18

Li, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-60 24

Li, Keqin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-02 20

Li, Lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-8 16

Li, Qing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-2 17

Li, Ran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-05 21

LI, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05 CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-5 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-34 22

Li, Xiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-14 20

Li, Xin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-1 15

Li, Xintong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-53 23

Li, Xue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-42 23

Li, Yajie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-34 22

Li, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-5 19

Li, Yanxia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-58 23

Li, Yike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08-2 19

Li, Yuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-3 16

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-04 20

Li, Zheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-7 17

Li, Zhengjiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-8 18

Li, Zhibin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A4 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B3 21

Li, Zhichao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-1 18

Li, Zuxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-38 23

Liang, Ketian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-14 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-15 21

Liang, kuankuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B1 15

Liang, Tong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA03-6 16

Liang, Xiaohui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-52 23

Liao, Guobo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-58 23

Liao, Xiaoyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-5 19

Lin, Dexi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-44 23

Lin, Wenliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B2 15

Lin, Xue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-49 23

Ling, Dan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-5 17

Liu, An . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-4 19

Liu, Baohua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01-B4 15

Liu, Bowen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-07 20

Liu, Chang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B5 15

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA05-6 16

Liu, Chang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-1 18

Liu, Chengrui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A1 15

Liu, Decheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-05 20

Liu, Fan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-23 22

Liu, Hongmei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-5 16

Liu, Jiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-8 18

Liu, Jianwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-4 18

Liu, Jinguo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B1 15

Liu, Kaixin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-6 19

Liu, Qiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-51 23

Liu, Tianyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-10 20

Liu, Wenjing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01 CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A1 15

Liu, Xiaohui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B4 15

Liu, Xiaoxiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01-A2 15

Liu, Xiuhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-54 23

Liu, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-1 16

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-03 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-05 21

Liu, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-2 19

Liu, Yao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-60 24

Liu, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06 CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-6 19

Liu, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-40 23

Liu, Yuanfang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-12 21

Liu, Yu’ang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-18 22

Liu, Yubai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A2 15

Liu, Yuhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-4 17

Liu, Yutian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-16 21

Liu, Zhenxing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06 CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-3 17

Long, Ping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-3 19

Lu, Baochun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-2 18

Lu, Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-4 16

Lu, Debiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-8 18

Lu, Ningyun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-29 22

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-5 19

Lu, Yiming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-5 17

Luo, Delin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-12 21

Luo, Linkai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08 CC

Luo, Hao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-10 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-14 20

Luo, Shihua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-06 21

Luo, Weiping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-6 18

Lv, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-40 23

Lv, Shiyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-4 17

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-5 17

Lv, Xunhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA03-5 16

MMa, Fangyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-44 23

Ma, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-1 16

Ma, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-20 22

27


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-33 22

Ma, Liang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-2 16

Ma, Mengbai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-1 19

Ma, Ping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-5 16

Ma, Renyue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-1 18

Ma, Xiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-2 20

Ma, Xianghua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-16 20

Mao, Yuxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-1 17

Mao, Zehui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-5 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-10 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-11 21

Meng, Yueyue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-15 20

Mi, Bo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-2 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-3 19

Miao, JingGang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B2 15

Motaz Mahil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-33 22

Mou, Dawei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-09 21

N

Niu, Yichun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-18 21

O

Okyay Kaynak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-10 20

Ouyang, Gaoxiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B2 15

Ouyang, Zhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-2 19

PPang Hongyang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-3 19

Peng, Hu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-14 20

Peng, Kaixiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-2 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-31 22

Peng, Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA03-2 16

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-14 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-15 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-41 23

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-46 23

Peng, Xi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-1 15

Peng, Zhiyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-4 18

Pi, Yanting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA05-2 16

Q

Qi, Haibin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-6 16

Qi, Ruiyun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-4 18

Qi, Tianjing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-31 22

Qian, Jinchuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-1 16

Qian, Moshu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-3 18

Qiao, Xinyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-3 17

Qin, Huixian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A4 15

Qin, Lida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-53 23

Qin, Na . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-33 22

Qin, Na . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-20 22

Qin, Ruochen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-4 16

Qin, Shengjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B4 15

Qin, Sizhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-09 20

Qiu, Aibing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-42 23

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-50 23

Qiu, Bohua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-37 22

R

Ran, Maopeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-52 23

Ren, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-16 20

Ren, Lihong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-3 16

Ren, Xuemei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-40 23

Ren, Yanheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-4 16

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-4 16

SS. Joe Qin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-09 20

Saite Fan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-28 22

Sang, Jianxue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-02 21

Shan, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-3 20

Shang, GuanWei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-8 18

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-4 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-15 20

Shao, Yubo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-5 17

Shen, Feifan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-28 22

Shen, Qikun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-22 22

Shen, Shaoping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01-A4 15

Shen, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-1 20

Sheng, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-18 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-09 21

Shi, Guangxu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-5 17

Shi, Haoqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-19 22

Shi, Jiantao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-6 16

Shi, Xiyao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08-4 20

Shi, Yahong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-5 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-34 22

Shi, Yongqian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-11 21

Song, Ci . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-04 21

Song, Jiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-6 18

Song, JianGuo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-4 19

Song, Kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-53 23

Song, Liqun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-6 17

Song, Zhihuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-1 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-09 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-28 22

Su, Hongye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-06 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-06 21

Su, Zhaoyang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-3 18

Sun, Chengxian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-27 22

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A5 15

Sun, Jian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-6 17

Sun, Jinghui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-5 17

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-7 18

Sun, Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-6 16

Sun, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-44 23

Sun, Xinya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-1 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-2 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-3 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-4 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-5 19

Sun, Xun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-22 22

Sun, Youxian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-13 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA02-3 15

TTai, Xiuhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-02 21

28


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-21 22

Tan, Shulin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA-01 17

Tao, Hongwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-41 23

Tang, Guojian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-17 21

Tang, Miao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA-B4 15

Tang, Tianhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-55 23

Tang, Xuesong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-35 22

Tang, Yuwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-6 19

Tao, Bo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-5 17

Tao, Songbing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-07 20

Tian, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-3 15

Tian, Kefeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A4 15

Tong, Yin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-36 22

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-2 19

Ttalbi, Mohamed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-38 23

W

Wan, Yiming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-4 17

Wang, Baocheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B3 15

Wang, Chang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-4 20

Wang, Cong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-5 16

Wang, Fan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A4 15

Wang, Fei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06-6 17

Wang, Guozhu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-3 16

Wang, Haiwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-5 18

Wang, Haixia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-4 19

Wang, Hao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-2 18

Wang, Hongyang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-51 23

Wang, Jia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-37 22

Wang, Jiandong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-3 C

Wang, Jianqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08-1 19

Wang, Jidong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-4 17

Wang, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-4 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-51 23

Wang, Jingde . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-44 23

Wang, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-4 18

Wang, Limin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-6 18

Wang, Min . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-17 21

Wang, Ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-4 19

Wang, Peiliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-23 22

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-38 22

Wang, Peng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-17 21

Wang, Pu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-6 16

Wang, Qing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-18 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-45 23

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-52 23

Wang, Shuyi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A1 15

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A2 15

Wang, Tianzhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-1 C

Wang, Tong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-35 22

Wang, Weijun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-3 17

Wang, Weikai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-35 22

Wang, Weiliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-18 21

Wang, Weiqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-30 22

Wang, Wenfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-01 20

Wang, Xianghua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA03-4 16

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-4 16

Wang, Xiaomin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-04 21

Wang, Xiuqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-40 23

Wang, Xuejiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-10 20

Wang, Yalin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-7 16

Wang, Yanfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-23 22

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-38 22

Wang, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06-5 17

Wang, Yanwen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-4 15

Wang, Yide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04-1 18

Wang, Yihan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-5 16

Wang, Youqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-2 17

Wang, Yuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-07 21

Wang, Yuling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-38 23

Wang, Zhanshan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-07 21

Wang, Zhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-3 19

Wang, Zhenhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09 CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-1 20

Wang, Zhiquan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-2 18

Wang, Zhixin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-1 17

Wang, Zhonghao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-12 20

Wang, Zhongkai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-1 19

Wang, Zicheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08 CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-1 19

Wang, Zilan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-03 21

Wei, Muheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-37 22

Wei, Qin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-2 19

Wei, Xinjiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-54 23

Wei, Yuqin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-39 23

Wen, Chenglin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04-2 18

Wen, Haoqiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-2 16

Weng, Runting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-61 24

Weng, Zhengxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-39 23

Wu, Dehao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-5 16

Wu, Lihua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-6 17

Wu, Na . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-5 19

Wu, Rui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-1 16

Wu, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-08 21

Wu, Xiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-11 21

Wu, Yunli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B3 15

Wu, Yunkai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-43 23

X

Xi, Xiaopeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-01 21

Xia, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-6 16

Xiao, Zhouxiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06-1 17

Xie, Guo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-1 15

Xie, Feiran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-46 23

Xie, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-06 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-06 21

Xing, Dongfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-59 23

Xu, Shuiqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-07 20

29


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB03-3 18

Xu, Zhengguo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-12 20

Xu, Zidong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-6 16

Xue, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-53 23

Y

Yan, Rongyi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-6 16

Yan, XingGang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-1 18

Yan, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-10 21

Yang, Chao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-46 23

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-41 23

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-15 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA03-2 16

Yang, Chunhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-7 16

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-14 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-15 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-46 23

Yang, Chunjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-13 20

Yang, Fan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07 CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-08 20

Yang, Guanghong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06 C

Yang, Hao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-24 22

Yang, Jianguo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-6 19

Yang, Juhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-59 23

Yang, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-11 20

Yang, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B4 15

Yang, Pu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-4 18

Yang, Shuai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-2 18

Yang, Weidong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06-5 17

Yang, Wudong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-3 19

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-26 22

Yang, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-37 22

Yang, Yuhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA02-6 16

Yang, Zeyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-23 22

Yang, Zijiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-3 19

Yao, Lina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB03-2 18

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-08 21

Yao, Yuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-6 19

Ye, Chenglei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-57 23

Ye, Hao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-5 18

Ye, Yinzhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-16 20

Yi, Lifu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08-1 19

Yi, Liu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-06 21

Yin, Hongpeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-58 23

Yin, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB03-4 18

Yin, Shen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-14 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-10 20

Yin, Xiaojing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-3 17

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-5 17

Yu, Kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08-1 19

Yu, Yijian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-59 23

Yuan, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01-A1 15

Yuan, Xiaofeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-7 16

Yuan, Yahui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08-4 20

Yue, Yangtengfei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-3 19

Z

Zeng, Jianping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-5 C

Zeng, Jiusun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-06 22

Zeng, Qingjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-43 23

Zhai Lixiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-3 19

Zhai, Yanqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-32 23

Zhang, Milu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-1 18

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-55 23

Zhang, Xiaojun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A4 15

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B2 15

Zhang, Aoxiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-6 17

Zhang, Bangcheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-4 18

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-5 18

Zhang, Bangcheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-6 18

Zhang, Bo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-01 21

Zhang, Bo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-2 20

Zhang, Chuanyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-29 23

Zhang, Chunming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-4 17

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-4 17

Zhang, Dengfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-2 19

Zhang, Hanwen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-3 17

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-13 21

Zhang, Hongli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-5 17

Zhang, Huifeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-54 24

Zhang, Jiaxu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-19 22

Zhang, Jilie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08 C

Zhang, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A5 15

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-27 23

Zhang, Jingxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-03 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-17 22

Zhang, Junfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-02 22

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-21 23

Zhang, Junzheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-15 21

Zhang, Kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-2 17

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-31 23

Zhang, Ke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-3 19

Zhang, Ke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-1 19

Zhang, Lidong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-5 19

Zhang, Qiming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-04 22

Zhang, Lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-01 21

Zhang, Minjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-11 21

Zhang, Qiyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-6 19

Zhang, Shaojie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-47 24

Zhang, Shen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-45 24

Zhang, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-5 19

Zhang, Wenfang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-5 19

Zhang, Wenhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-1 21

Zhang, Xiangming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-3 21

Zhang, Xiaoxiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-2 21

Zhang, Xiuyun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A3 15

Zhang, Xu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-2 19

Zhang, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-6 61

zhang, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B4 15

Zhang, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B5 15

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA05-6 17

Zhang, Yong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06 CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-2 18

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-3 18

Zhang, Youmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-5 19

Zhang, Yufeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A6 15

30


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-11 22

Zhang, ZhenHai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-32 23

Zhao, Shanshan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-31 23

Zhao Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-2 16

Zhao, Chengrui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-6 17

Zhao, Chenwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-35 23

Zhao, Chunhui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-3 15

Zhao, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-34 22

Zhao, Ling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05 CC

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-2 20

Zhao, Min . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06-3 18

Zhao, Qingxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-6 18

Zhao, Yijie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04-5 19

Zhao, Zhenning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-5 20

Zheng, Liangguang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-6 18

Zheng, Weijian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-3 16

Zheng, Xiaoxiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-22 23

Zheng, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-5 18

Zheng, Yinger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01-A6 15

Zhong, Guanghua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-3 19

Zhong, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-5 19

Zhong, Jingtao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-44 24

Zhong, Kewei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-18 23

Zhong, Maiying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA03-1 17

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-03 22

Zhong, Ningfan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-32 23

Zhong, Yujiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-5 19

Zhou, Donghua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA02-6 16

Zhou, Donghua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA02-4 15

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA02-6 16

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-03 20

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-18 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-01 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-02 21

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-17 21

Zhou, Funa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04-2 19

Zhou, Han . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-58 24

Zhou, Junwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-16 22

Zhou, Le . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-09 21

Zhou, Meng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-4 21

Zhou, Yecheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01-A5 15

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-27 23

Zhou, Zhe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-23 23

Zhou, Zhijie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-13 22

Zhou, Jianghua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B3 15

Zhu, Cunren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-3 18

Zhu, Daqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-25 23

Zhu, Dewen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-61 25

Zhu, Fanglai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09 C

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-3 21

Zhu, Hongyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-10 22

Zhu, Kaiqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-29 23

Zhu, Linfu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-8 19

Zhu, Xiaoqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-1 17

Zhu, Zhiyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-14 22

Zhuang, Hao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-2 19

Zong, Qun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-A3 15

Zou, Xiangyi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-B4 15

31


Book of Abstracts

Saturday, July 6, 2019

SaA01 13:30-17:30 #9 Bld Room 201Salute Session:Seminar on ”Intelligent Autonomous Safety Control ofAerospace and Motion System ” in Commemoration of Jiachi Yang ’sCentenary BirthdayChair: Li, Zhibin Academy of Opto-Electronics, CASCo-Chair: Liu, Wenjing Beijing Institute of Control EngineeringCo-Chair: Hu, Shaolin Xi’an University of Technology

ISaA01-A1 13:30-13:50Sensor fault diagnosis scheme design for spacecraft attitude controlsystem

Yuan, Li Beijing Institute of Control EngineeringWang, Shuyi Beijing Institute of Control EngineeringLiu, Wenjing Beijing Institute of Control EngineeringLiu, Chengrui Beijing Institute of Control Engineering

Aiming at the sensor subsystem of spacecraft attitude control system e-quipped with gyroscopes, earth sensors and sun sensors, the observer-based fault detection and fault location problems are studied to ensurethe decoupling of fault diagnosis results with actuator faults. The kine-matics equation of spacecraft attitude control system and the measure-ment model of various sensors are given. Then the sensor subsystemis regarded as a virtual system with gyroscope outputs as input andearth sensor outputs and sun sensor outputs as outputs.On this basis,observers with different functions are designed according to differentaxes of spacecraft attitude control system, and the diagnostic logic isproposed correspondingly. Finally, the effectiveness of the algorithm isverified by the simulation of the satellite attitude control system.

ISaA01-A2 13:50-14:10Design and Implementation of Autonomous Fault Detection and SafetyManagement for Geostationary Satellite Control System

Liu, Xiaoxiang Beijing Institute of Control EngineeringWang, Shuyi Beijing Institute of Control EngineeringYuan, Li Beijing Institute of Control EngineeringLiu, Yubai Beijing Institute of Control EngineeringGuo, Jianxin Beijing Institute of Control Engineering

The stable operation capability of the control system determines the fi-nal life time of the satellite and the continuity of the satellite’s on-orbitmissions. It is also an important guarantee for the payload capacity.With the increase in the number of satellites both in orbit and underdevelopment, as well as the continuous increase in mission complexi-ty and performance requirements, the fault detection and processing ofGEO satellite control system can no longer rely too much on the ground.Based on the typical configuration and characteristics of GEO satellitecontrol system, this paper gives the design and implementation meth-ods of on-orbit autonomous fault detection and safety management, in-cluding input data validity judgment, component’s fault diagnosis andprocessing, and system-level fault detection and safety managementas well, detailing the precautions and design points, so as to ensurethat the GEO satellite attitude and its working mode are maintained ina stable and productive status throughout the entire life cycle.

ISaA01-A3 14:10-14:30Integrated Design of Fault Diagnosis and Fault Tolerant Control for S-pacecraft System

Zhang, Xiuyun Tianjin Univ.Zong, Qun Tianjin Univ.Liu, Wenjing Beijing Institute of Control Engineering

For spacecraft attitude control system, considering the comprehensiveinfluence of actuator fault, external interference and sensor measure-ment error, first of all, an adaptive sliding mode unknown input observeris designed to achieve simultaneous decoupling estimation of unknownstates, faults and interferences. Compared with the traditional fault di-agnosis methods, the proposed observer can estimate the magnitudeof fault and disturbance at the same time, without complex coordinatetransformation, and eliminate the limit that the fault must be differen-tiable. Secondly, based on the estimations of the observer and con-

sidering the estimation errors, the finite-time fault-tolerant controller isdesigned to realize the integrated design of fault diagnosis and fault-tolerant control, so as to ensure the rapid recovery of stability after thefailure of the spacecraft. Finally, the validity of the proposed algorithmis verified by simulation.

ISaA01-A4 14:30-14:50Direct Adaptive Fault-tolerant Control of Satellite Attitude based onBackstepping and Neural Network

Tian, Kefeng Beijing Institute of Control EngineeringShen, Shaoping Xiamen Univ.Li, Zhibin Academy of Opto-Electronics, CASQin, Huixian Academy of Opto-Electronics, CASZhang, Xiaojun Academy of Opto-Electronics, CASWang, Fan Academy of Opto-Electronics, CAS

Fault-tolerant control is the last line of defense to ensure the safe andreliable operation of the long-term life of the satellite. In actual oper-ation, the accuracy of parameter identification by fault diagnosis andisolation module may not be well guaranteed, which affects the per-formance of the reconstruction controller. In this paper, for satelliteswith attitude control using angular momentum exchange, in order to en-sure that the attitude of the satellite can converge to the desired targetattitude under normal undisturbed conditions, the basic method of re-cursively constructing the Lyapunov function of the closed-loop systemis obtained based on the Backstepping method. Then, based on theinfluence of actuator failure and disturbance torque, using the approx-imation ability of multi-layer neural network, the adaptive fault-tolerantcontrol law consisting of basic control term, neural network term androbust control term is derived. Finally, the simulation comparison ismade for the normal situation, the partial failure of the flywheel and thecomplete failure of the flywheel.

ISaA01-A5 14:50-15:10A New Control Allocation Method Based on the Improved Grey WolfOptimizer Algorithm for Aircraft with Multiple Actuators

Gai, Wendong Shandong Univ. of Science and TechnologySun, Chengxian Shandong Univ. of Science and TechnologyZhou, Yecheng Shandong Univ. of Science and TechnologyZhang, Jing Shandong Univ. of Science and Technology

Abstract―In this paper, a new control allocation method based on theimproved grey wolf optimizer (IGWO) algorithm is proposed for redun-dant control of aircraft with multiple actuators. Firstly, we introduce theADMIRE model which is an aircraft with multiple actuators. Then, thecontroller based on the linear quadratic regulator (LQR) theory is de-signed. And the control allocation method based on improved grey wolfoptimizer algorithm is introduced. Finally, to further prove the effec-tiveness of our proposed method, both the actuator without failure andthe actuator with loss of effectiveness failure are both considered in thesimulation. The results show that the attitude angle control of Aircraftwith multiple actuators can be realized by this method.

ISaA01-A6 15:10-15:30Research on the Flight Anomaly Detection During Take-off PhaseBased on FOQA Data

Jiang, YunPeng China Academy of Civil Aviation Science andTechnology

Le, NingNing China Academy of Civil Aviation Science andTechnology

Zhang, Yufeng China State Shipbuilding CorporationZheng, Yinger China Academy of Civil Aviation Science and

TechnologyJiao, Yang China Academy of Civil Aviation Science and

Technology

Aiming at the take-off phase which has high risk of fatal accidents, adata-driven method for flight anomaly detection is proposed; firstly, thekey performance parameters of the take-off phase are chosen; sec-

32

CAA SAFEPROCESS 2019 Book of Abstracts: Saturday Sessions

ondly, Airbus A320 aircraft is taken as the research object, and thecorresponding QAR data in FOQA station are divided into training setand testing set; thirdly, the training set is clustered by one-class SVMmethod to get the anomaly detection model, then the model is used todetect the outlier of the testing set; finally, the flight anomaly detectionexamples of two-dimensional, three-dimensional and multi-dimensionalparameters are given respectively, and the outlier flights deviating fromthe group characteristics can be accurately marked, which provides im-portant decision support information for the flight safety management.

ISaA01-B1 15:50-16:10Fault-tolerant control for a multi-propeller aerostat based on slidingmode control allocation method

Liang, kuankuan Shanghai Jiao Tong Univ.Chen, li Shanghai Univ. of Engineering ScienceLiu, Jinguo Shenyang Institute of Automation, CAS

This paper develops a fault-tolerant control strategy for a multi-propelleraerostat based on sliding mode control allocation method. The loss ofeffectiveness of propeller faults and the wind disturbance is consideredin the system. The proposed control approach consists of two mod-ules: the upper-level virtual control part, which is designed to enablethe closed-loop system asymptotically stable; the lower-level controlallocation part, which can accommodate the propeller faults, and re-distribute the virtual control vector to the available propellers. Stabilityanalysis shows that the closed-loop system is globally asymptoticallystable. The effectiveness of the proposed fault-tolerant control strate-gy is demonstrated by simulation results based on a simplified multi-propeller aerostat with propeller faults.

ISaA01-B2 16:10-16:30Robust Fault Tolerant Control of Airship Residence Based on LosslessSeparation Operator

Ouyang, Gaoxiang Techn. & Engine. Center for Space Utilization,CAS

Lin, Wenliang Capital Aerospace Machinery Co., LtdMiao, JingGang Academy of Opto-Electronics, CASZhang, Xiaojun Academy of Opto-Electronics, CAS

This paper studies the problem of robust fault-tolerant control caused byactuator failure and uncertain parameters in the process of airship resi-dence control. Firstly the actuator failure is mathematically representedby a generalized quadratic form, which facilitates the consideration ofstructural information and others in controller design. Consequently, thedesign conservatism is greatly reduced. Finally, the non-convex fault-tolerant control of airship pitch angle is convexed by using the losslessseparation operator presented in the paper, and the simulative resultsshow its effectiveness.

ISaA01-B3 16:30-16:50High Performance Control and Fault Tolerant Control Requirements ofAstronomical Observation Carrier Platform

Li, Zhibin Academy of Opto-Electronics, CASZhou, Jianghua Academy of Opto-Electronics, CASWu, Yunli Beijing Institute of Control EngineeringHu, Kang Academy of Opto-Electronics, CASWang, Baocheng Academy of Opto-Electronics, CASHuang, Wanning Academy of Opto-Electronics, CAS

Compared with the observatory built on the ground, it is difficult toachieve ideal observation effect. The observatory based on the nearspace aerospace vehicle not only has the same effect as the astronom-ical satellite, but also has the advantages of low cost and reusability,Figure so it has gradually become an important application directionof the aerospace vehicle. Astronomical observation has high require-ments for platform control: firstly, it needs high pointing accuracy andhigher attitude stability; secondly, the signal from celestial objects isweak and needs long-term continuous tracking observation, so it need-s high reliability and safety control. Therefore, based on the reviewof related research status, this Figure paper focuses on several keytechnologies, including: error allocation of components of closed-loopsystem according to the design of predictability and reconfigurability;scheme and performance requirements of pointing accuracy and sta-bility;method of targeted health prediction; degradation of redundancyallocation and different levels of self-healing control. Correspondencerelation and so on, provide certain guiding Figure significance for thegrowing engineering application needs

ISaA01-B4 16:50-17:10

Effect of Manned Submersible Operation on Structural SafetyQin, Shengjie National Deep Sea CenterZhang, Yi National Deep Sea CenterLiu, Xiaohui National Deep Sea CenterYang, Lei National Deep Sea CenterLiu, Baohua National Deep Sea CenterTang, Miao National Deep Sea CenterZou, Xiangyi National Deep Sea Center

Manned submersible is an important equipment for marine resourcesexploration and development, and its structural framework is the basisto ensure the safety of divers. Based on the submergence data andmaintenance records of the Jiaolong manned submersible, the opera-tion process of the manned submersible is studied, and the influenceof the operation process on the structure frame is analyzed from theaspects of ocean environment factors, the submersion time and the ac-celeration of the submersible in the operation process. The researchshows that the wind wave load, seabed environmental pressure andseabed collision will all bring threats to the structure of manned sub-mersible, and the wind wave load has the greatest impact on the struc-ture safety of the submersible.

ISaA01-B5 17:10-17:30Safety Assessment of the JiaoLong Deep-sea Manned Submersiblebased on Bayesian Network

Liu, Chang Tsinghua Univ.Zhang, Yi Harbin Engineering Univ.He, Xiao Tsinghua Univ.

Safety assessment is of great importance to the deep-sea manned sub-mersible, but little literature has been reported on this topic. The goalof this paper is to work out an effective tool for the safety assessment ofthe deep-sea manned submersible according to the study of JiaoLong,which is the first manned submersible to dive more than 7,000 meters inChina. In this paper, a relative new subsystem division of the mannedsubmersible is introduced firstly. Furthermore, a BN-based safety as-sessment method is proposed which combines the Bayesian Network(BN) and data-driven fault detection algorithms. Qualitative and quan-titative analysis can both be implemented based on the BN, and real-time safety assessment can be realized by combining data-driven faultdetection algorithms. The proposed method is verified on the JiaoLongmanned submersible by constructing and analyzing the BN. Also, anexample of the propeller fault detection using kernel principal compo-nent analysis (KPCA) algorithm is displayed to illustrate how to employthe proposed method in real-time.

SaA02 13:30–17:00 Room 307Award Session: Fang Chong-zhi Excellent Paper Award FinalChair: Hu, Changhua The Rocket Force University of EngineeringCo-Chair: He, Xiao Tsinghua University

ISaA02-1 13:30–13:55Remaining useful Life Prediction of Lithium Ion Battery Based on Im-proved Particle Filter Algorithm

Peng, Xi Xi’an Univ. of TechnologyXie, Guo Xi’an Univ. of TechnologyLi, Xin Xi’an Univ. of TechnologyHu, Shaolin Xi’an Univ. of TechnologyHei, Xinhong Xi’an Univ. of Technology

As a key part of industrial systems, capacity degradation modeling andremaining useful life(RUL) prediction of lithium ion batteries have at-tracted wide attention. For the characteristics of lithium ion battery ca-pacity degradation, particle filter method can be used to predict theRUL. In order to further enhance the stability of the algorithm and im-prove the accuracy of prediction, this paper uses a hybrid algorithmcombining particle filter(PF) algorithm and extended finite impulse re-sponse (EFIR) algorithm to predict the RUL of lithium-ion battery. Inorder to verify the effectiveness of the algorithm, the lithium-ion batterydataset provided by NASA PCoE is used for the experiments, and theprediction errors of the proposed method and standard particle filter al-gorithm are compared. The comparison results show the effectivenessand feasibility of the proposed method.

ISaA02-2 13:55–14:20A Geometric Approach to Fault Detection and Isolation of LinearDiscrete-time Systems

Zhang, Zhao Tsinghua Univ.Huang, Jie Tsinghua Univ.

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He, Xiao Tsinghua Univ.

This paper investigates fault detection and isolation problems fordiscrete-time systems with external disturbance. Using geometric ap-proach, sufficient condition for designing an Hinf-based observer is de-veloped. Coding sets are designed to deal with multiple faults occurringsimultaneously and some valuable conclusions are given. A simulationexample is presented to show the validity of theoretical results and theeffectiveness of the proposed approach.

ISaA02-3 14:20–14:45Fault diagnosis based on EEMD and key feature representation withseparation of stationary and nonstationary signals

Tian, Feng Zhejiang Univ.Zhao, Chunhui Zhejiang Univ.Fan, Haidong Zhejiang Ene. Group Resea. Inst.Zheng, Weijian Zhejiang Ene. Group Resea. Inst.Sun, Youxian Zhejiang Univ.

With the rapid advancement of science and technology, the modern in-telligent power plant with large capacity and low energy consumptiongradually take place of the traditional power plants. Vibration signal iswidely used for fault diagnosis in modern power plant. However, be-cause of nonstationarity, nonlinearity and complexity of vibration signal,it is difficult to analyze the vibration signal directly. To solve the prob-lem, a novel method using vibration signal is proposed in this paper todevelop the fault diagnosis model. First, ensemble empirical mode de-composition (EEMD) is employed to decompose the original signal intoserval intrinsic mode functions (IMF). To derive rich faulty information,both time domain and frequency domain statistical features are calcu-lated for each IMF. Then fault related key features will be selected toreduce the feature redundancy. In feature selection, we find that fault-y information in nonstationary part which may be neglected still playsan important role in fault diagnosis to reveal the trend of the originalsignal. So Augmented DickyFuller test is utilized to divide the IMFs in-to stationary part and nonstationary part, and then feature selection isperformed in stationary part and nonstationary part respectively. Final-ly, the key features of both stationary part and nonstationary part areused to develop fault diagnosis model. The efficacy of the proposedmethod is illustrated using the dataset from the intelligent power plantand the superiorities are shown in comparison with other two method.

ISaA02-4 14:45–15:10Part Mutual Information Based Quality-related Component Analysis forFault Detection

Wang, Yanwen Tsinghua Univ.Chen, Maoyin Tsinghua Univ.Zhou, Donghua Shandong Univ. of Scie. and Techn.

In this article, a novel part mutual information based quality-relatedcomponent analysis (PMIQCA) method is proposed for quality-relatedfault detection by searching for the low-dimensional subspace of mea-surement variables that retains the maximal statistical dependencieswith quality variables. The detection rate of quality-unrelated faults isreduced while the detection rate of quality-related faults is improved.The basic idea is to select the most relevant measurement variablesand principal components (PC) with the maximal part mutual informa-tion (PMI) for each iteration, so as to build a more accurate supervisoryrelationship between process variables and quality variables. Then,the appropriate statistics for quality-related fault detection are estab-lished.Finally, the validity and feasibility of the proposed new methodare demonstrated via Tennessee Eastman Process (TEP).

ISaA02-5 15:10–15:35Multimode Process Monitoring with Mode Transition Constraints

Wu, Dehao Tsinghua Univ.Chen, Maoyin Tsinghua Univ.Zhou,Donghua Shandong Univ.of Scie. and Techn.

Multimode process monitoring has gained much attention both in a-cademia and industry recently, and the hidden Markov model (HMM)has been introduced to model the multimodality of process data. How-ever, most of HMM-based approaches cannot detect the mode disor-der fault, if the multimode process operates under mode transition con-straints. In this paper, a new HMM-based method is proposed to ad-dress this problem. The moving window Viterbi (MW-Viterbi) algorithmis developed to identify operating modes, where reconstructed samplesare utilized for mode identification if a fault occurs. Then, the Maha-

lanobis distance is adopted for fault detection, and a reconstruction-based method is derived for fault identification. Numerical results showthe superiority of the proposed method in multimode process monitor-ing with mode transition constraints.

ISaA02-6 15:35–16:00Fault-tolerant Cooperative Formation Control for Multi-agent Systemswith Actuator Faults

Shi, Jiantao Nanjing Resea. Inst. of Elect. Techn.Zhou, Donghua Shandong Univ. of Scie. and Techn.Sun, Jun Nanjing Resea. Inst. of Elect. Techn.Yang, Yuhao Nanjing Resea. Inst. of Elect. Techn.

This paper deals with the fault-tolerant formation control problem of aclass of linear leader-follower multi-agent systems (MASs) subject toactuator faults. For each following agent, an observer and a fault es-timator are designed to estimate the unmeasurable state and actuatorfault vectors, respectively. Using the online obtained state and fault esti-mation information, a novel cooperative fault-tolerant control protocol isproposed to guarantee formation maintenance of the MAS. It is provedthat the formation errors of all the agents will converge to a small setaround the origin, if the parameters in the cooperative controller, ob-server and fault estimator are properly chosen. Finally, a simulationexample is utilized to illustrate the effectiveness of the proposed fault-tolerantrnformation control protocol.

ISaA02-7 16:00–16:25Deep Learning for Quality Prediction of Nonlinear Dynamic Processeswith Variable Attention-Based Long Short-Term Memory Network

Yuan, Xiaofeng central south Univ.Li, Lin central south Univ.Wang, Yalin central south Univ.Yang, Chunhua central south Univ.Gui, Weihua central south Univ.

Industrial processes are often characterized with high nonlinearitiesand dynamics. For soft sensor modeling, it is of great significanceto model the nonlinear and dynamic relationship between input andoutput data. Thus, recurrent neural network (RNN) and long short-term memory network (LSTM) are suitable for quality prediction of softsensor modeling. However, they do not consider the relevance of d-ifferent input variables for quality prediction. To address this issue, avariable attention-based long short-term memory (VA-LSTM) networkis proposed for soft sensing in this paper, which is the first applicationof attention mechanism in process data modeling area. In VA-LSTM,variable attention is designed to selectively extract important input vari-ables according to their relevance with quality prediction. After that,different weights are adaptively assigned to obtain weighted input sam-ple at each time step. Finally, LSTM network is exploited to capture thelong-term dependencies of the weighted input time series to predict thequality variable. The performance of the proposed modeling methodis validated on an industrial debutanizer column and a hydrocrackingprocess.

SaA03 13:30–15:30 Room 208Model based diagnosis-1Chair: Zhong, Maiying Shandong Univ. of Science and TechnologyChair: Mao, Zehui Nanjing Univ. of Aeron. and Astron.

ISaA03-1 13:30–13:50The parity space-based fault detection for linear discrete time systemswith integral measurements

Zhu, Xiaoqiang Shandong Univ. of Science and TechnologyFang, Jingzhong Shandong Univ. of Science and TechnologyZhong, Maiying Shandong Univ. of Science and TechnologyLiu, Yang Shandong Univ. of Science and Technology

In this paper, we investigate the problem of the parity space-basedfault detection (FD) for a class of linear discrete time systems with in-tegral measurements. The traditional parity space-based FD method isno longer applicable to the systems with integral measurements. Thetransfer matrices need to be redesigned to make the traditional parityrelation still hold. Moreover, an algorithm for calculating the transfermatrices and solving the optimization problem by the singular valuedecomposition (SVD) is also provided. Finally, an illustrative exampleabout the three-tank system is demonstrated the validity of the pro-posed scheme.

ISaA03-2 13:50–14:10

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Open-Circuit Fault Diagnostic Method for Three-level Inverters Basedon Park’s Vector

Wen, Haoqiao Central South Univ.Peng, Tao Central South Univ.Tao, Hongwei Central South Univ.Yang, Chao Central South Univ.

Inverters are important equipment for power transmission, and open-circuit fault is one of the most common faults of the inverter. In or-der to enhance the reliability and the availability of inverters, this paperpresents an open-circuit fault diagnosis method for three-level inverters,which can effectively detect fault condition and locate faulty power de-vice. The normalozed Park’s vector is applied for the fault detection.For fault localization, the fault arm is determined based on the averagevalue of absolute current value of the currents, and then the specificfault power device is located with the polarity of the normalized cur-rent average value. The simulation results indicate that the diagnosticmethod can not only diagnosis open-circuit faults, but also has a betterrobustness.

ISaA03-3 14:10–14:30Application of Hybrid Integrated Model Based on Mechanism ModelResiduals in Fiber Production Melt Conveying

Gong, Longhao Donghua Univ.Hao, Kuangrong Donghua Univ.Ren, Lihong Donghua Univ.

The polyester fiber production process includes three sub-processes ofpolymerization, melt conveying and spinning. Aiming at the predictionof melt performance indicators in the process of melt conveying, thiswork introduces a hybrid integration model by integrating the mechanis-m model and data-driven model. In the melt conveying process of fiberproduction, the data from the heat exchanger to the entire section of thespinneret were selected for comparative experiments. Comparing thehybrid integration model proposed in this paper with Gated RecurrentUnit (GRU), Support Vector Regression (SVR) and mechanism model,both MSE and MAPE evaluation indexes can reflect the superiority ofhybrid integration model in prediction accuracy.

ISaA03-4 14:30–14:50Actuator Fault Detection for Autonomous Underwater Vehicle Using In-terval Observer

Zhang, Chunming Shandong Univ. of Science and TechnologyWang, Xianghua Shandong Univ. of Science and TechnologyRen, Yanheng Shandong Univ. of Science and Technology

This paper proposes a new actuator fault detection scheme based oninterval observer for autonomous underwater vehicle (AUV). Firstly, thenonlinear vertical motion model of AUV is simplified and a linear timeinvariant (LTI) system is obtained. For this LTI system, interval observeris designed, which consists of two parts: one for the estimate of the up-per bound of the states and the other for the estimate of the lower boundof the states. Using the information from interval observer, residual andthreshold are generated simultaneously, which is quite different from theconventional method where the generation of residual and the choiceof threshold are separated. Hence the proposed scheme provides amore simple realization of actuator fault detection. Finally, simulationsare conducted to verity the effectiveness of the proposed scheme.

ISaA03-5 14:50–15:10Research on Fault Estimation and Fault-tolerant Control of HypersonicAircraft Based on Adaptive Observer

Fang, Yifan Nanjing Univ. of Aeron. and Astron.Jiang, Bin Nanjing Univ. of Aeron. and Astron.Lv, Xunhong Nanjing Univ. of Aeron. and Astron.Mao, Zehui Nanjing Univ. of Aeron. and Astron.

This paper studies fault estimation and fault-tolerant control schemefor a class of hypersonic aircraft with actuator faults and disturbances.In order to estimate the unknown fault, a robust adaptive observer isestablished with the adaptive law for fault estimation, and the designconditions are derived through the Lyapunov stability analysis to en-sure the estimation errors converge and the disturbances satisfy theH-Infinity performance. Based on the fault estimation, an active fault-tolerant controller is constructed to make the faulty system stable andsatisfy some H-Infinity performance. Finally, the effectiveness of theproposed fault estimation and fault-tolerant control scheme is verifiedon the semi-physical simulation platform of a hypersonic aircraft.

ISaA03-6 15:10–15:30Detecting and Estimating Intermittent Actuator Faults in Linear S-tochastic Systems

Yan, Rongyi Beijing Instit. of Elect. EngineeringXia, Hui Beijing Instit. of Elect. EngineeringLiang, Tong Beijing Instit. of Elect. EngineeringHe, Xiao Tsinghua Univ.

Intermittent faults (IFs) in practical process are characterized by non-determinability, repeatability and unpredictability, which poses an enor-mous challenge to diagnosing IFs. In this paper, the detection andestimation problem of intermittent actuator faults for a class of linearstochastic systems is investigated. In order to determine the appearing(disappearing) time before the subsequent disappearing (appearing)time, an observer-type detection filter is designed by utilizing a geo-metric approach. Based on the moving horizon technique, the outputof the observer is applied to construct a novel residual, which is moresensitive to the appearing (disappearing) time of the IF. Then, two hy-pothesis tests are proposed to determine all the appearing time anddisappearing time, respectively. Moreover, an estimation algorithm isprovided for the magnitude of the IF. Finally, a simulation example onan unmanned arial vehicle system is given to illustrate the effectivenessof the proposed scheme.

SaA04 13:30–15:30 Room 207DDD methods-1Chair: Song, Zhihuan Zhejiang Univ.Chair: Li, Yuan Shenyang Univ. of Chemical Techn.

ISaA04-1 13:30–13:50Back-propagation Based Contribution for nonlinear fault diagnosis

Qian, Jinchuan Zhejiang Univ.Jiang, Li Zhejiang Univ.Song, Zhihuan Zhejiang Univ.Ge, Zhiqiang Zhejiang Univ.

This paper proposes a novel fault diagnosis method by means of back-propagation based contribution (BBC) for nonlinear process. As amethod based on the deep learning model, BBC can deal with thenonlinear problem in process monitoring by utilizing the nonlinear fea-tures extracted by auto-encoder (AE). Moreover, the smearing effectis an important factor affecting the performance of fault diagnosis. Inorder to solve this problem, BBC utilizes the basic idea of reconstruc-tion based contribution (RBC), and describes the propagation of faultinformation by back-propagation (BP) algorithm. The validity of the pro-posed method is tested and verified by a nonlinear numerical exampleand the Tennessee Eastman benchmark process.

ISaA04-2 13:50–14:10Industrial Process Fault Classification Based on Weighted Stacked Ex-treme Learning Machine

Gao, Kai China Univ. of PetroleumDeng, Xiaogang China Univ. of PetroleumCao, Yuping China Univ. of Petroleum

Stacked extreme learning machine (SELM) has emerged as an effec-tive industrial process fault classification method. However, complexindustrial processes usually involve a large number of variables, whichmay have different effects on fault classification performance. To dis-tinguish process variables’effect on classification result, a partial Fvalue based weighted stacked extreme learning machine (FSELM) isproposed for fault classification. Firstly, the partial F value method isused to analyze process variables’contribution to each type of faultdata. Then, the weight of each process variable is determined basedon contribution analysis. Lastly, the stacked extreme learning machineis utilized to learn the relationship between the weighted process vari-able data and fault types. Simulation results on the Tennessee East-man process show that, the proposed method has higher classificationaccuracy than the extreme learning machine and the stacked extremelearning machine.

ISaA04-3 14:10–14:30Fault Diagnosis of Chemical Processes Based on k-NN Distance Con-tribution Analysis Method

Wang, Guozhu Henan Institute of Tech.Du, Zhiyong Henan Institute of Tech.Hu, Yongtao Henan Institute of Tech.

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Li, Yuan Shenyang Univ. of Chemical Tech.

In modern chemical processes, varieties of fault detection and diagno-sis methods have been used for ensuring process safety and productquality widely. As an important branch, fault detection and diagnosismethods based on data-driven are effective in large-scale chemical pro-cesses. However, they do not often show superior performance owingto the self-limitations and the characteristics of process data, such asnonlinearity, non-Gaussian, and multi-operating mode. To cope withthese issues, k-NN (k-Nearest Neighbor) fault detection method and itsextension have been developed in recent years. Nevertheless, thesemethods are used for fault detection mainly, few papers can be foundabout fault diagnosis. In this paper, a novel abnormal variables iden-tification method is proposed, this method uses k-NN distance contri-bution analysis theory to evaluate which variables are most likely tobe abnormal, meanwhile, the feasibility of this method is verified bycontribution decomposition theory. The proposed search strategy canguarantee that all abnormal variables are found in each sample. The re-liability and validity of the proposed method are verified by a numericalexample and the Continuous Stirred Tank Reactor system.

ISaA04-4 14:30–14:50Bearing Fault Detection and Separation of Wind Turbine Based on Arti-ficial Immune System

Ren, Yanheng Shandong Univ. of Sci. and Tech.Wang, Xianghua Shandong Univ. of Sci. and Tech.Chunming, Zhang Shandong Univ. of Sci. and Tech.

In this paper, a novel bearing fault diagnosis algorithm based on arti-ficial immune system is proposed for wind turbine. Historical data ofdifferent cases is firstly analyzed and features including mean and vari-ance are got, which are then transformed into binary strings. Thesebinary strings are regarded as self-strings, based on which detectorsare generated using negative selection algorithm for fault detection andpositive selection algorithm for fault separation. For test data, featuresand their corresponding binary strings are calculated, which are thencompared with proposed detectors according to the improved Ham-ming matching rule. From the comparison results, it can be got whetherthe bearing fails or not (namely fault detection) and if any, which faultbelongs to (namely fault separation). To verify the effectiveness, simu-lations are conducted finally.

ISaA04-5 14:50–15:10Improved transfer component analysis and it application for bearingfault diagnosis across diverse domains

Ma, Ping Xinjiang Univ.Zhang, Hongli Xinjiang Univ.Wang, Cong Xinjiang Univ.

In recent years, intelligent fault diagnosis models based on machinelearning used for intelligent condition monitoring and diagnosis haveachieved considerable success. However, in the current research, thediagnosis process is based on an assumption that the same featuredistribution exists between training data and testing data. Regrettably,in real application, training data and testing data are often from diversedomains, the difference in feature distributions is often prevalent; in thiscase, the traditional diagnostic models lack adaptability. To addressthis issue, this work proposed a diagnosis framework based on domainadaptation. This framework is inspired by the domain adaptation abili-ty of transfer learning, in that the model trained by the labeled data insource domain can be transferred to diagnose a new but similar targetdata. The domain adaptation algorithm transfer component analysis (T-CA) and its improved algorithm- improved transfer component analysis(ITCA) are embedded into this framework, respectively, to verify its ap-plicability. An experiment was conducted on the datasets of bearing todemonstrate the applicability and practicability of the proposed transferframework. The results show that the proposed method presents highaccuracy in the transfer task of bearing fault diagnosis under differentconditions across diverse experimental positions and fault types.

ISaA04-6 15:10–15:30Batch Process Monitoring Using Multi-way Laplacian Autoencoders

Gao, Xuejin Beijing Univ. of Tech.Xu, Zidong Beijing Univ. of Tech.Wang, Pu Beijing Univ. of Tech.

For the highly nonlinear batch process, a fault detection method basedon multi-way Laplacian autoencoder (MLAE) is proposed. Autoencoder

(AE) is an effective non-linear feature extraction method. However, AEsignored the local geometric structure of original data set. The proposedMLAE method add Laplace regularization term to loss function, whichthe extracted feature preserves more local geometric structure informa-tion. Moreover, the corresponding Laplacian matrix is constructed byaverage local affinity matrix of all batch runs, which contains the infor-mation of the stochastic variations and deviations among batches andgreatly reduces the amount of training calculation. Finally, the proposedmethod is applied to penicillin fermentation process for simulation ex-periment. The results show that the method can detect faults in timeand has good monitoring performance.

SaA05 13:30–15:30 Room 205Condition monitoring and fault predictionChair: Fang, Huajing Huazhong Univ. of Science and TechnologyChair: Peng, Kaixiang University of Science and Technology Beijing

ISaA05-1 13:30–13:50An Improved LSTM Neural Network with Uncertainty to Predict Remain-ingUseful Life

Wu, Rui Beijing Information Sci. and Tech. Univ.Ma, Jie Beijing Information Sci. and Tech. Univ.

Data-driven Prognostic(DDP) has become one of the major method ofcomponent of Prognostic and Healthy Management(PHM) systems inthe industrial area. The fault prediction methods mainly include faultfailure probability assessment and remaining useful life(RUL) predic-tion. As the basis for the development of equipment maintenance strat-egy, the remaining service life prediction is one of the important links ofPHM. Accurately predicting the RUL can provide comprehensive, accu-rate and effective information for the development of equipment mainte-nance strategies, which helps to avoid equipment failure and reduce theloss caused by failure, thus ensuring the safe and reliable operation ofthe equipment. In recent years, the RUL prediction has received exten-sive attention in research and engineering fields and achieved certainresults. Among them, the method based on degraded data modelinghas become one of the mainstream methods in the field of life predic-tion because it does not require failure data and the convenience ofcharacterizing the uncertainty of degradation. DDP about RUL methodbased on degradation data can be classified into the machine learningmethod and the mathematical statistics method. Prognostic techniquesare designed to accurately estimate the RUL of subsystems or compo-nents using sensor data. However, mathematical statistics methods ofestimating RUL use sensor data to make assumptions as to how thesystem degrades or fades (eg, exponential decay); As well as the cur-rent some machine learning methods ignore the uncertainty. Basedon current problems, we propose a novel (Long-Short Term Memo-ry)LSTM Neural Network complement with Uncertainty: automatical-ly learn higher-level abstract representations from the underlying rawsensor data (All Features Extraction), and use these representations toestimate RUL from the sensor data; it does not rely on any degradationtrend assumption, is robust to noise, and can handle missing valuesand uncertainty. We compared several publicly available algorithms ona publicly available Turbofan engine dataset and found that several ofthe LSTM metrics (Score, etc.) outperformed the previously proposedstate-of-art techniques.

ISaA05-2 13:50–14:10A Novel Scheme for Remaining Useful Life Prediction and Safety As-sessment Based on Hybrid Method

Peng,Kaixiang University of Science and Technology BeijingJiao, Ruihua University of Science and Technology BeijingZhang, Kai University of Science and Technology BeijingMa, Liang University of Science and Technology BeijingPi, Yanting University of Science and Technology Beijing

The prediction of remaining useful life (RUL) and safety assessment arethe key of prognostics and health management (PHM) that provide de-cision support for it. A hybrid approach for the prediction of RUL whichcombines partial least squares (PLS) with support vector regression(SVR) and similarity based prediction (SBP) is proposed firstly. TheSVR model, trained in a supervised manner, is employed to learn fea-tures extracted by PLS to capture the health indicator (HI) degeneratetrajectory. Then the RUL prediction is implemented by calculating thesimilarity between the HI degenerate trajectories. Furthermore, on thebasis of the prediction results, we construct a fuzzy comprehensive e-valuation model to evaluate the safety level. To validate the proposed

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approach, a case study is performed on benchmark simulated aircraftengine datasets. The results show the superiority of the hybrid ap-proach compared with other methods reported in the literature and in-dicate the effectiveness of the fuzzy comprehensive evaluation methodin safety assessment.

ISaA05-3 14:10–14:30RUL Prediction: Reducing Statistical Model Uncertainty Via BayesianModel Aggregation

Jia, Chao China Elect. Standard. InstituteZhang, Hanwen Zhejiang Univ.

It is important to predict the remaining useful life (RUL) for evaluatingthe performance of industrial equipment. Many simple and complexmethods have been proposed to predict RUL based on stochastic pro-cesses. However, these methods have different prediction accuracies.The uncertainty associated with using one of these methods instead ofanother is called statistical model uncertainty. Therefore, some prob-lems naturally arise: How can we reduce the uncertainty among d-ifferent methods? Is it possible to obtain a more exact prediction ofRUL, compared with the individual method? In this study, we apply aBayesian model aggregation (BMA) approach to solve these problems.For a Wiener degradation process with unknown parameters, assumethat there are 𝑃 types of methods to predict RUL, for example, max-imum likelihood estimation (MLE), stochastic Newton algorithm (SNA)and Kalman filter (KF)- based methods. Then, there are 2𝑃 −1 distinctcombinations of these 𝑃 types of methods, each with a correspondingstatistical model and an estimated parameter vector. BMA can statis-tically combine these estimated parameter vectors through a weightedaverage, and thus, the probability density function (PDF) of RUL canbe obtained. BMA can be successfully applied to realistic bearing da-ta, and simulation results show that BMA achieves higher predictionaccuracy than an individual method.

ISaA05-4 14:30–14:50Research on Measurement Methods of Transferability between Differ-ent Domains in Transfer Learning

Qin, Ruochen Beihang Univ.Lu, Chen Beihang Univ.

If transfer learning can be used in fault diagnosis of rotating machin-ery, the transferability between different fault domains should be mea-sured first. A certain degree of similarity between the source domainand the target domain is the prerequisite for the effectiveness of thetransfer learning method. In this paper, the transferability measurementresults of mahalanobis distance, Kullback-Leibler Divergence and Max-imum Mean Discrepancy (MMD) between data domains are comparedand analyzed. And the stability and accuracy of mobility measuremen-t results are compared and analyzed by using statistical analysis andanomaly detection. Finally, a case study is carried out using the gear-box data set to verify the effectiveness and superiority of the proposedmethod.

ISaA05-5 14:50–15:10Centrifugal pump fault diagnosis based on MEEMD-PE Time-frequencyinformation entropy and Random forest

Wang, Yihan Beihang Univ.Liu, Hongmei Beihang Univ.

In the process of fault diagnosis of centrifugal pump, according to thecharacteristics of large amount of information, non-stationary and non-linear of vibration signal, a fault diagnosis method based on modifiedensemble empirical mode decomposition(MEEMD-PE) time-frequencyinformation entropy and Random forest is proposed in this paper. First,the intrinsic mode functions (IMFs) components from high frequency tolow frequency are obtained by MEEMD-PE method, and the IMFs withnoise components are determined by the permutation entropy, TheseIMFs are regarded as pseudo components and removed. The main re-maining IMFs, which contain important fault information are retained;Second, the short-time Fourier transform is performed on a series ofIMFs. Then the time-frequency matrix containing the fault feature in-formation is obtained. The time-frequency information entropy is alsocalculated. The principal component analysis method is used to re-duce the dimension of the obtained fault feature matrix, removing re-dundant feature information. At the same time, wavelet entropy featureextraction method is used to compare MEEMD-PE time-frequency in-formation entropy. Finally, the fault feature matrix after dimensionalityreduction is classified by random forest. The experimental results show

that the method can effectively diagnose the centrifugal pump.

ISaA05-6 15:10–15:30Safety-Oriented Fault Monitoring for Manned Deep-Sea Submersibles

Zhang, Yi Harbin Engineering Univ.Ding, Zhongjun National Deep Sea CenterLiu, Chang Tsinghua Univ.Qi, Haibin National Deep Sea CenterZhao, Qingxin National Deep Sea CenterHuang, Jie Tsinghua Univ.He, Xiao Tsinghua Univ.

Fault monitoring for manned deep-sea submersibles has great signifi-cance for the safety of pilots and equipment of submersibles. Basedon an analysis of the existing fault monitoring methods and fault con-tent of the manned deep-sea submersible JIAOLONG, faults actuallyoccurred in JIAOLONG in the recent years are investigated in detail.Safety-oriented fault categorization for manned deep-sea submersibleJIAOLONG is proposed. Also, this paper goes into discussion and anal-ysis of the existing fault monitoring systems and looks into the prospectof future development of fault monitoring for manned deep-sea sub-mersibles.

SaA06 13:30-15:30 #9 Bld Room 301Invited Session: Data-driven fault diagnosis and health management ofindustrial systemsChair: Zheng, Ying Huazhong Univ. of Science and TechnologyCo-Chair: Liu, Zhenxing WuHan Univ. of Science and TechnologyCo-Chair: Zhang, Yong WuHan Univ. of Science and Technology

ISaA06-1 13:30-13:50Research on Remaining Useful Life Prediction Based on Nonlinear Fil-tering for Lithium-ion Battery

Xiao, Zhouxiao Huazhong Univ. of Science and TechnologyFang, Huajing Huazhong Univ. of Science and TechnologyChang, Yang Huazhong Univ. of Science and Technology

With the widespread application of lithium-ion batteries in industriesaround the world, lithium-ion battery performance degradation predic-tion and remaining useful life (RUL) estimation methods are receiv-ing much more attention. This paper summarizes the nonlinear filter-ing algorithms used in RUL estimation of lithium-ion batteries, whichcompares and analyzes the applicable conditions and performance ofthe commonly used nonlinear filtering algorithms, including extendedKalman filtering (EKF), unscented Kalman filtering (UKF), particle filter-ing (PF), extended particle filtering (EPF) and unscented particle filter-ing(UPF). Simulations are obtained by lithium-ion battery performancedegradation model and the performance of these algorithms are veri-fied.

ISaA06-2 13:50-14:10Diagnose of Sub-module Fault in Modular multilevel converters Basedon Moving Average Method

Li, Cui Huanggang Normal Univ.Liu, Zhenxing WuHan Univ. of Science and TechnologyZhang, Yong WuHan Univ. of Science and Technology

Modular multilevel converters (MMCs) have become one of the mostpromising topologies for medium- and high-power applications withtheir characteristics, such as scalability, redundancy and high quali-ty output voltage and current. Reliability is a very important issue fora MMC system, MMCs are composed of several sub-modules (SMs).Hence, the diagnosis of faulty SMs is important to guarantee properoperation. In this paper, an effective fault localization approach for MM-Cs is proposed, based on moving average of the SM capacitor voltageto locate the faulty SM. Specifically, localization evaluates the SM volt-age variation. To verify the feasibility and effectiveness of the proposedfault localization approach, we performed MMC simulation sunder nor-mal and open-circuit fault conditions using MATLAB. The results showthat the proposed approach can quickly and easily locate the faulty SMsto allow a stable operation of the MMC.

ISaA06-3 14:10-14:30Fault Detection of Modular Multilevel Converter with Kalman FilterMethod

Hu, Huanzhen Wuhan Univ. of Science and TechnologyZhang, Yong Wuhan Univ. of Science and TechnologyLiu, Zhenxing Wuhan Univ. of Science and Technology

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Zhao, Min Wuhan Univ. of Science and Technology

In this paper, the fault detection problem of the modular multilevelconverter (MMC) system is investigated with kalman filtering method.Based on the running rules of the circulating current and output currentfor the MMC system, the state-space model is established and the esti-mation of both the circulating current. The output current is realized byusing the kalman filtering theory. By collecting the predicted and mea-sured values of circulating current and the output current, the residualcan be achieved by using the difference between them. Nextly, theresidual estimation function and its threshold are constructed, then thefault can be detected according to the proposed fault detection strategy.Finally, 11 levels of MMC simulation system in MATLAB/Simulink is setup, the effectiveness of the proposed fault detection method is verified.

ISaA06-4 14:30-14:50Fault Detection of Lithium-Ion Batteries Subject to Probabilistic Para-metric Uncertainties

Liu, Yuhao Huazhong Univ. of Science and TechnologyWan, Yiming Huazhong Univ. of Science and Technology

The fault detection task in lithium-ion battery management system (BM-S) is critical to the safety and reliability of rechargeable and hybrid elec-tric vehicles. To explicitly account for inevitable errors of battery modelparameters, a robust fault detection method is proposed in this paper.The residual generator is established by exploiting an equivalent cir-cuit model. The effect of uncertainties on the residual is parametrizedas polynomial dependence on probabilistic uncertain parameters andnoises. Then Gaussian mixtures are adopted to derive the residu-al distribution. To account for residual uncertainties in the detectiondecision, the weighted sum of distances from the generated residualto each Gaussian component of the residual distribution is proposed.Simulation results illustrate that our proposed method outperforms theconventional approach that does not consider parametric uncertainties.

ISaA06-5 14:50-15:10A Fault Detection Method with Ensemble Empirical Mode Decomposi-tion and Support Vector Data Description

Wang, Yang Huazhong Univ. of Science and TechnologyLing, Dan Zhengzhou Univ. of Light IndustryYang, Weidong Huazhong Univ. of Science and TechnologyTao, Bo Huazhong Univ. of Science and TechnologyZheng, Ying Huazhong Univ. of Science and Technology

In order for fault detection of the processes with noise and nonlinearity,a method based on Ensemble Empirical Mode Decomposition (EEMD)and Support Vector Data Description (SVDD) is proposed. In this work,EEMD-based denoising method is utilized to remove the noise fromthe original dataset. The SVDD model is then developed to handlethe nonlinear data for fault detection. The proposed method containsthree steps. Firstly, the original dataset is decomposed into a seriesof Intrinsic Mode Functions (IMFs) by EEMD method. Each IMF char-acterizes the corresponding scale information of the data. Secondly,the original data is reconstructed using partial reconstruction denoisingmethod. Only the relevant IMFs which mostly contain useful informa-tion are retained, and the IMFs that primarily carry noise are discarded.The optimal number of relevant IMFS is selected based on Signal-to-Noise Ratio (SNR). Finally, the SVDD model is constructed on the re-constructed data to detect the faults. The effectiveness of the proposedmethod is demonstrated by a numerical example. The results showthe proposed method performs better compared with the other existingmethods.

ISaA06-6 15:10-15:30Research on Reliable High-speed Train Axle Temperature MonitoringSystem based on Fluorescence Optical Fiber Temperature Sensor

Wang, Fei Xi’an Univ. of TechnologyZheng, Liangguang Southeast Univ.Zhao, Chengrui Ningbo CRRC Times Transducer Technology CO.,

LTD

The platinum resistance temperature sensor which is widely used inhigh speed train has the problem of reliability, and usually caused traindelay. This paper presents a reliable high-speed train axle tempera-ture monitoring system based on fluorescent optical fiber temperaturesensor, which has the advantages of accurate temperature measure-ment, good versatility, simple principle, and low cost. The fluorescentdecay effect is used to measure the temperature, and the optical fiber is

used to transfer the fluorescent signal in order to overcome the failure ofinsulation, water, and electromagnetic interference (EMI). The systemshows high accuracy and reliability in the type test, and will be used onthe track engineering vehicle to get a real test.

SaA07 13:30-15:30 #Bld Room 308Invited Session: Fault Diagnosis and Forecasting Method Fusing Qual-itative Knowledge and Quantitative InformationChair: Zhang, Bangcheng Changchun Univ. of TechnologyCo-Chair: Huang, Deqing Southwest Jiaotong Univ.Co-Chair: Hu, Guanyu Hainan Normal Univ.

ISaA07-1 13:30-13:50Navigation of Simultaneous Localization and Mapping by Fusing RGB-D Camera and IMU on UAV

Dai, Xi Southwest Jiaotong Univ.Mao, Yuxin Southwest Jiaotong Univ.Huang, Tianpeng Southwest Jiaotong Univ.Li, Binbin Southwest Jiaotong Univ.Huang, Deqing Southwest Jiaotong Univ.

Simultaneous localization and mapping (SLAM) is one of the key com-ponents of the navigation system of unmanned aerial vehicles (UAVs).In this paper, a novel system that enables a UAV to navigation in a GPS-denied environment with an RGB-D (Red, Green, Blue, and Depth)camera and Inertial Measurement Unit (IMU) is presented. Such a sys-tem is realized via a host computer on a UAV, in which the IMU infor-mation read from the flight controller and the image information readfrom the RGB-D camera are fused using extended Kalman filter (EKF).Experiments are designed to test the feasibility of the proposed naviga-tion scheme. Accurate assessment of our idea shows a good responsewhen rapidly moving and rotating the UAV

ISaA07-2 13:50-14:10Neural-Network-Based State and Fault Estimation for a Discrete-TimeNonlinear System

Zhang, Xiaoxiao Shandong Univ. of Science and TechnologyWang, Youqing Shandong Univ. of Science and Technology

An observer for the simultaneous estimation of the system state andactuator and sensor faults of a discrete recurrent neural network (RNN)system was proposed. The proposed approach enabled disturbanceattenuation and guaranteed observer convergence. First, the discreteRNN was transformed into the form of discrete linear parameter vari-ation (LPV) model. Second, the LPV model was further transformedinto a descriptor system by extending system state and sensor fault.Then, an 𝐻∞ observer was proposed for the simultaneous estimationof the system state and actuator & sensor faults of the discrete LPVsystem. Finally, the problem of observer design was translated into alinear matrix inequality solution. Numerical simulation results validatedthe effectiveness and correctness of the presented method.

ISaA07-3 14:10-14:30Fault Prediction of High-speed Train Running Gears Based On HiddenMarkov Model and Analytic Hierarchy Process Method

Cheng, Chao CRRC Changchun Railway Vehicles Co.LTDQiao, Xinyu Changchun Univ. of TechnologyFu, Caixin CRRC Changchun Railway Vehicles Co.LTDWang, Weijun Changchun Univ. of TechnologyYin, Xiaojing Changchun Univ. of Technology

The stable operation of the high-speed train running gear is one of thekey components to ensure the safety and reliability of high-speed trains.This paper proposes a research method based on the hidden Markovmodel and analytic hierarchy process (HMM-AHP) high-speed train run-ning gear fault prediction. The multi-observation sequence is used tomake the hidden Markov model more accurately model the system un-der study to describe the internal and external characteristics of thesystem. In order to ensure the more accurate and reliable results of thefusion of multi-observation sequence data, the analytic hierarchy pro-cess method is used to reasonably assign the feature weighting factorto predict the online fault of the high-speed train running gear, and for-m an online fault diagnosis framework based on HMM-AHP model. Atlast, this paper takes the high-speed train running gear as the researchobject. The proposed HMM-AHP model is used to simulate the high-speed of trains running gear, and the validity and accuracy of the modelare verified.

ISaA07-4 14:30-14:50

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A Survey of Fault Diagnosis Methods for White body Welding Produc-tion Line

Wang, Jidong Changchun Univ. of TechnologyGao, Siyang Changchun Univ. of TechnologyZhang, Bangcheng Changchun Univ. of TechnologyLv, Shiyuan Changchun Univ. of Technology

The article mainly introduces in detail the outline of the malfunction ofthe White body welding assembly line, including its specific definition,the cause of the malfunction and the harm caused by the malfunction.Firstly, the signal extraction method of White body welding assemblyline is introduced, and then the prediction method of failure rate andknowledge are presented respectively from the model. The fault diag-nosis method of current BIW welding assembly line is expounded inthree aspects of the prediction method of failure rate of data. At last,the developing trend of fault diagnosis method of White body weldingassembly line is described.

ISaA07-5 14:50-15:10Fault Prediction of Brightness Sensor based on BRB in Rail VehicleCompartment LED Lighting System

Yin, Xiaojing Changchun Univ. of TechnologyShi, Guangxu Changchun Univ. of TechnologyZhang, Bangcheng Changchun Univ. of TechnologyLv, Shiyuan Changchun Univ. of TechnologyShao, Yubo Changchun Univ. of Technology

To guarantee the normal workflow and accurate brightness adjustment,it is important to predict fault of brightness sensor in rail vehicle com-partment LED lighting system. In this paper, a BRB (belief rule base)based fault prediction model is proposed to accurate brightness adjust-ment and reliability based on the analysis of the failure mechanism ofthe brightness sensor in the rail vehicle compartment LED lighting sys-tem. The fault prediction model based on BRB can make full use of thesystem’s expert prior knowledge, which can fuse the system featurequantity to achieve accurate fault prediction of the brightness sensor.In this process, the parameters of the model are updated by iterativeestimation algorithm to compensate for the inaccuracy of expert knowl-edge. Finally, in order to verify the validity and accuracy of the proposedmodel, a case is studied by using the proposed prediction model forbrightness sensor module in the rail vehicle compartment LED lightingsystem, which shows that the method can accurately predict the faultswith qualitative knowledge and quantitative information.

ISaA07-6 15:10-15:30Fault Diagnosis Method of WSN Nodes Based on Wavelet Packet andBelief Rule Base

Zhang, Bangcheng Changchun Univ. of TechnologyZhang, Yang Changchun Univ. of TechnologyZhang, Aoxiang Changchun Univ. of TechnologyWu, Lihua Hainan Normal Univ.Hu, Guanyu Hainan Normal Univ.

In order to diagnose node faults in Wireless Sensor Network (WSN),a new fault diagnosis method based on wavelet packet and belief rulebase (BRB) is proposed in this paper. Firstly, based on the data setof wireless sensors, the experimental samples of WSN nodes undervarious typical fault conditions are established through fault simulation;secondly, the energy of each frequency band of the samples is calcu-lated by using three-layer wavelet packet decomposition, and the fea-ture vector of WSN nodes fault diagnosis is constructed by the ratioof energy of each frequency band in normal operation; finally, the faultdiagnosis method of WSN nodes is proposed by using the BRB mod-el and the expert knowledge. The experimental results show that themethod proposed in this paper can effectively use qualitative knowledgeand quantitative data to establish a non-linear model between input andoutput in the environment of small sample data, and can achieve goodtest results for nodes fault diagnosis of WSN.

SaA08 13:30-15:30 #9 Bld Room新闻厅Invited Session: Fault diagnosis and Health Management of Rail TransitChair: Cai, Bai-gen Beijing Jiaotong Univ.Co-Chair: Luo, Linkai Xiamen Univ.Co-Chair: Jin, Huiyu Xiamen Univ.

ISaA08-1 13:30-13:45Vibration Signal Analysis For Rail Flaw Detection

Li, Bin CRSC Research & Design Institute Group Co. Ltd

Chen, Xiaoguang CRSC Research & Design Institute Group Co.Ltd

Wang, Zhixin CRSC Research & Design Institute Group Co. LtdTan, Shulin CRSC Research & Design Institute Group Co. Ltd

Rails are the foundation of rail transport, and any defects of the railsmay directly affect the running state of the train or even lead to majorsafety incidents. However, the existing algorithms for rail crack detec-tion are too expensive and difficult to perform on-line real-time moni-toring. In this paper, we propose a method based on vibration signalfor rail crack detection, which fits the vibration signal in the healthy railand the cracked rail by least squares method. The transmission modeof vibration signal in the healthy rail and the cracked rail can be con-structed, and the transmission mode can be used to distinguish the dif-ference between the two types of rail on the higher harmonics. On thisbasis, the crack type of the cracked rail can be further distinguished.Using this method, we have established a rail crack detection system,which achieves a good on-line detection of cracks, and we discuss thereliability and safety of its on-line use in the future.

ISaA08-2 13:45-14:00Text Mining Based Identification Model for Urban Rail Transit SystemInfrastructure Fault Analysis

Zhang, Bo Beijing National Railway Research & DesignInstitute of Signal & Communication Group Co. Ltd

Li, Qing Beijing National Railway Research & DesignInstitute of Signal & Communication Group Co. Ltd

A text mining based identification model for urban rail transit system in-frastructure fault analysis is proposed for the Fault analysis and providebasis for fault prevention, control and diagnosis. The proposed modeluses the Naive Bayes algorithm is used to segment the long text datawhich has been preprocessed, and identifies the cause of infrastruc-ture fault. The analysis results are displayed clearly in the form of wordclouds. The proposed model was experimentally verified by using 15240 records of AFC equipment faults (defects) records from ChongqingMetro Group Co., Ltd. in 2018.

ISaA08-3 14:00-14:15Failure Recognition for Switch Machines Based on Machine Learning

Hu, Enhua CASCO Signal Ltd.Zhu, Cunren CASCO Signal Ltd.Li, Chunmei CASCO Signal Ltd.

With the faster and faster development of urban rail transit, the schedul-ing of the operation has become increasingly tight. In light of this back-ground, higher demands on the reliability and maintainability of metrosignaling equipment were proposed. At present, the switch machine,which contributes the highest failure rate in the operation of urban railtransit lines, has caught the attention of the maintenance companies,since its failure and disrepair may directly affect the punctuality andthe occurrence of accidents. When the switch malfunctioned or oper-ated abnormally, some differences will be revealed on the curve. Con-sequently, important evidence for the normal operation of the switchmachine is whether the characteristic curve of the switch is displayednormally. On the basis of understanding the characteristics of com-mon failures, analyzing the characteristic curve for normal operationsof the switch machine can contribute to the proactive determination ofwhether there might be an impending fault with the machine; or thequicker localization and diagnosis of the cause after the failure.

ISaA08-4 14:15-14:30Big Data Platform for Faults Prediction Diagnosis of CBI Conferences& Symposia

Chen, Yu CASCO SIGNAL LTD.Chen, Jiyu CASCO SIGNAL LTD.

Safety and reliability have always been the most concerned research ofcomputer based interlocking system(CBI). With the rapid developmentof computer technology, information network technology, new materialtechnology and digital technology, CBI system itself produces massiveand complex data, which has become an important resource for inno-vation and development of CBI system. How to use these massive datato help the system complete automatic or semi-automatic fault diagno-sis, and then ensure the safe operation of the system, has become thefocus for various interlocking manufacturers. This paper introduces Fail-Safe, several fault model analysis methods, and introduces a big dataplatform which can be used for fault prediction and diagnosis analysismethods.

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ISaA08-5 14:30-14:45Data Acquisition and Transmission System for Tramcar Powered by Hy-drogen Cell

Li, Hui CRRC Tangshan Co.,Ltd.Sun, Jinghui CRRC Tangshan Co.,Ltd.Dong, Jingchao CRRC Tangshan Co.,Ltd.Lu, Yiming CRRC Tangshan Co.,Ltd.Du, Fei CRRC Tangshan Co.,Ltd.

Aimed at the limited recording capability of critical vehicle data of ex-istent recorder devices on tramcars, this paper studies the CANopennetwork, the design and development of an onboard data recorder suit-able for hydrogen cell power tramcars. The recorder is able to recordall the data over vehicle bus, and transmit in distance pre-configuredcritical data and malfunction information in real time. The ground serv-er receives this information and performs data mining, thus realizingautomated diagnostics of tramcars.

ISaA08-6 14:45-15:00Prospect and Review on Deepen Diagnosis and Maintenance of Elec-trified Module for Rail Vehicles

Song, Liqun CRRC Tangshan co., LTDSun, Jian CRRC Tangshan co., LTD

With the application of electrical control system in rail vehicles, the auto-matic control technology, network technology and intelligent diagnosishas been improved continuously, and meanwhile the continuous im-provement of vehicle performance occurred. Simultaneously the needfor faulty diagnostic and maintenance are coming out. Currently diag-nostic and maintenance level stays at the basic surface level, and themainly failure repair is replacement, resulting in a high reserve rate of s-pare parts, a long maintenance period of faulty parts, and a long periodof capital turnover. Rail vehicles urgently need to conduct further diag-nosis and maintenance research in the field of electrification, which isthe level of diagnosis and maintenance should be deepened. This pa-per is to propose a development direction of deep diagnosis and deepmaintenance. The test bench equipment is used to stress maintenancedown to the circuit board component level, speeding up the develop-ment from basic maintenance to deep diagnosis and maintenance, andeffectively strengthen the diagnostic and maintenance capabilities forthe electrical equipment of rail transit vehicles.

ISaA08-7 15:00–15:15Train-level Fault Diagnosis based-on Feature Selection

Li, Zheng CRRC Tangshan Co.,LtdSun, Jinghui CRRC Tangshan Co.,LtdDong, Jingchao CRRC Tangshan Co.,Ltdli, Hui CRRC Tangshan Co.,LtdDu, Fei CRRC Tangshan Co.,Ltd

In this paper, a train-level fault diagnosis optimization method based onfeature selection is proposed. Based on historical operational data, thefeature space of train-level fault diagnosis is established. An improvedsupport vector machine (SVM)-based feature selection method is usedaccording to feature contribution. The value is screened out as a diag-nostic parameter for train-level faults, and train-level fault diagnosis isoptimized. In this paper, taking the train-level traction severe fault asan example, combined with the actual operational data, the intrinsic re-lationship between the fault and the train signals is studied to optimizethe train-level fault diagnosis.

ISaA08-8 15:15–15:30Performance Monitoring and Analysis of Down-Link Signal in Balise-based Train Positioning Systems

Li, Zhengjiao Beijing Jiaotong Univ.Cai, Bagen Beijing Jiaotong Univ.Liu, Jiang Beijing Jiaotong Univ.Wei, Shangguan Beijing Jiaotong Univ.Lu, Debiao Beijing Jiaotong Univ.Zhu, Linfu Acade. of Railway Science

To monitor and analyze of the down-link signal transmission perfor-mance of the balise-based train positioning system (BTPS), a new per-formance monitoring and analysis parameter is proposed. This paperbuilds a transmission model of down-link signal in BTPS, and describesthe shortcomings of the existing performance monitoring and analysismethods. By analyzing the equivalent circuit schematic of the on-boardantenna and the balise antenna, the input impedance is proposed as

an important parameter for evaluating the down-link signal transmissionperformance. 6 statistical features are extracted from the main lobeenvelope curve of the real part of the complex input impedance, thetransmission performance of down-link signal in BTPS at different trainoperating condition are analyzed. The simulation results demonstratethe effectiveness of the input impedance for analyzing the transmissionperformance of down-link signal in BTPS.

SaA09 13:30-15:30 #9 Bld Room 202Fault-tolerant control methodsChair: Zhang, Youmin Concordia Univ.Chair: Zhang, Dengfeng Nanjing Univ. of Sci. and Tech.

ISaA09-1 13:30-13:50Active Fault-Tolerant Control of A Class of Multi-Agent Systems Basedon Sliding Mode Technology

Ma, Renyue Nanjing Univ. of Aero. and Astr.Zhang, Ke Nanjing Univ. of Aero. and Astr.Jiang, Bin Nanjing Univ. of Aero. and Astr.Yan, Xing-gang Univ. of Kent

The fault-tolerant formation control problem of a class of multi-agentsystems is investigated in this paper, and a distributed active fault-tolerant control protocol based on fault estimation observer and slidingmode control technique is constructed to reduce the influence of thefault. The theoretical analysis shows that the desired formation patternand trajectory will be achieved and a simulation result illustrates theeffectiveness of the method.

ISaA09-2 13:50-14:10Fault-tolerant Speed Synchronous Control of Multi-motor System a-gainst Inverter Faults

Zhang, Dengfeng Nanjing Univ. of Sci. and Tech.Zhuang, Hao Nanjing Univ. of Sci. and Tech.Lu, Baochun Nanjing Univ. of Sci. and Tech.Bo, Cuimei Nanjing Tech. Univ.Wang, Zhiquan Nanjing Univ. of Sci. and Tech.

Considering the servo DC motor subject to possible power inverter ordrive faults, a simple fault-tolerant speed synchronous control schemeis proposed for the multi-motor system with ring-coupling structure. Byvirtue of the advantages of the ring-coupling based synchronizationcontrol technique, only the fault detector for testing the faults of eachmotor subsystem and the fault-tolerant control (FTC) decision logic unitare attached to the multi-motor system in order to modify the referenceinput of each subsystem. Such strategy does not interfere with the reg-ular system operation and reduces the FTC design complexity. Theseparation between the tracking controllers design and the synchro-nization compensators design also reduces the complexity of the con-trol law development, so as to guarantee the expected synchronizationprecision even in the fault case. Simulations on the speed synchronouscontrol of a triple brushless DC-motor system demonstrate the validityof the proposed FTC strategy

ISaA09-3 14:10-14:30Adaptive Backstepping Fault Tolerant Controller Design for UAV withMultiple Actuator Faults

Qian, Moshu Nanjing Tech. UniversityZhai, Lixiang Nanjing Univ. of Aero. and Astr.Zhong, Guanghua Nanjing Tech. UniversityGao, Zhifeng Nanjing Univ. of Posts and Telecommunications

An adaptive backstepping fault-tolerant flight control design is proposedfor an attitude system of unmanned aerial vehicle (UAV) with externaldisturbances and multiple actuator faults. The allowable actuator fail-ure is unknown, and the total occurances of faults can be unlimited.An adaptive backstepping controller is designed to compensate for theactuator fault effect and realize attitude tracking without additional faultdetection and estimation mechanism. It is proved ultilizing Lyapunovanalysis that the all the signals of the closedloop system are globallybounded, and the output tracking error of the system can exponentiallyconverge to any small neighborhood of origin. Finally, simulation resultsare given to demonstrate the effectiveness of the proposed strategy.

ISaA09-4 14:30-14:50Adaptive Fault-tolerant Controller for Hypersonic Flight Vehicle with S-tate Constraints Using Integral Barrier Lyapunov Function

Peng, Zhiyu Nanjing Univ. of Aero. and Astr.

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Qi, Ruiyun Nanjing Univ. of Aero. and Astr.

For the longitudinal model of hypersonic flight vehicles (HFV), this ar-ticle designs an adaptive fault-tolerant controller to achieve full-stateconstraints. Firstly, integral barrier Lyapunov function (iBLF) is appliedon the parameterized longitudinal model to ensure that the flight pathangle (FPA), the angle of attack (AOA), and the pitch rate in the con-straint interval, and the problem of ”differential expansion” of is avoid-ed because of the introduction of the dynamic surface method. Then,aiming at the unknown fault of the rudder surface, the fault-tolerant con-troller structure is designed. Finally, it is proved using Lyapunov theorythat the proposed method can ensure the closed-loop stability of thesystem, and all of the signals in the system are bounded. Simulationresults verify the effectiveness of the controller design in this paper.

ISaA09-5 14:50-15:10Active Fault-Tolerant Tracking Control of an Unmanned Quadrotor Heli-copter under Sensor Faults

Zhong, Yujiang Northwestern Ploytech. Univ.Zhang, Wei Northwestern Ploytech. Univ.Zhang, Youmin Concordia Univ.Zhang, Lidong Fudan Univ.

This paper is devoted to the design of a nonlinear active fault-toleranttracking control (AFTTC) scheme for an unmanned quadrotor helicopter(UQH) under sensor faults. The proposed AFTTC scheme is dividedinto two loops, where the outer loop and inner loop controllers are de-signed for the position control and the attitude control, respectively. Thesliding mode control (SMC) algorithm is introduced to make the UQHtrack the desired trajectory and yaw angle, and to stabilize the pitchand roll motions. For implementing the active fault-tolerant control, afault detection and diagnosis (FDD) scheme is constructed by utilizinga robust three-step unscented Kalman filter. Based on the precise faultinformation, the SMC-based control laws are updated online to mitigatethe adverse effects of sensor faults. Simulation results show that theproposed AFTTC scheme works well in both normal and different faultyscenarios.

ISaA09-6 15:10–15:302D constrained iterative learning predictive fault-tolerant control forbatch processes with state delay

Song, Jiang Hainan Normal Univ.Luo, Weiping Hainan Normal Univ.Zhang, Qiyuan Liaoning Shihua Univ.Wang, Limin Hainan Normal Univ.

The 2D state space system model is established with state delay andactuator faults, and what is more, the iterative learning control (ILC) lawrequired by the batch process model is designed. Using the knowledgeof 2D system theory and an iterative learning control law, the 2D statespace model can be changed into an equivalent 2D-Roesser closed-loop system model. According to the optimized cost function and Lya-punov stability theory, sufficient conditions for the solvability of modelpredictive control (MPC) problems in the form of linear matrix inequal-ities (LMIs) are given. A new solution is proposed which depends onthe bound of the state delay. At last, the effectiveness of the method isproved through simulation experiments.

SaB03 15:50–17:30 #9 Bld Room 208Model based diagnosis-2Chair: Hou, Yandong Henan Univ.Chair: Yao, Lina Zhengzhou Univ.

ISaB03-1 15:50–16:10A rapid diagnosis method of small faults based on adaptive synovialobserver

Liu, Chang Henan Univ.Huang, Ruirui Henan Univ.Hou, Yandong Henan Univ.Cheng, Qianshuai Henan Univ.

In order to solve the problem of fast fault estimation involving large am-plitude noise disturbance and small amplitude fault at the same time,a new sliding mode variable structure adaptive estimation method isdesigned. First, the original system is decoupled into two subsystem-s by constructing a suitable transformation matrix. For the subsystemcontaining only minor faults, a small fault fast estimation algorithm isproposed, which significantly improves the estimation performance ofsmall faults. For the subsystem containing noise disturbances and s-

mall faults, a sliding mode observer is designed to eliminate the effectsof noise and stabilize the integrated observer. Then the Lyapunov sta-bility theory is used to prove the stability of the proposed integrated ob-server. Finally, the effectiveness of the method is verified by simulationexperiments.

ISaB03-2 16:10–16:30Sensor fault diagnosis and fault tolerant control for stochastic distribu-tion time-delayed control systems

Wang, Hao Zhengzhou Univ.Yao, Lina Zhengzhou Univ.

In this paper, a new fault diagnosis and fault-tolerant control methodbased on the model equivalent transformation is proposed for the s-tochastic distribution time-delayed control systems, in which the ran-dom delay between the controller and the actuator and the externaldisturbance is considered. The system is modeled by using a linear B-spline to approximate the probability density function (PDF) of systemoutput. The original system is transformed into an equivalent systemwithout random delay based on the Laplace transformation method.Then, the equivalent system is converted to the augmentation systemwith a new state variable is introduced. The observer is designed toestimate the fault information based on the augmentation system. Ob-server gain matrices and controller parameters are obtained by solv-ing the linear matrix inequality (LMI). The adaptive control algorithm isused to make the PDF of the system output track the desired distribu-tion. Finally, the validity of the proposed method is verified by computersimulation results.

ISaB03-3 16:30–16:50Sensor Fault Estimation via Iterative Learning Scheme for LinearRepetitive System

Feng, Li Chongqing Jiaotong Univ.Deng, Meng Chongqing Jiaotong Univ.Xu, Shuiqing Hefei Univ. of Tech.Zhang, Ke Chongqing Univ.

In this study, a sensor fault estimation framework is proposed for linearrepetitive system. The problem of sensor fault estimation is convertedto state estimation via state redefinition. Then, state observer is de-signed for state reconstruction while iterative learning law is presentedfor fault estimation. The uniformly convergence of error extended sys-tem is guaranteed by asymptotic stability and optimal function. Finally,the efficiency and merits of the proposed scheme are illustrated by anumerical example.

ISaB03-4 16:50–17:10Interval Observer-based Fault Detection for UAVs Formation with Actu-ator Faults

Yin, Lei Nanjing Univ. of Aero. and Astr.Liu, Jianwei Nanjing Univ. of Aero. and Astr.Yang, Pu Nanjing Univ. of Aero. and Astr.

This paper presents an interval observer-based fault detection (IOFD)scheme in unmanned aerial vehicles (UAVs) formation system. First-ly, for the formation system in healthy case, the interval observer isconstructed based on the known boundary information of the distur-bance term and relative output estimation error. Then, the residual er-rors that can be used for detecting the actuator faults are developed bythe output estimation error. Different from the traditional FD schemes,the IOFD scheme does not need the threshold generators and residualevaluation functions. At last, simulation results are given to illustrate theeffectiveness and feasibility of proposed scheme.

ISaB03-5 17:10–17:30Fault Estimation Observer Design for a Class of Nonlinear Multi-agentSystems in Finite Frequency Domain

Fan, Qian Hubei Indu. Cons. Group Co.,LtdChen, Xiaohui Wuhan Univ. of Sci. and Tech.Chen, Jianliang Wuhan Univ. of Sci. and Tech.

This paper focuses on the fault estimation observer design problem infinite frequency domain for a class of Lipschitz nonlinear multi-agentsystems. First, the relative output estimation error is defined basedon the directed communication topology of multi-agent systems, andan observer error system is obtained by connecting adaptive fault es-timation observer and the state equation of the original system. Then,the sufficient conditions for satisfying the robust performance index infinite frequency domain are obtained according to the performance in-

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dex defined in finite frequency domain and the properties of the matrixtrace. Meanwhile, the pole assignment method is used to configure thepoles of the observer error system in a certain area. Finally, the simula-tion results are presented to illustrate the effectiveness of the proposedmethod.SaB04 15:50–17:30 #9 Bld Room 207DDD methods-2Chair: Wen, Chenglin Hangzhou Dianzi Univ.Chair: Wang, Tianzhen Shanghai Maritime Univ.

ISaB04-1 15:50–16:10An Imbalance Fault Detection Method for Marine Current Turbine UsingVoltage Signal

Li, Zhichao Shanghai Maritime Univ.Wang, Tianzhen Shanghai Maritime Univ.Zhang, Milu Shanghai Maritime Univ.Wang, Yide Nantes Univ.Diallo, Demba Univ. of Paris-Sud

Marine current turbine (MCT) has been widely used recently. Attach-ments on blades will affect the operation of the system by introducingimbalance and it is essential to monitor its working state, repair or re-place the faulted blade (s) to prevent its damages. MCT imbalancefault detection using electric signals has advantages over traditionalvibration-based method. However, there are some shortcomings in us-ing decomposition method to eliminate the influence of turbulence. Inthis paper, an imbalance fault detection method using voltage signalis proposed for marine current turbines. In the proposed method, theinstantaneous voltage frequency and average voltage frequency is cal-culated through Hilbert transform (HT). Meanwhile, the imbalance faultfrequency is extracted by using the cubic spline interpolation. Final-ly, the time-frequency spectrum of wavelet transform (WT) is used todetect whether there is a blade imbalance fault. Theoretical analysis,simulation results and experimental results under different conditionsvalidate the proposed method.

ISaB04-2 16:10–16:30Deep Learning Fault Diagnosis Method Based on Feature GenerativeAdversarial Networks for Unbalanced Data

Zhou, Funa Henan Univ.Yang, Shuai Henan Univ.Chen, Danmin Henan Univ.Wen, Chenglin Hangzhou Dianzi Univ.

Due to its powerful feature representation capabilities, DNN can be ap-plied to the field of fault diagnosis. When the samples used to trainDNN are unbalanced, the fault features extracted from only a smal-l number of fault samples via DNN can be masked by large number ofnormal samples, which may result in high misclassification rate. Aimingat this problem, this paper proposes a fault diagnosis method basedon feature Generative Adversarial Network (FGAN) and stacked Auto-Encoder (SAE). The main innovation is to design a feature guided gen-erator to improve the performance of GAN. The generator designed inthis paper can generate more qualified new samples in the sense thatit uses the fault feature extracted by SAE to guide the training of thegenerator. The experimental results of rolling bearings verify the effec-tiveness of the proposed algorithm.

ISaB04-3 16:30–16:50Moving window abnormal-condition monitoring strategy based on su-pervised sample selection and its industrial application

He, Kaixun Shandong Univ. of Sci. and Tech.Su, Zhaoyang Shandong Univ. of Sci. and Tech.

With training data of redundant information, process monitoring mod-els inevitably introduce excessive noise which could increase the falsealarm rate (FAR) and the mis-detection rate (MDR). Hence, informa-tive training samples should be selected carefully and it is the key stepto establish a desirable process monitoring model. To cope with thisproblem, in the present work, a supervised training sample-selectionmethod is proposed. In addition, to detect the abnormal-conditionof dynamic processes, a moving-window strategy based on an im-proved statistical index is proposed. With our method, monitoring modelcould be developed more conveniently and the start and ending time ofabnormal-condition could be detected timely. To prove the validity andeffectiveness of our proposed strategy, a numerical simulation and asimulation industrial process are presented.

ISaB04-4 16:50–17:10

Composite Fault Diagnosis of Rotor Broken Bar and Air Gap Eccentric-ity Based on Park Vector Module and Decision Tree Algorithm

Mao, jiaqi Nanjing Univ. of Aeronautics and AstronauticsChen, Fuyang Nanjing Univ. of Aeronautics and AstronauticsJiang, Bin Nanjing Univ. of Aeronautics and AstronauticsWang, Li Nanjing Univ. of Aeronautics and Astronautics

Taking the traction motor of CRH2 high-speed train as the researchobject, this paper proposes a composite fault diagnosis method basedon park vector module for the composite fault of rotor broken bar andair gap eccentricity. Firstly, the current noise is reduced with the im-proved empirical mode decomposition method; and the three phasestator current is converted to park vector using the extension park vec-tor method, to effectively avoid the case in which the composite faultfeatures are submerged by the fundamental frequency characteristic-s; Secondly, the park vector module of stator current is transformedby fast Fourier transform, and compound fault features are extracted infrequency domain. Finally, the fault feature is put into the decision treeclassifier to estimate the fault degree. The data of CRH2 semi-physicalsimulation platform are used to verify the validity of this method.

ISaB04-5 17:10–17:30A Decision Tree Based Method for Treatment Therapy of HCC

Zhao, Yijie Tsinghua Univ.Ye, Hao Tsinghua Univ.Zhong, Jing WiseHealthcare TECLOGY (SHANGHAI) Co. LtdWang, Haiwei WiseHealthcare TECLOGY (SHANGHAI) Co. Ltd

Hepatocellular carcinoma is a common cancer in China. The progno-sis of HCC patients has great relationship with doctors and hospitals.China’s medical system has concluded a standard treatment crite-rion recent years. However, it is difficult to measure the consistencybetween the criterion and clinical operations. This paper focuses onlearning the treatment experience from advanced doctors using data-based method. A decision tree method is proposed to generate aux-iliary treatments automatically. The average predict consistency rateswith doctors are over 65%. It can work even with data missing. Fur-thermore, factors recommended by standard criterion mostly appear intop layers of decision tree, which indicates the decision tree method isa meaningful attempt to analyze HCC treatment from data perspective.

SaB05 15:50–17:30 #9 Bld Room 205Invited Session: Fault diagnosis and health managementChair: Huang, Darong Chongqing Jiaotong Univ.Co-Chair: Zhao, Ling Chongqing Jiao-tong Univ.Co-Chair: Li, Wei Lanzhou Univ. of Technology

ISaB05-1 15:50-16:10Power load prediction method based on VMD and dynamic adjustmentBP cooperation

Kuang, Fengtian Chongqing Jiaotong Univ.Huang, Darong Chongqing Jiaotong Univ.

Aiming at the shortcomings of low prediction accuracy due to the ran-domness and complexity of power load data, this paper proposes apower load prediction method based on VMD and dynamic adjustmentBP. Firstly, for the redundant information and trend components con-tained in the original data of the power load, the VMD decomposedcomponent reconstruction is used to remove the trend component andthe redundant information. Secondly, based on the VMD detrended,aiming at the disadvantage of low accuracy caused by fixed points intraditional BP neural network prediction, the dynamic adjustment of n-odes is designed to achieve the optimal prediction. Finally, based on theelectric load data provided by Chongqing Tongnan Electric Power Co.,Ltd., the prediction model proposed in this paper is used to estimatethe electric load forecast. The comparison of the example simulationresults shows that the predicted values of the VMD and the dynamical-ly adjusted BP cooperative electric load forecasting method are closerto the real one. The load value and the prediction error are lower, whichis a better short-term power load forecasting method.

ISaB05-2 16:10-16:30An improved LSSVM fault diagnosis classification method based oncross genetic particle swarm

Zhang, Xu Chongqing Jiao-tong Univ.Huang, Darong Chongqing Jiao-tong Univ.Zhao, Ling Chongqing Jiao-tong Univ.Mi, Bo Chongqing Jiao-tong Univ.

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Liu, Yang Chongqing Jiao-tong Univ.

It is difficult to select the parameters of least squares support vectormachine (LSSVM) when studying the classification algorithm, A particleswarm optimization algorithm based on crisscross inheritance methodis proposed to find the optimal parameters of LSSVM. Further, thewavelet packet is adopted to process the bearing signal and extrac-t time-frequency domain features, which are used as the input of theLSSVM. The classification model is established and applied to diag-nose the fault of bearing. Classification result shows the classificationaccuracy is improved, and the LSSVM is optimized.

ISaB05-3 16:30-16:50Cryptanalysis on A (k,n)-threshold Multiplicative Secret SharingScheme

Long, Ping Chongqing Jiaotong Univ.Mi, Bo Chongqing Jiaotong Univ.Huang, Darong Chongqing Jiaotong Univ.Pan, Hongyang Chongqing Jiaotong Univ.

Shamir’s secret-sharing scheme is an important building block ofmodern cryptography. However, since multiplication between two vari-ables is not linear, how to confidentially and efficiently multiply twoshared secrets remains an open problem. Recently, Taihei et al. pre-sented a feasible (k,n)-threshold secret-sharing protocol which is capa-ble of achieving such product result even if only k servers are available.Nevertheless, we argue their scheme is vulnerable that the thresholdproperty can not withstand collaborative attacks. Thus accordingly,in this paper, we designed a practical cracking method against theirscheme. In terms of intensive analysis, it can be see that our schemeis able to efficiently reveal the shared secret with high probability albeitless than k servers are compromised.

ISaB05-4 16:50-17:10Health Supervision based on Low Rank Analysis for Aerospace Track-ing

Liu, An State Key Laboratory of Astronautic DynamicsHu, Shaolin Xi’an Satellite Control CenterWang, Ming Xi’an Satellite Control CenterSong, Jianguo Xi’an Satellite Control Center

In view of the big noise and performance degradation of the trackingprocess of the ground TTC (Tracking, Telemetering, and Command)equipment, it is difficult to diagnose and identify the abnormal condi-tions problems. A method for establishing a low rank analysis model ispresent. Through the tracking of historical data, a mathematical modelof low rank decomposition is established. Furthermore, the anoma-ly monitoring and identification of tracking process can be carried outmore accurately through the establishment of maximum variance statis-tic control line. According to the projection of statistics, the influencevariables of abnormal occurrence are separated and achieve abnormalseparation and alarm. The multi-loop tracking data for a satellite by ac-tual tracking can be analyzed to show that his method can effectivelyeliminate the influence of measurement noise in tracking process, ef-fectively identify abnormal land realize abnormal separation and alarm.

ISaB05-5 17:10-17:30Research on Co-Design of Security Control and Communication for Cy-ber Physical System under Cyber-Attacks

Shi, Yahong Lanzhou Univ. of TechnologyLi, Wei Lanzhou Univ. of Technology

For actuator failures and cyber-attacks, this paper investigates the co-design problem of fault tolerance/attack tolerance control and com-munication quality for cyber physical system (CPS). Firstly, basedon the discrete event-triggered communication scheme (DETCS), asystem framework of fault-tolerant/attack-tolerant control is proposed,and a closed-loop CPS model is established that integrates the trig-ger condition, actuator failures and cyber-attacks into a single uni-form framework. Secondly, synthesizing the appropriate Lyapunov-Krasovskii functions, the time-delay system theory and the affineBessel-Legendre inequality, the -stable constrained sufficient conditionwith fault-tolerance/attack tolerance for CPS is deduced. Then a lessconservative design method of robust fault-tolerant/attack-tolerant con-troller gain matrix and event trigger matrix is given. In this way, the co-design goal involving security control and communication is achieved.Finally, the simulation experiments are conducted to demonstrate theeffectiveness of the proposed method.

SaB06 15:50–17:50 #9 Bld Room 301Risk estimation of control systemsChair: Yang, Guanghong Northeastern Univ.Co-Chair: Liu, Yi Zhejiang Univ. of Tech.

ISaB06-1 15:50–16:10A Dynamic Risk Analysis Method for High-speed Railway CatenaryBased on Bayesian Network

Ma, Mengbai Shandong Univ. of Sci. and Tech.Dong, Wei Tsinghua Univ.Sun, Xinya Tsinghua Univ.Ji, Xingquan Shandong Univ. of Sci. and Tech.

The catenary of the high-speed rail power supply system is greatly af-fected by the weather during operation. Once it breaks down, there willbe serious consequences. Besides, the mechanism of failure risk ofcatenary is complex so that it’s difficult to analyze. Aiming at suchcharacteristics, this paper proposes a dynamic flashover risk proba-bility calculation method combining characteristic quantity based onBayesian network. In this paper, the flashover risk propagation chainof the catenary in the humid and polluted environment is establishedand the probability mathematical model of the risk propagation processis given. In addition, the mechanism of risk propagation is used toestablish the functional relation between the monitored characteristicquantity and the risk probability. Then the functional relation is used asthe dynamic condition probability of Bayesian network to calculate thedynamic probability of the whole risk. The consequences of rail stationpassenger congestion caused by catenary flashover in bad weather areanalyzed and the severity of consequence is determined to assess thedynamic risk level.

ISaB06-2 16:10–16:30A Method of Dynamic Risk Analysis and Assessment for Metro PowerSupply System Based on Fuzzy Reasoning

Guo, Lili Shandong Univ. of Sci. and Tech.Dong, Wei Tsinghua Univ.Sun, Xinya Tsinghua Univ.Ji, Xingquan Shandong Univ. of Sci. and Tech.

Direct current (DC) cable is the main current-carrying component in theDC transmission system. Its operating state is important to the stabilityof the metro traction power supply system. Once a fault occurs, it willnot be able to supply power to the train normally and cause seriousconsequences. At the same time, its risk mechanism and propagationchain are complex, so it is not easy to analyze. Aiming at such char-acteristics, this paper proposes a dynamic risk analysis and evaluationmethod for metro power supply system based on fuzzy reasoning. Inthis paper, the risk propagation chain model of the subway DC powersupply system causing the power supply system to stop power supply isstudied, and the fault mechanism of DC cable breakdown is analyzed.The fuzzy probability is used to derive the risk propagation probability,and the graph theory is used to analyze the severity of the risk conse-quences caused by DC cable breakdown. Finally, a dynamic risk analy-sis and evaluation method for the DC cable breakdown risk propagationchain of the subway power supply system is established.

ISaB06-3 16:30–16:50A Dynamic Risk Analysis Method for Compound Faults of Traction Sys-tem of High Speed Train Based on Characteristic Variables

Yue, Yangtengfei Shandong Univ. of Sci. and Tech.Dong, Wei Tsinghua Univ.Sun, Xinya Tsinghua Univ.Ji, Xingquan Shandong Univ. of Sci. and Tech.

The traction system of high-speed train has many faults, among whichcompound faults account for a certain proportion. Compared with s-ingle faults, compound faults will lead to more serious consequences.This paper takes the fault chain of compound fault of ”CRH380D high-speed train traction inverter IGBT open circuit and motor speed sen-sor gain coefficient is too small” as an example, establishes a math-ematical model of the fault chain based on the fault mechanism, andobtains the relationship between fault characteristic variables and trac-tion motor failure rate by using relevant experimental data and nationalstandards, thus obtains the probability of compound risk chain occur-rence. Event tree and grey clustering method were used to evaluatethe consequences of the compound risk chain. Finally, taking the ac-tual Chengdu-Chongqing passenger dedicated line as an example, therisk analysis and evaluation of the compound fault were carried out.

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ISaB06-4 16:50–17:10A Dynamic Risk Analysis Method for Escalator of Rail Transit HubBased on Characteristic Quantity

Ding, Shige Shandong Univ. of Sci. and Tech.Dong, Wei Tsinghua Univ.Sun, Xinya Tsinghua Univ.Wang, Haixia Shandong Univ. of Sci. and Tech.

In this paper, a research method based on characteristic quantity is pro-posed aiming at the dynamic risk propagation of escalators in regionalrail transit hubs. Through the analysis of the risk propagation process ofescalator electrical fault, the function relationship between failure rateof escalator and characteristic quantities is obtained. Then AnyLogicis used to model for obtaining the dynamic distribution of passengerflow in the transportation hub, so as to get the consequences severityof the escalator failure. Finally, the Chongqing North Railway Stationtransportation hub is selected as a case to verify the effectiveness ofthe method, so it provides effective technical support for the risk controlof escalator electrical faults in the transportation hub.

ISaB06-5 17:10–17:30A Dynamic Risk Analysis and Assessment Method for Traction Systemof Metro Train Based on Characteristic Quantity

Zhao, Zhenning Shandong Univ. of Sci. and Tech.Dong, Wei Tsinghua Univ.Sun, Xinya Tsinghua Univ.Wu, Na Shandong Univ. of Sci. and Tech.

Aiming at the characteristics of various fault risks, difficulty in quanti-fying risk factors and complex fault propagation mechanism of subwaytrain traction system, in this paper, the typical faults frequently occur-ring in subway traction system is studied and the dynamic risk analysisand evaluation method of metro traction system is proposed. The faultevent that the traction motor is difficult to operate normally due to thebreakdown of the front end support capacitor of the traction invertersystem is selected. By extracting the relevant characteristic quantityin the risk chain, the probability of the traction motor failure due to thebreakdown of the support capacitor of the inverter system is obtained.The risk impact combined with the relevant factors of metro operationand maintenance is assessed using cluster analysis method. The pro-posed method takes the quantitative characteristic of fault risk propaga-tion into account, and can overcome the problems that risks are difficultto quantify in traditional risk analysis methods.

ISaB06-6 17:30–17:50Orthogonal Locality Preserving Projections Thermography for Subsur-face Defect

Liu, Kaixin Zhejiang Univ. of Tech.Tang, Yuwei Zhejiang Univ. of Tech.Yao, Yuan National TsingHua Univ.Liu, Yi Zhejiang Univ. of Tech.Yang, Jianguo Zhejiang Univ. of Tech.

In this work, the nonlinear dimensionality reduction technique formanifold learning is introduced into the field of thermographic da-ta processing to facilitate subsurface defect detection. Specifical-ly, thermal images recorded by an infrared camera are analyzed bythe proposed orthogonal locality preserving projections thermography(OLPPT) method. In detail, OLPPT constrains the primary functions’orthogonality on the basis of the LPP ground and studies the thermo-graphic data by mapping the adjacency graph to retain the local fea-tures. The projection results highlight the locations and shapes of thedefective areas by eliminating the majority of uneven backgrounds. Thefeasibility of OLPPT is demonstrated with its application to the defectanalysis of a carbon fiber reinforced polymer (CFRP) sample.

SaB07 15:50–17:30 #9Bld Room 308Invited Session:Advanced Alarm Systems for Complex Industrial Facil-itiesChair: Wang, Jiandong Shandong Univ. of Sci. and Tech.Co-Chair: Yang, Fan Tsinghua Univ.

ISaB07-1 15:50–16:10An Improved Intelligent Warning Method Based on MWSPCA and itsApplication in Drilling Process

Geng, Zhiqiang Beijing Univ. of Chemical Tech.Chen, Ning Beijing Univ. of Chemical Tech.Wang, Zhongkai Beijing Univ. of Chemical Tech.

Han, Yongming Beijing Univ. of Chemical Tech.

The drilling process is an important step in petroleum production engi-neering, but the drilling process is risky and costly. In order to improvethe safety and reduce the impact of faults in the drilling process, thispaper proposes an intelligent early warning method based on movingwindow sparse principal component analysis (MWSPCA). The MWSP-CA method is used to monitor the drilling process in real time, andquickly locate the occurrence time of the anomaly and identify the pos-sible fault types. Finally, the proposed method is applied to analyze thedata of the drilling process. The experimental results verify that the pro-posed method can effectively warn the faults in the drilling process andreduce risks and costs during the petroleum production engineering.

ISaB07-2 16:10–16:30Energy Efficiency Recognition and Diagnosis of Complex IndustrialProcesses using Multivariate Nonlinear Regression Method

Geng, Zhiqiang Beijing Univ. of Chemical Tech.Cheng, Minjie Beijing Univ. of Chemical Tech.Han, Yongming Beijing Univ. of Chemical Tech.Wei, Qin Beijing Univ. of Chemical Tech.Ouyang, Zhi Beijing Univ. of Chemical Tech.

Due to the characteristics of multi-dimensional, strong coupling andnoise of complex industrial data, this paper establishes an energy ef-ficiency recognition and diagnosis model using the multivariate nonlin-ear regression method to analyze the objective mathematical relation-ship between raw material consumption and the corresponding outputin complex industrial processes. The curve estimation between rawmaterials and production of complex industrial processes are carriedout. Then the raw materials are replaced by the calculation results ofthe equation with higher fitting degree. Finally, the linear regression andenergy efficiency diagnosis of the substituted raw materials are carriedout. The proposed model is tested and analyzed using actual industrialdata of ethylene production plants. The results show the validity andapplicability of the proposed model. Meanwhile, the objective mathe-matical relationship between the ethylene production capacity and theenergy consumption of various raw materials can be recognized to ad-just the proportion of raw materials in ethylene production plants, whichcan improve the energy efficiency and reduce raw materials waste.

ISaB07-3 16:30–16:50On Industrial Alarm Deadbands for Univariate Analog Signals

Wang, Zhen Shandong Univ. of Sci. and Tech.Wang, Jiandong Shandong Univ. of Sci. and Tech.Bai, Xingzhen Shandong Univ. of Sci. and Tech.Yang, Zijiang Shandong Univ. of Sci. and Tech.

Deadbands are widely used in industrial alarm systems to eliminatenuisance alarms. A deadband index is formulated to determine whichkinds of signals are suitable for using deadbands. A design methodis proposed to determine optimal alarm deadbands based on alarmdurations and maximum alarm deviations. Numerical and industrial ex-amples are provided to support the obtained results.

ISaB07-4 16:50–17:10A data driven method to detect first-out alarms based on alarm occur-rence events

Hu, Wenkai China Univ. of GeosciencesChen, Tongwen Univ. of Alberta

A first-out alarm is an alarm that comes first in a group of highly corre-lated alarms. To achieve alarm reduction, it is highly recommended inindustrial alarm management standards to only present first-out alarmsto operators, whereas consequential alarms should be silenced or sup-pressed using first-out display or logic alarm suppression in ANSI/ISA-18.2. However, to collect such groups of correlated alarms and identifyfirst-out alarms from them usually relies on proficient knowledge andexperience, and thus is time and resource intensive. This work pro-poses a data driven method to detect first-out alarms from historicalalarm data. Two main steps are involved: 1) consequential alarms aredetected for each unique alarm based on alarm occurrence events; 2)first-out rules are produced and pruned based on redundant alarm anal-ysis. The effectiveness of the proposed method is demonstrated by anindustrial case study involving alarm data collected from a real indus-trial facility. The detected first-out rules can be used as candidates todesign first-out display or logic alarm suppression strategies in practice.

ISaB07-5 17:10-17:30

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Optimal Test Sequencing Method with Unreliable Tests based on Quasi-depth First Search Algorithm

Liao, Xiaoyan Nanjing Univ. of Aeronautics and AstronauticsLi, Yang Nanjing Univ. of Aeronautics and AstronauticsLu, Ningyun Nanjing Univ. of Aeronautics and AstronauticsJiang, Bin Nanjing Univ. of Aeronautics and Astronautics

The test sequencing problem is optimized to reduce test cost and en-sure the performance of Fault detection and diagnosis (FDD), which isthe intersection of FDD and Design for Testability (DFT). Consideringthe false and missing alarm of complex system diagnosis, this paperproposed an optimal test sequence strategy for fault diagnosis underunreliable test based on the quasi-depth first search (QDFS) algorith-m. Firstly, the method established a heuristic evaluation function con-sidering fault diagnosis abilities, information entropy, test cost and thereliability of the test. Then, the quasi-depth first search algorithm isused to build the fault diagnosis strategy. The theory and experimentsdemonstrate that the cost based on the QDFS method is much betterthan greed algorithm. Therefore, the proposed method could be usedto design the strategy for fault diagnosis under unreliable test.

SaB08 15:50–17:10 #9 Bld Room新闻厅Invited Session: Fault Diagnosis Methods for High Speed Railway Sig-nal SystemsChair: Zhang, Jilie Southwest Jiaotong Univ.Chair: Wang, Zicheng China Railway Eryuan Engineering Group CO.

LTD

ISaB08-1 15:50-16:10Fault Diagnosis of Track Circuit Compensation Capacitor Based on G-WO Algorithm

Wang, Zicheng China Railway Eryuan Engineering Group CO. LTDYi, Lifu China Railway Eryuan Engineering Group CO. LTDYu, Kai China Railway Eryuan Engineering Group CO. LTDGu, Guoxiang Southwest Jiaotong Univ.Wang, Jianqiang China Railway Eryuan Engineering Group CO.

LTD

Track circuit (TC) is an important equipment in China Train Control Sys-tem (CTCS). But its failure rate has been high. The predictive mainte-nance mechanism of TC can further ensure the safe operation of train.While the fault diagnosis and realization of TC status is the premise toachieve prediction maintenance. With regards to this, a fault diagno-sis method for TC based on Grey Wolf Optimizer (GWO) algorithm isput forward in this paper. A Uniform Transmission Line (UTL) Model ofTC is established and the impact on the Locomotive Signal AmplitudeEnvelope (LSAE) by ballast resistance, compensation capacitor is ana-lyzed. The above parameters are chosen as the decision variables. Tominimum the difference between the real LSAE and the one calculatedusing the UTL model as the objective to form the fitness function. GWOalgorithm has the characteristic of insensitivity to initial solution values,higher optimization efficiency and global optimization ability and it isemployed to iteratively search for the optimum solution of TC parame-ters. Experiment results show that the method proposed in this papercan realize the diagnosis of important parameters of TC and it has highadaptability and excellent accuracy.

ISaB08-2 16:10-16:30Design of the Safety Control Logic for Railway Stations Based on PetriNets

Li, Yike Southwest Jiaotong Univ.Tong, Yin Southwest Jiaotong Univ.Guo, Jin Southwest Jiaotong Univ.

The control logic in the traditional interlocking depends on human’sexperience, lacking a unified generation and verification method. Inthis paper,we use Petri nets to model railway stations and then obtainthe mathematically optimal control logic to ensure safety and liveness.First, we use a modular method based on its devices including tracksegments, signals and switches. The interlocking condition is formulat-ed as a set of linear constraints. According to the supervisory controltheory, monitor places can be calculated to enforce the constraints. Fi-nally the control logic derived from the monitor place is proven maxi-mally permissive. Finally we take the simulation to investigate the per-formance of the control logic.

ISaB08-3 16:30-16:50Parameter Estimation and Fault Diagnosis for Compensation Capacita-

tors in ZPW-2000 Jointless Track CircuitYang, Wu-dong Southwest Jiaotong Univ.Zhang, Ji-lie Southwest Jiaotong Univ.Gu, Guoxiang Louisiana State Univ.

We propose a parameter estimation approach to fault diagnosis forjointless track circuits in railway transportation, focusing on the compen-sation capacitors. How to estimate various parameters of the jointlesstrack circuits poses a tremendous challenge, because the existing trackcircuits do not have sensor networks embedded to the railway network.Assuming the available special inspection train and the measurementdata, we analyze how various parameters of the jointless track circuitscan be estimated, and how faults in the compensation capacitors canbe detected. Our analysis results are illustrated by a numerical exam-ple.

ISaB08-4 16:50-17:10Research on Prediction of Time Between Failures for Onboard Subsys-tem of Train Control System

Yuan, Yahui Beijing Jiaotong Univ.Cai, Baigen Beijing Jiaotong Univ.Wei, Shangguan Beijing Jiaotong Univ.Shi, Xiyao Beijing Jiaotong Univ.

The prediction of Time Between Failures (TBF) is one of the key issuesof the fault prediction. There is little effective method to predict the TBFof onboard subsystem of train control system. In this paper, a combinedprediction model of TBF with high accuracy is proposed. Firstly, Singleprediction method including Echo State Network (ESN), Back Propa-gation(BP) neural network, Support Vector Machine (SVM) is used topredict the time between failures of onboard subsystem. In order to en-hance the performance of the prediction, a combined prediction modelis established on the basis of Seasonal-Trend decomposition procedurebased on Loess (STL). Finally, simulation is employed with the accura-cy of 96.49%. This study is helpful for the implementation of PreventiveMaintenance (PM) tasks.

SaB09 15:50–17:30 #9 Bld Room 202Invited Session: Interval Estimation for Fault DiagnosisChair: Zhu, Fanglai Tongji Univ.Co-Chair: Guo, Shenghui Suzhou Univ. of Scie. and Techn.Co-Chair: Wang, Zhenhua Harbin Institute of Tech.

ISaB09-1 15:50–16:10Sensor fault detection for linear systems by multiple 𝐻2/𝐻∞ observers

Zhang, Wenhan Harbin Institute of Tech.Wang, Zhenhua Harbin Institute of Tech.Shen, Yi Harbin Institute of Tech.

This paper studies sensor fault detection for linear discrete-time sys-tems by multiple 𝐻2/𝐻∞ observers approach. A limited number offault modes can be obtained by considering the system only occursone fault at a time. For each fault mode, we design an 𝐻2 / 𝐻∞ faultdetection observer with its residual is sensitive to fault and robust todisturbances and noises. Meanwhile, each fault detection observer isoptimized for the certain fault mode. Under the assumption that the dis-turbances and noises are unknown but bounded, zonotopic techniquesare used to achieve residual evaluation based on a bank of the de-signed observers. Finally, simulation results are provided to illustratethe effectiveness and superiority of the proposed method.

ISaB09-2 16:10–16:30Interval observer design for nonlinear switched systems

Che, Haochi Soochow Univ.Huang, Jun Soochow Univ.Ma, Xiang Soochow Univ.

The interval observers based on linear programming for nonlinearswitched systems with disturbances are developed in this study. Thenonlinearity is assumed to satisfy the property of Lispschitz, and thedisturbances are all bounded. The interval observers are constructedand multiple linear copositive Lyapunov function is used to analyze uni-formly ultimate boundness of the error systems. Finally, the efficiencyof the proposed method is illustrated by a numerical example.

ISaB09-3 16:30–16:50Actuator Fault Detection for Uncertain Systems Based on the IntervalOutput Observer*

Zhang, Xiangming Tongji Univ.Zhu, Fanglai Tongji Univ.

45


Shan, Yu Tongji Univ.Guo, Shenghui Suzhou Univ. of Scie. and Techn.

This paper investigates the problems of the residual-based actuatorfault detections based on observer designs for a class of uncertain lin-ear systems. First, a reduced-order observer is designed and it can es-timate the original system states asymptotically when the system doesnot suffer from actuator faults. Second, an interval observer, which isrobust to disturbances but sensitive to actuator faults in a sense of in-terval output estimation, is developed. Furthermore, a residual-basedactuator fault detection scheme is proposed. Finally, the validity of theproposed methods is verified by a simulation example.

ISaB09-4 16:50–17:10Interval State and Fault Estimation Based on Unknown Input Observerand Interval Hull Computation

Zhou, Meng Beijing Univ. of Chemical Tech.Cao, Zhengcai Beijing Univ. of Chemical Tech.Wang, Jing Beijing Univ. of Chemical Tech.Wang, Chang Beijing Aerospace Automation Research Institute

This paper proposes an interval state and sensor fault estimationmethod based on unknown input decoupling technique and interval hullcomputation approach. To facility the problem of fault estimation con-veniently, the sensor fault vector is first considered as an auxiliary s-tate, such that an augmented descriptor system is generated. Next,an unknown input observer is designed based on P-radius method forthe augmented descriptor system to decouple some of the unknownsystem disturbance. Then, the bounds of the augmented state vec-tor effected by the other unknown inputs that cannot be decoupled arecalculated via an interval hull approximation. By decoupling some ofunknown inputs to the estimation error system, the size of state es-timation set is reduced. Finally, numerical examples are provided toillustrate the effectiveness of the proposed method.

ISaB09-5 17:10–17:30On functional interval observers for discrete-time linear systems

Guo, Shenghui Suzhou Univ. of Sci. and Tech.Jiang, Bin Nanjing Univ. of Aeron. and Astron.

This paper presents a straightforward method of the functional intervalobservers design for discrete-time linear systems. First, a frameworkof the functional interval observer is constructed. Second, the suffi-cient conditions of the existence of such functional interval observersare given. Third, a straightforward calculate method of the functionalobserver$𝑟$ngain matrices is proposed. Furthermore, the same studyis also considered for the systems with output disturbances. Finally, anumerical simulation example shows the validity and$𝑟$neffectivenessof the proposed methods.

Poster Session PSaAJuly 6, 13:40–15:40

Poster Area (9号楼3层外厅)Chair: Lei, Yaguo Xi’an Jiaotong UniversityCo-Chair: Peng, Tao Central South Univ.Co-Chair: Ge, Zhiqiang Zhejiang Univ.

◁ PSaA-01Fault Diagnosis Method Based on GA-IBP Neural Network

Li, Bo Air Force Engineering Univ.Zhang, Lin Air Force Engineering Univ.Zhang, Bo Air Force Engineering Univ.Wang, WenFeng Air Force Engineering Univ.Hao, Zhewei Air Force Engineering Univ.

Aiming at the problems of slow convergence and low diagnostic accu-racy in back propagation (BP) neural network for fault diagnosis, thispaper designs an improved double-adaptive-BP neural network basedon error pointer, optimizes its initial parameters by genetic algorithm(GA) and establishes a fault diagnosis model of improved BP (GA-IBP)neural network based on GA optimization. It uses the IGBT fault data inthe inverter circuit as the training and test samples, and conducts sim-ulation analysis through MATLAB. The results show that the improvedalgorithm has obvious effects on improving network convergence speedand diagnostic accuracy.

◁ PSaA-02Penicillin Fermentation Process Fault Detection Based on Multi-rateSampling kNN

Li, Keqin Northeastern Univ.

Feng, Jian Northeastern Univ.

For the multi-rate sampling problem of the penicillin fermentation pro-cess, some traditional methods cannot solve due to the different lengthsof samples. To solve this problem, a multi-rate sampling fault detectionmethod based k-nearest neighbor is proposed. The training sample setis divided into multiple groups according to different sampling rates ofvariables. Calculate the distance between the samples of each groupseparately. The 𝑘 nearest neighbors distances of all samples are uni-fied to calculate the control limit. The PenSim2.0 software was usedto generate a sample set of penicillin fermentation process that con-tained multiple variables at different sampling rates. The simulationresult shows the effectiveness of the proposed method.

◁ PSaA-03Dynamic Laplacian eigenmaps for process monitoring

Zhang, Jingxin Tsinghua Univ.Chen, Maoyin Tsinghua Univ.Zhou, Donghua Shandong Univ. of Scie. and Techn.

This paper proposes a novel process monitoring method based on La-palacian eigenmaps. Aimed at the “out of sample” issue, we adop-t radial basis function neural network instead of the traditional lineartransformation, which is able to discover the accurate nonlinear func-tional relationship between the raw data and the low-dimensional data.Besides, in order to utilize temporal information, time-lagged embed-ding is employed to extract more meaningful information and dynamiccharacteristics. Thus, the proposed approach can be actually appliedto nonlinear dynamic systems. Eventually, a numerical case demon-strates the effectiveness of the proposed approach.

◁ PSaA-04Process fault detection based on skew Gaussian distribution transfor-mation and canonical variable analysis method

Guo, Xiaoping Shenyang Univ. of Chemical Techn.Gao, Jiajun Shenyang Univ. of Chemical Techn.Li, Yuan Shenyang Univ.of Chemical Techn.

When the process data have non-Gaussian distribution characteristics,the fault detection model based on the Gaussian distribution hypothe-sis method will cause high false alarm. For this problem, a novel faultdetection method, which based on skew Gaussian distribution transfor-mation and canonical variable analysis (SGDT-CVA), is proposed. Firstof all, the Gaussian distribution transformation function is adaptively de-termined by the skewness of the process data. After transformation, theprocess data is transformed into obedient or approximately obedientGaussian distribution. Then, CVA method is used to analyze the max-imum correlation between variables, and the statistics are constructedaccording to the relationship between variables.Finally, experiments onthe Tennessee Eastman process are used to illustrate the effectivenessof the proposed method and the results show the performance of theSGDT-CVA method is better than PCA, kNN and CVA.

◁ PSaA-05Fault Detection Based on Modified t-SNE

Liu, Decheng Tsinghua Univ.Guo, Tianxu Tsinghua Univ.Chen, Maoyin Tsinghua Univ.

Dimension reduction is a general step to process high dimensional da-ta for fault detection. Principal Component Analysis (PCA) divides dataspace into principal component space and residual space. But it is aglobal method without considering local geometric properties betweendata points. Concentrating on local structure of data, manifold learningcan be introduced in dynamic and continuous process for fault detec-tion. It can extract latent features of data, and also be viewed as non-linear dimension reduction. In this paper, we propose a modified t-SNEalgorithm for fault detection, simultaneously considering local structureand different scales of variables. Modified t-SNE converts Mahalanobisdistance to the conditional probability for representing pairwise similar-ities instead of Euclidean distance, which satisfies the characteristicsof industrial process data. A subspace can be obtained from high-dimension to low-dimension by applying modified t-SNE, which effec-tively preserves local structure. Simulation on Tennessee Eastman pro-cess (TEP) demonstrates the effectiveness of our proposed method.

◁ PSaA-06Detecting Oscillations via Adaptive Chirp Mode Decomposition

Chen, Qiming Zhejiang Univ.

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Lang, Xun Zhejiang Univ.Xie, Lei Zhejiang Univ.Su, Hongye Zhejiang Univ.

High performance is a fundamental requirement for the maintenance ofan industrial plant in face of increasing competitive pressure. Oscilla-tion is one of the most common abnormal phenomena encountered inprocess industries, thus it is of great importance to detect oscillation-s before implementing the performance-improvement methods. Thispaper proposed a novel method based on the adaptive chirp mode de-composition (ACMD) to detect the oscillations in control loops. Firstly,the process variable is decomposed by the ACMD into several chirpmodes, called as intrinsic mode functions (IMF). Then, the energy ra-tio, normalized correlation coefficient, consistency function and sparse-ness index are combined to identify oscillations contained in these IMF-s. The proposed method is automatic and data-driven without requiringany prior knowledge about the underlying process dynamics. Simula-tion studies demonstrated the detection ability of our approach in fivecases, i.e. normal condition, external disturbance, external disturbanceand poor tuning, stiction, stiction with external disturbance. Results ob-tained from this approach on industrial data show that it can be readilyimplemented in industrial environment.

◁ PSaA-07Online open-circuit fault detection and location utilizing estimated in-stantaneous amplitudes

Tao, Songbing Chongqing Univ.Chai, Yi Chongqing Univ.Liu, Bowen Chongqing Univ.Jiang, Congmei Chongqing Univ.Xu, Shuiqing Hefei Univ.of Tech.

As one of the most key parts of wind power generation systems (W-PGSs), the grid-connected converter plays a significant role for ener-gy conversion. It is meaningful to ensure the stability and safety ofconverters in WPGSs. In this paper, a novel signal processing basedapproach is proposed to detect and locate power switch failures in grid-connected converters of WPGSs, which is completely different from ex-isting current based approaches. Based on the proposed weighted s-liding Hilbert transform (WSHT) algorithm, instantaneous amplitudes ofthe three-phase output current can be estimated online without effectsof the boundary problem. At last, simulation results demonstrate theeffectiveness for open-circuit faults in the grid-connected converters.

◁ PSaA-08A process fault diagnosis method using multi-time-scale dynamic fea-ture extraction based on convolutional neural network

Gao, Xinrui Tsinghua Univ.Yang, Fan Tsinghua Univ.Feng, Enbo China National Chemical Corporation Ltd.

Unlike many other techniques used in process control, which are widelyapplied in practice and play significant roles, abnormal situation man-agement (ASM) still relies heavily on the human experience, not leastbecause the problem of fault detection and diagnosis (FDD) has notbeen well addressed. In this paper, a process fault diagnosis methodusing multi-timescale dynamic feature extraction based on convolution-al neural network (CNN) consisting of similarity measurement, variableranking, and multi-time-scale dynamic feature extraction is proposed.The CNN based model containing fixed multiple sampling (FMS) layercan extract dynamic characteristics of process data at different time s-cales. The benchmark Tennessee Eastman (TE) process is utilized toillustrate the performance of the fault diagnosis method.

◁ PSaA-09Dynamic Processes Modeling and Monitoring based on a Novel Dy-namic Latent Variable Model

Zhou, Le Zhejiang Univ. of Scie. and Techn.Ge, Zhiqiang Zhejiang Univ.Song, Zhihuan Zhejiang Univ.Qin, Sizhao Univ. of Southern California

For dynamic process modeling and monitoring purpose, it is desirableto extract both the auto-correlations and the cross-correlations in mea-surements. Besides, the proposed dynamic model is also expected toprovide an accurate prediction, visualization and an explicit interpreta-tion of the data structures. From this perspective, the dynamic latentvariable model is more suitable compared to the traditional dynamic

principal component analysis (DPCA). In this paper, a novel indepen-dent dynamic latent variable model is proposed to explicitly extract sev-eral independent dynamic latent variables with which to capture pro-cess dynamics in the measurements. The proposed model is derivedin the probabilistic framework and the model parameters are estimatedvia the expectation-maximum algorithm. Finally, a case study is illus-trated to evaluate the performance of the proposed method for dynamicmodeling and process monitoring.

◁ PSaA-10A Novel Recursive Data-Driven Realization of SIR in Closed-loop Sys-tem

Liu, Tianyu Harbin Institute of Techn.Luo, Hao Harbin Institute of Techn.Wang, Xuejiao Harbin Institute of Techn.Yin, Shen Harbin Institute of Techn.Okyay Kaynak Harbin Institute of Technology

Modern factories are facing severe challenges from production safetyto dealing tons of process data. Aiming to provide powerful tools forsystem analysis and design through data-driven techniques, this paperproposed a novel recursive data-driven Stable Image Representation(SIR) identification method. By controlling the rows future and pastvectors, the algorithm given in this paper can provide adjustable iden-tification precision with relatively low computational load. The correct-ness of this paper is demonstrated by a DC Motor benchmark system.The work in this paper is essential to the future research on data-drivenrecursive realization of stability margin and gap metric.

◁ PSaA-11Fault Detection of Actuators via Extended State Observer

Hou, Yanze China Acad.of Space Techn.Zhang, Minjie China Acad. of Space Techn.Yang, Lei China Acad.of Space Techn.

This paper focuses on the issue of fault detection of actuators. The pro-posed fault detection scheme consists of the nominal actuator dynamic-s and the corresponding modified extended state observer. Simulationof typical autopilot of flight vehicles shows that the proposed methodcan identify actuator performance degradation from hierarchical distur-bances.

◁ PSaA-12Dust Deposition Diagnosis of Photovoltaic Modules Using Similarity-Based Modeling (SBM) Approach

Wang, Zhonghao Zhejiang Univ.Xu, Zhengguo Zhejiang Univ.

This paper focuses on the photovoltaic (PV) system and studies thedust depositing diagnosis of the PV modules. An unsupervised data-driven method called Similarity-Based Modeling (SBM) was used todeal with this problem and some improvements of this method wereadopted. SBM is a nonparametric empirical modeling technology thatuses pattern recognition from historical data to generate estimates ofthe current values of each variable in a set of modeled data sources.The motivation to use SBM is that the mainstream approaches now,contrast experiments approaches and theoretical formulas approach-es have many disadvantages. Contrast experiments are complicatedand need experiment systems with high-cost. Theoretical formulas ap-proaches are not accurate enough. SBM overcomes them to someextent. Numerical experiments are also studied to testify that the pro-posed method has good performance in the application.

◁ PSaA-13Remaining useful life prediction under multiple fault patterns for degra-dation processes with long-range dependence

Zhang, Hanwen Zhejiang Univ.Yang, Chunjie Zhejiang Univ.Sun, Youxian Zhejiang Univ.

Many different faults many occur during the operation of practical indus-trial devices or systems. Under different failure patterns, the degrada-tion process will inevitably have different statistical characteristics anddirections. Therefore, it is necessary to distinguish between differentfault patterns when predict the RUL. In this paper, a novel frameworkof RUL prediction under multiple fault patterns is proposed. Consid-ering the practical industrial situation, we suppose the degradation isobserved by multiple sensors, and has long-range dependence. To i-dentify the fault pattern, an SVM-based classifier is constructed using

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the historical labeled data. To fuse multiple sensors information, themulti-dimensional observation data are projected to a one-dimensionaldegradation index. The projection vector is obtained by optimizing thedegradation index performance. Then the RUL can be predicted usingan FBM-based model. The effectiveness of the proposed method isdemonstrated by a case study of turbofan engines.

◁ PSaA-14A Novel Redundant Information Elimination Aided Classification Ap-proach for Cervical Cancer Diagnosis

Peng, Hu Harbin Institute of Techn.Jiang, Yuchen Harbin Institute of Techn.Li, Xiang Harbin Institute of Techn.Luo, Hao Harbin Institute of Techn.Yin, Shen Harbin Institute of Techn.

Cervical cancer is one of the most dangerous diseases among femalediseases. Because its clinical manifestation is not obvious, existingdiagnostic approaches rely more on regular examination and timelydetection of lesions. In this paper, the application of Support VectorMachine (SVM) classifier for classification and diagnosis is firstly intro-duced, Then a modified approach combining classifier model with par-tial least squares (PLS) regression is proposed, which can achieve fur-ther recognition and diagnosis after eliminating redundant information,so as to obtain a more stable and rapid diagnostic model for screen-ing people at risk of disease. The improved approach and the princi-pal component analysis-support vector machine (PCA-SVM) approachproposed by other researchers are applied to diagnose medical data. Inthis paper, the effectiveness of the two approaches is verified by simula-tion tests on cervical cancer data. Compared with existing approaches,results show that the proposed approach has better performance thanPCA-SVM in the diagnosis of cervical cancer, and it achieves high clas-sification accuracy by using fewer features.

◁ PSaA-15Fault Prediction Method of the On-board Equipment of Train ControlSystem Based on Grey-ENN

Meng, Yueyue Beijing Jiaotong Univ.Shangguan Wei Beijing Jiaotong Univ.Cai, Bai-gen Beijing Jiaotong Univ.Zhang, Junzheng Beijing Jiaotong Univ.

On-board equipment is the core component of Train Control System. Itis of great significance to perform the fault prediction of on-board equip-ment in order to improve the safety of the train. This paper proposesa fault prediction method based on Grey-Elman neural network(Grey-ENN) for 300T on-board equipment. Firstly, through the statistics andanalysis of the AE-log data of on-board equipment, the operation statesevaluation and division have been completed. Secondly, the GSM-SVM(Support Vector Machine is optimized by Grid Search Method) modelhas been used to recognize operation states, followed by verifying thevalidity of the equivalent failure rate. The experiment results show thatthe fault states can be distinguished based on GSM-SVM with the accu-racy of 93.4%. Finally, a joint fault prediction model has been employedto accomplish the complete prediction of serious and emergency faultswith overall prediction accuracy of 86%, which verifies the feasibility andeffectiveness of the Grey-ENN prediction method, and fault predictionresult has certain guiding significance for maintenance decision.

◁ PSaA-16Fault Diagnosis of ROV Propeller Based on VMD and AR

Ren, Feng Shanghai Institute of Techn.Ye, Yinzhong Shanghai Urban Construction Vocational CollgeMa, Xiang-hua Shanghai Institute Of Techn.

Propeller fault diagnosis is of great significance to the maintenanceof power system of Remote Operated Vehicle (ROV). In this paper, amethod of combining variational mode decomposition (VMD) with ARspectral analysis is proposed. Firstly, the VMD decomposition of thevibration signal of the propeller is carried out, and the multi-componentdecomposed is analyzed by AR spectrum. The VMD-AR spectrum andthe accumulated energy of VMD-AR spectrum of the vibration signalare obtained. Based on the comparative analysis of two kinds of Atlasof propeller normal condition and fault condition, the fault diagnosis ofpropeller can be realized finally. The effectiveness of this method isproved by concrete experiments.

◁ PSaA-17Design of Guidance and Control System for Hypersonic Morphing Ve-

hicle in Dive PhaseBao, Cunyu National Univ. of Defense Techn.Wang, Peng National Univ. of Defense Techn.Tang, Guojian National Univ. of Defense Techn.

This paper studies the design of guidance and control system for hy-personic morphing vehicle in the dive phase. The morphing mode ofhypersonic morphing vehicle is designed. Based on the relative line-of-sight equations, a control-oriented guidance and control model ofhypersonic morphing vehicle is designed in the dive phase. In orderto realize the flight mission of the vehicle in the dive phase, the guid-ance and control method of the hypersonic morphing vehicle in the divephase is designed by using the block dynamic surface method and thelift is controlled by using the morphing characteristics of the vehicle.Through numerical simulation analysis, the effectiveness of the inte-grated method of guidance and control in the dive phase is verified andthe flight mission requirements of hypersonic morphing vehicle in thedive phase are well fulfilled.

◁ PSaA-18Sensor Fault Detection and Isolation in Toolface Control of Rotary S-teerable Drilling System

Niu, Yichun China Univ. of PetroleumSheng, Li China Univ. of PetroleumWang, Weiliang China Univ.of PetroleumGeng, Yanfeng China Univ. of PetroleumZhou, Donghua Shandong Univ. of Scien. and Techn.

This paper is concerned with the problem of sensor fault detection andisolation in toolface control of dynamic point-the-bit rotary steerable sys-tem (DPRSS). Considering the dynamic characteristics of DPRSS, themathematical model of triple closed-loop toolface control system is es-tablished. Depending on the statistical method, two sets of hypothe-sis tests are designed and the aims of fault detection and isolation forthe accelerometer, the gyro in stabilized platform and the gyro in outerhousing are both achieved. Finally, the feasibility and effectiveness ofthe proposed method are validated by simulations.

Poster Session PSaBJuly 6, 13:40–15:40

Poster Area (9号楼2层外厅)Chair: Zeng, Jianping Xiamen Univ.Co-Chair: Gao, Ming China Univ. of Petroleum (Huadong)Co-Chair: Luo, Delin Xiamen Univ.

◁ PSaB-01Remaining Useful Life Prediction for a Fractional Degradation Processwith Non-stationary Increments

Xi, Xiaopeng Tsinghua Univ.Chen, Maoyin Tsinghua Univ.Zhou, Donghua Shandong Univ. of Science and Technology

Predicting the remaining useful life (RUL) is a central part of the prog-nostics and health management (PHM) technology with regard to vari-ous kinds of commercial plants, for the reason that accurate RUL pre-diction results can lay a solid theoretical foundation for the maintenancework. Subject to complicated environmental factors, the degradationprocesses of some sophisticated devices like large blast furnaces maypresent nonlinear, non-Markovian, and non-stationary characteristics,which cannot be well described by most of the traditional stochasticprocess models. To solve this problem, we mainly establish a nov-el degradation model combining both the arc tangent function and thesub-fractional Brownian motion (sub-FBM). Sub-FBM is attached to aclass of typical fractional diffusion processes with non-stationary incre-ments, and corresponds to more moderate memory effects. Unknownmodel parameters are sequentially estimated by the detrending movingaverage (DMA) method and a maximum likelihood (ML) algorithm. Theclosed-form RUL distribution is further obtained based on a compre-hensible weak convergence transformation. A numerical example fullydemonstrates the advancement of the proposed prognostics scheme.

◁ PSaB-02Detection of Incipient Leakage Fault in EMU Braking System

Sang, Jianxue Tsinghua Univ.Zhang, Junfeng Tsinghua Univ.Guo, Tianxu Tsinghua Univ.Tai, Xiuhua CRRC Qingdao Sifang Rolling Stock Research

Institute Co.

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Chen, Maoyin Tsinghua Univ.Zhou, Donghua Shandong Univ. of Science and Technology

The safety of braking system is crucial to guarantee the normal opera-tion of electric multiple units (EMU). As an important device of brakingsystem, the brake cylinder is required to provide accurate brake pres-sure, which is the key parameter that directly affects the braking perfor-mance. Currently, the KNORR-based fault detection strategy is utilizedto monitor each brake cylinder separately. This strategy is effective forgeneral faults, but ineffective for incipient leakage faults. In this paper, anovel detection method is proposed for incipient leakage fault detection,and the method can monitor multiple brake cylinders simultaneously.First, variables with high level of consistency are selected by analyzingthe characteristics of the real data from braking system. Then a speciallinear data transformation is carried out to obtain approximate station-ary properties. Finally a new detection statistic is proposed based onthe infinite norm of the transformed data. Experimental studies are car-ried out on the EMU brake test bench of Qingdao Sifang Rolling StockResearch Institute Co., Ltd. to demonstrate the effectiveness of theproposed fault detection method.

◁ PSaB-03An Improved Random Forest Algorithm of Fault Diagnosis for RotatingMachinery

Wang, Zilan Shandong Univ. of Sci. and Tech.Zhong, Maiying Shandong Univ. of Sci. and Tech.Liu, Yang Shandong Univ. of Sci. and Tech.

In this paper, an improved semi-supervised random forest (RF) algo-rithm is presented for fault diagnosis of rotating machinery. Firstly, alarge number of unlabeled samples are divided into two parts, denot-ed respectively as unlabeled sample I and unlabeled sample II. Thena graph-all the labeled samples are used to train the multiple decisiontrees. If the classification result is consistent with the one of label pre-diction, then the unlabeled sample I is added to the labeled samplesand used for building RF model. While, the data of unlabeled sample IIare utilized for testing of the obtained RF model. Finally, the developedRF algorithm is applied to an experimental platform of rotating machin-ery. It is shown from the simulation results that, for the case of samplesbeing noise pollution and with unsatisfying labels, the new developedsemi-supervised RF algorithm can improve the fault classification ac-curacy than the traditional RF.

◁ PSaB-04Transient Fault Diagnosis of Track Circuit Based on MFCC-DTW

Yang, Jing Southwest Jiaotong Univ.Wang, Xiaomin Southwest Jiaotong Univ.Zhang, Wenfeng Southwest Jiaotong Univ.Zheng, Qiming Southwest Jiaotong Univ.Song, Ci Southwest Jiaotong Univ.

With the rapid development of high-speed railway at home and abroad,the sudden failure of track circuit will seriously affect the safety andtransportation efficiency. In this paper, a fault diagnosis method of trackcircuit is proposed based on Meier frequency coefficient and dynamictime regulation model. The fault state of track circuit equipment is ana-lyzed by using transient theory, and the state of track circuit equipmentis classified into multiple states. Mayer frequency coefficient and princi-pal component analysis are used to extract features. K-means cluster-ing is used to construct template libraries for different faults. The match-ing distance between test data and template libraries is compared byDTW model to diagnose faults. Using the measured transient voltagedata of track circuit, the performance of the model is tested, and therealization and validation of fault diagnosis are completed. The resultsshow that compared with other machine learning methods, the MFCC-DTW algorithm improves the diagnosis time greatly, and the correct rateis more than 90 %. The method classifies the transient state of track cir-cuit and provides an economical and feasible solution for the real-timediagnosis of multiple faults in centralized monitoring system.

◁ PSaB-05Fault diagnosis for the planetary gearbox based on a hybrid dimensionreduction algorithm

Li, Ran Shandong Univ. of Science and TechnologyLiu, Yang Shandong Univ. of Science and Technology

A hybrid dimension reduction algorithm based on feature selection andkernel principal component analysis (KPCA) is proposed in this paper

to better realize the classification of the planetary gearbox faults. First-ly, in order to reduce the redundancy of some unnecessary features inthe sample to a greater extent and the complexity of the kernel matrixcalculation, a multi-criterion fusion feature selection method is used toeliminate the irrelevant features. Secondly, through KPCA, the nonlin-ear principal component of the selected features is produced. Then,fault is recognized by put the feature subset into the SVM classification.The proposed algorithm is applied to a planetary gearbox fault diagno-sis experiment, and the experimental results show that the proposedalgorithm outperforms the ones which use feature selection or KPCAseparately.

◁ PSaB-06Mixture Probabilistic Linear Discriminant Analyzer for Process FaultClassification

Yi, Liu Zhejiang Univ.Zeng, Jiusun China Jiliang Univ.Xie, Lei Zhejiang Univ.Lang, Xun Zhejiang Univ..Luo, Shihua Jiangxi Univ. of Finance and EconomicsSu, Hongye Zhejiang Univ.

Fault misclassification results in misleading locations of root faults,which brings economic loss and safety concerns. In order to enhancethe performance of fault classification, this paper proposes a novel mix-ture fault classifier based on probabilistic linear discriminant analyzer(PLDA). Unlike the probabilistic model with a single latent variable,more useful information can be extracted by PLDA using within- andbetween-class latent variables. With the introduced state and mixturevariables, the proposed mixture PLDA (MPLDA) classifier assigns atest sample to different components for each class in probability. Forthe class label decision, a robust state inference strategy is develope-d, which includes investigating the effect of the shared between-classvariable on conditional probability and adopting the voting scheme tocollect evidences from training samples to identify the class source ofthe test sample. After classifier construction, the model parameters areobtained by the expectation-maximization (EM) algorithm. The valid-ity of MPLDA classifier in fault classification is then illustrated by theapplication of the Tennessee Eastman (TE) process.

◁ PSaB-07Intermediate Observer-based Fault Estimation for Nonlinear Systemwith Input Disturbances

Wang, Yuan Northeastern Univ.Wang, Zhanshan Northeastern Univ.

In this paper, the intermediate observer-based fault estimation prob-lem is investigated for nonlinear system with the actuator faults, sensorfaults and input disturbances. First, in order to facilitate handling thesensor faults, the system is transformed into augmented form. Sec-ond, the intermediate observer is utilized to estimate the states, faultsand input disturbances simultaneously, which overcomes the constraintof observer matching condition. The estimation of input disturbancesis introduced to improve the accuracy of fault estimation. Finally, bymeans of Lyapunov stability theory, it is proved that the estimation er-rors are uniformly ultimately bounded. Simulation results are given tovalidate the effectiveness of the proposed approach.

◁ PSaB-08Learning observer based fault diagnosis and fault tolerant control formanipulators with sensor fault

Wu, Wei Zhengzhou Univ.Yao, Lina Zhengzhou Univ.Kang, Yunfeng Zhengzhou Univ.

In this paper, a learning observer based manipulators sensor fault di-agnosis scheme is proposed. The dynamic model of the manipulatoris taken as the research object and the effects of the disturbance isconsidered. When the fault occurs in the sensor, a learning observer isdesigned to obtain the fault information. Correspondingly the stabilityanalysis of the observation error system is carried out using Lyapunovstability theorem. Then, a sliding mode fault tolerant controller is de-signed to make the manipulator can track the desired trajectory. Finally,a simulation example is given to prove the effectiveness of the algorith-m.

◁ PSaB-09Intelligent Fault Diagnosis Method for Coupling Rotating MachineryBased on Deep Convolutional Neural Network

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Mu, Dawei China Univ. of Petroleum (East China)Sheng, Li China Univ. of Petroleum (East China)

In the industrial process, coupling faults between different componentstend to continually happen due to the complications of mechanical e-quipments. Compared to the single fault, coupling faults have complexcharacteristics and features, especially when they have the similar pat-terns. In this paper, a fault diagnosis method of rotating machinery isproposed to diagnose coupling machinery vibration signals based ondeep convolutional neural network. The proposed algorithm coalescesboth feature extraction and pattern classification into a single and com-pact learning network which can load raw signals without pretreatment.Through the wind turbine drivetrain diagnostics simulator (WTDS) plat-form, vibration signals are collected and the effectiveness of the pro-posed method is validated. The results demonstrate that the detectionaccuracy of the proposed method is higher than other intelligent faultdiagnosis algorithms.

◁ PSaB-10Fault Isolation Via Multiple-model Estimation for Traction Inverter withIGBT Open Circuit Fault

Yan, Yu Nanjing Univ. of Aeronautics and AstronauticsJiang, Bin Nanjing Univ. of Aeronautics and AstronauticsMao, Zehui Nanjing Univ. of Aeronautics and AstronauticsZhu, Hongyu Nanjing Engineering Institute of Aircraft SystemsLI, Han CSSC Systems Engineering Research Institute,

Oceanic Intelligent Technology Innovation Center

In this paper, a fault isolation method based on multiple-model esti-mation is proposed for IGBTs (Insulated Gate Bipolar Transistor) opencircuit faults in the inverters of the high-speed train traction systems.Considering that an IGBT has three operating modes and there are12 IGBTs in a three-level inverter, the multiple-model is introduced todescribe the dynamics of inverters. Due to the redundant design char-acteristics of the inverter sensors in high-speed trains, sensors datafusion method is employed to estimate the system states. Combinedwith the faulty multiple-model, the fault isolation scheme is constructedto determine which IGBT has a fault. The simulation results show theeffectiveness of the fault isolation algorithm

◁ PSaB-11Fault Diagnosis for Stator Inter-turn Short Circuit Fault of Traction Mo-tors under Closed-loop Structure

Wu, Xiao Nanjing Univ. of Aeronautics and AstronauticsZhang, Yufeng CSSC Systems Engineering Research Institute,

Oceanic Intelligent Technology Innovation CenterMao, Zehui Nanjing Univ. of Aeronautics and AstronauticsJiang, Bin Nanjing Univ. of Aeronautics and AstronauticsShi, Yongqian Nanjing Univ. of Aeronautics and Astronautics

In this paper, the fault diagnosis problem is studied for the stator inter-turn short circuit fault in the traction motor of high-speed trains underclosed-loop structure. Considering the robustness of the closed-loopsystem induces that the stator inter-turn short circuit fault could be cov-ered, a new mathematic model of transfer function is established fromthe reference input to the output. Then a fault diagnosis scheme, whichcontains a residual for detecting faults and a multi-model matching al-gorithm for isolating faults, is proposed. Simulations verify the perfor-mance of the fault diagnosis scheme. Through the stability analysis, thefault detectability condition for the closed-loop traction motor system isdriven, which is also illustrated by simulations.

◁ PSaB-12Fault Diagnosis of Aero-engine Gas Path Base on PSO-SVM

Chi, Jin Xiamen Univ.Liu, Yuanfang Xiamen Univ.Luo, Delin Xiamen Univ.Cao, Langcai Xiamen Univ.

Aiming at the high incidence of faults and high maintenance cost ofaero-engine gas path components, this paper adopts the condition-based maintenance mode and introduces the fault diagnosis methodcombining particle swarm optimization (PSO)with support vector ma-chine(SVM).Firstly, the fault diagnosis method of aero-engine gas pathbased on SVM is proposed. Then, kernel function parameters andpenalty coefficients of SVM are optimized by PSO. Finally, the aero-engine gas path fault diagnosis model based on PSO-SVM is estab-lished. The simulation results show that the design method of fault di-

agnosis of aero-engine Gas path based on PSO-SVM has advantagesof short prediction time, high prediction efficiency and higher accuracythan the traditional diagnosis method based on SVM, reached 100%.Moreover, the effect of PSO on kernel function parameters and penaltycoefficients is better than that of other optimization algorithms.

◁ PSaB-13Research on the safety assessment of heavy trucks in transit based onthe Information entropy-based weighting and bi-level Belief Rule Base

Li, Gailing Army Military Transportation Univ.Zhou, Zhijie Rocket Force univ. of EngineeringHu, Changhua Rocket Force univ. of EngineeringHu, Guanyu Hainan Normal Univ.He, Wei Harbin Normal Unive.

Heavy truck have the characteristics of super long, super wide, superhigh and overweight, which are prone to traffic accidents and resultin heavy losses. This paper aims to detect various safety hazards andachieve the safety assessment of heavy vehicles in transit by monitoringstate parameters. A safety assessment model which combines the in-formation entropy-based weighting method (IEBW) and the bi-level Be-lief Rule Base (BRB) are proposed in this paper for the safety assess-ment of heavy trucks. Firstly, the information entropy-based weightingmethod is used to select key features which can significantly representthe actual safety status of heavy trucks. Secondly, the bi-level BRB isused to accurately and timely reflect the complicated relationships be-tween features parameters and safety status of heavy trucks. At thesame time, the bi-level BRB can greatly reduce the number of BRBrules which makes it a possible to construct a multi-attribute BRB moreconveniently. The proposed model is applied to the safety assessmentof heavy trucks. Experimental results show that the proposed modelcan fuse subjective and uncertain information, has the characteristicsof fast evaluation, high accuracy and closer to reality

◁ PSaB-14A comparison of OCMPM and OCSVM in motor and sensor fault detec-tion for traction control system

Chen, Zhiwen Central South Univ.Chen, Zhuo Central South Univ.Peng, Tao Central South Univ.Liang, Ketian Central South Univ.Yang, Chunhua Beijing Univ. of Sci. and Tech.

Fault detection is critical to ensure the safe operation of high speedtrains. One class support vector machine (OCSVM) and one class min-imax probability machine (OCMPM) are two domain-based single classclassification methods commonly used in fault detection. This papersystematically analyzes their training and detecting complexity, princi-ple of optimization, and hyperparameter influence of these two methodsand compares their performance on motor and sensor fault data fromthe simulated traction control system of the high speed train. It showsthat OCMPM achieves higher false positive rate than OCSVM given thesame false negative rate. But OCMPM is unfeasible used for real-timefault detection when the training dataset is large.

◁ PSaB-15Comparison of Several Data-driven Models for Remaining Useful LifePrediction

Chen, Zhiwen Central South Univ.Liang, Ketian Central South Univ.Yang, Chao Central South Univ.Peng, Tao Central South Univ.Chen, Zhuo Central South Univ.Yang, Chunhua Central South Univ.

At present, there are many data-driven models for remaining useful life(RUL) prediction, but few literature compare the advantages and disad-vantages of those models. Understanding the characteristics of differ-ent models is one of the key steps in model selection. Therefore, sixtypical data-driven models for remaining useful life prediction are cho-sen for experimental comparison. These models are linear regression(LR), support vector regression (SVR), random forest (RF), gradien-t boosting decision tree (GBDT), convolutional neural network (CNN)and recurrent neural network (RNN). A characteristics analysis is con-ducted to provide an intuitive result for model comparison. Then, thesuggestion of model selection is given based on existing data and taskrequirement.

◁ PSaB-16

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Optimized Neural Network by Genetic Algorithm and Its Application inFault Diagnosis of Three-level Inverter

Chen, Danjiang Zhejiang Wanli Univ.Liu, Yutian Zhejiang Wanli Univ.Zhou, Junwei State Grid Hangzhou Xiaoshan Power Supply

Company

Multilevel inverters have been widely applied in high-voltage and high-power applications. Therefore, fault diagnosis of such circuits is be-coming more and more important. Fault diagnosis for single deviceopen-circuit fault of three-level inverter based on BP (back propaga-tion) neural network is studied in this paper. One of the weak-points ofBP algorithm which is commonly used is that the optimal procedure iseasily stacked into the local minimal value and cause strict demands ofinitial value. So a fault diagnosis method based on BP neural networkand genetic algorithm (GA) is proposed in this paper. Firstly, bridgevoltage of three-level inverter is collected as fault signal and feature isextracted to determine the structure of the BP neural network. Afterthis, GA is applied to optimize the initial weights and thresholds of BPneural network, and then the network is trained to diagnose faults ofthree-level inverter to determine the specific failure device. The simu-lation result shows that the method can isolate fault modes proposedexactly, and the weak-point of network can effectively avoid, improvethe diagnostic accuracy.

◁ PSaB-17A Non-Greedy Algorithm Based L1-Norm LDA Method for Fault Detec-tion

Wang, Min Tsinghua Univ.Chen, Maoyin Tsinghua Univ.Zhang, Jingxin Tsinghua Univ.Zhou, Donghua Tsinghua Univ.

L1-norm based linear discriminant analysis (L1-LDA) has attractedmuch attention. Compared with conventional L2-norm based linear dis-criminant analysis (L2-LDA) whose nobjective function is based on thedistance criterion, L1-LDA has better robustness against outliers andnoise. However, most existing approaches about L1-LDA solve the op-timal projection matrix one by one with greedy strategy, which learn aset of local optimal projection vectors by maximizing the ratio of the L1-norm-based between-class dispersion and the L1-normbased within-class dispersion. It cannot be guaranteed that the obtained optimalprojection matrix is globally optimal. In order to simultaneously considerthe robustness of the anomaly data and the global optimal solution, thispaper proposes a non-greedy algorithm based L1-norm LDA methodfor fault detection. And the effectiveness of the proposed method is il-lustrated by a numerical example and the Tennessee Eastman process.

◁ PSaB-18Fault Tolerant Control for High-Order Multi-agent Systems with Switch-ing Interaction Topologies

Wang, Qing Zhejiang Wanli Univ.Liu, Yu’ang Zhejiang Wanli Univ.Zhong, Kewei State Grid Hangzhou Xiaoshan Power Supply

CompanyDong, Chaoyang Beihang UniversityHou, Yanze CAST

Fault tolerant control (FTC) for high-order multi-agent systems (MAS)with switching interaction topologies are investigated via active distur-bance rejection control (ADRC) approach. The influence of the dynam-ics uncertainties and the actuator fault functions are estimated by thedesigned extended state observer (ESO), and compensated in the de-signed control strategy real time. The closed-loop system is proved tobe stable despite the actuator fault. The algorithm for the chosen of theparameters is also given. The simulation results demonstrate the effec-tiveness of the designed controller, the MAS can achieve the predefinedswitching time-varying formation.

◁ PSaB-19Research on Fault Diagnosis of Three Degrees of Freedom GyroscopeRedundant System

Shi, Haoqiang Xi’an Univ. of Tech.Hu, Shaolin Xi’an Univ. of Tech.Zhang, Jiaxu Xi’an Univ. of Tech.

As the core component of the navigation system, the gyroscope direct-ly affects the navigation performance of the system. In this paper, the

three-degree-of-freedom gyroscope is used as the object, firstly, thethree gyroscope redundant configuration forms are studied, and thecombined gyroscope data simulation platform is built by using the UAVand the gyroscope sensor, and simulate possible failures by interferingwith a gyroscope during flight tests. Secondly, the faulty gyro detectionand identification are carried out by using the measured redundant in-formation. At the same time, the gyroscope fault detection algorithmwith redundant configuration is proposed, the accuracy and feasibilityof the method are verified by the measured data of the gyroscope. Thesimulation shows that the method can accurately detect the gyroscopefailure, improve the data utilization of the gyroscope, and increase thereliability of the navigation system.

◁ PSaB-20Fault Diagnosis and Tolerant Control for Sensors of PWM Rectifier Un-der High Switching Frequency

Gong, Zifeng Southwest Jiaotong Univ.Huang, Deqing Southwest Jiaotong Univ.Qin, Na Southwest Jiaotong Univ.Ma, Lei Southwest Jiaotong Univ.

This paper considers the digital realization of fault diagnosis (FD) andfault tolerant control (FTC) method for both catenary current and DC-link voltage sensor of the PWM rectifier under high switching frequency.A novel state observer is designed, which plays a key role in the FD andFTC algorithms and facilitates their digital implementation. The FD al-gorithm consists of residual calculation and comparison between resid-uals and the corresponding thresholds. The FTC technique is realizedby control system reconfiguration that replaces the wrong measuredvalue with output of the state observer. Results of simulation experi-ments confirm the feasibility and superiority of the proposed method.

◁ PSaB-21Understanding the Fault in EMU Braking System

Tai, Xiuhua CRRC Qingdao Sifang Rolling Stock ResearchInstitute Co.

Guo, Tianxu Tsinghua Univ.Chen, Maoyin Tsinghua Univ.Zhang, Junfeng Tsinghua Univ.Zhou Donghua Tsinghua Univ.

The electric multiple unit (EMU) has become one of the most importantcomponents in strategic transportation in China. The braking systemshould receive special attention from both academic and practical per-spectives. Understanding the fault of EMU braking system is one ofthe key points before doing research on fault detection. In this paper,we give a very brief introduction of the electro-pneumatic brake controlstructure and the network topology of EMU fault detection and diag-nosis(FDD) module. Finally, a PCA based fault detection algorithm isproposed and the efficiency of the algorithm is verified through the ex-periment operated on a certain model of EMU braking system.

◁ PSaB-22Fault Detection and Estimation of Multi-Agent Systems: Neighborhood-Observer-Based Approach

Gong, Jianye Yangzhou Univ.Fu, Qilong Yangzhou Univ.Zheng, Xiaoxiao Yangzhou Univ.Sun, Xun Yangzhou Univ.Jiang, Bin Nanjing Univ. of Aero. and Astr.Shen, Qikun Yangzhou Univ.

In this paper, the adaptive fault detection and estimation problems areinvestigated for a class of linear multiagent systems with sensor fault-s, and adaptive neighborhood observer-based approaches are respec-tively proposed to detect and estimate the faults. Based on graph andLyapunov stability theory, it is proved that the detection and estimationerrors converge to an adjustable small neighborhood of the origin withall signals in the closed-loop system being bounded. Finally, simulationresults demonstrate the effectiveness of the approach proposed in thispaper.

◁ PSaB-23Batch Process Fault Diagnosis Based on The Combination of Deep Be-lief Network and Long Short-Term Memory Network

Liu, Fan Hangzhou Dian Zi Univ.Wang, Peiliang Huzhou Univ.Cai, Zhiduan Huzhou Univ.Zhou, Zhe Huzhou Univ.

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Wang, Yanfeng Huzhou Univ.Yang, Zeyu Zhejiang Univ.

With the rapid development of deep learning in recent years, moreand more deep architecture models have been used for batch pro-cess fault diagnosis. Deep Belief Network (DBN) has advantages inextracting features and processing high-dimensional, non-linear data,but the relevance of time series is not fully considered in training withtime-dependent signals. The batch process has the characteristicsof non-linearity, multiple working conditions and multiple time periods.Hence, DBN does not perform well in batch process. For this purpose, amethod based on the combination of Long Short-Term Memory (LSTM)network and Deep Belief Network (DBN) is proposed. The method firstadopts the preprocessing method of variable expansion and continu-ous sampling, and then uses DBN-LSTM network for feature extraction,time correlation analysis, and fault diagnosis. This method is applied toa class of semiconductor etching process. The experimental result-s show that the proposed method can effectively extract time-orderednonlinear fault features from the original batch process data and hashigh fault diagnosis accuracy.

◁ PSaB-24Safe Reconfigurability of a Class of Nonlinear Interconnected Systems

An, Zixi Nanjing Univ. of Aeronautics and AstronauticsYang, Hao Nanjing Univ. of Aeronautics and AstronauticsJiang, Bin Nanjing Univ. of Aeronautics and Astronautics

In this paper, based on the small-gain theorem of large-scale intercon-nected systems, we study the convergence performance of nonlinearinterconnected systems with cycles, and establish a safely reconfig-urable condition for the control law of each subsystem, which is appliedto design fault-tolerant control (FTC) strategies. Both individual andcooperative FTC methods are presented in this paper by redesigningthe controller of each subsystem and adjusting the interconnected gainbetween subsystems to ensure that the trajectories of states do notexceed the given safety bound.

◁ PSaB-25Thruster Fault Tolerant Control Scheme for 4500-m Human OccupiedVehicle

Huang, Ming Shanghai Maritime Univ.Zhu Daqi Shanghai Maritime Univ.Chu, Zhenzhong Shanghai Maritime Univ.

In this paper, a thruster fault tolerant control combines with trajectorytracking control method is applied for 4500-m Human Occupied Vehi-cle. First, the tracking control method and thruster configuration of ahuman occupied vehicle with 4500m operation depth is simply intro-duced. Then control allocation problem of underwater vehicle is de-scribed, thruster forces reconstructed during control allocation. Finally,introduced a hybrid fault tolerant control method, this hybrid methodis designed based on weighted pseudo-inverse matrixes and quan-tum particle swarm optimization (QPSO), compared with the classicalweighted pseudo-inverse method, and simulations results illustrate theperformance of the thruster fault tolerant control strategy.

◁ PSaB-26Design of Test Case for ATP Speed Monitoring Function Based onCause-effect Graph

Dou, Lei Southwest Jiaotong Univ.Yang, Wudong Southwest Jiaotong Univ.

In order to improve the current ATP (Automatic Train Protection, AT-P) speed monitoring function test case incomplete problem, Taking thespeed monitoring function of ATP in the TSM (Target Speed Monitor(TSM) area as the research object, the cause-effect graph is used toanalyze the speed monitoring logic and design test cases. Firstly, func-tional logic of speed monitoring function of ATP in TSM area is ana-lyzed. Then, the process of designing test cases for cause-effect graphis introduced. Finally, the cause-effect graph is utilized to design testcases for the speed monitoring function of ATP in TSM area. The re-sults show that the test case designed by this method can improve thefunctionality of ATP speed monitoring function and the completeness ofthe safety test, thus ensuring the quality of ATP products.

◁ PSaB-27Simple adaptive control with anti-windup compensator for aircraft atti-tude control

Gai, WenDong Shandong Univ. of Sci. and Tech.

Zhou Yecheng Shandong Univ. of Sci. and Tech.Sun, Chengxian Shandong Univ. of Sci. and Tech.Zhang, Jing Shandong Univ. of Sci. and Tech.

In this paper, the simple adaptive control (SAC) with the anti-windupcompensator (SAC-AW) is proposed for the attitude tracking of aircraftin the presence of actuator fault and saturation. First, the aircraft atti-tude model with actuator saturation is established. Then the SAC-AW isdesigned to deal with the actuator fault and saturation in aircraft attitudecontrol system. Meanwhile, the closed loop system stability is provedby utilizing Lyapunov’s direct method. Simulation results verify theeffectiveness of the presented method for aircraft attitude control in thepresence of the actuator fault and saturation.

◁ PSaB-28Multiphase and Multimode Monitoring of Batch Processes Based onDensity Peak Clustering and Just-in-time Learning

Fan, Saite Zhejiang Univ.Shen, Feifan Zhejiang Univ.Song, Zhihuan Zhejiang Univ.

In this paper, a data-driven framework base on density peak clustering(DPC) and just-in-time learning (JITL) is developed to handle with mul-tiphase and multimode monitoring problem of batch processes. To dealwith batch-to-batch variations and non-Gaussian distributions of batchdata, DPC is firstly used for phase and mode classification and identifi-cation. Due to the variety of output trajectories in the same phase andmode, JITL is used to extract similar trajectories as an advanced sub-division strategy to obtain sub-datasets with similar output trajectories.Thus, for each sub-phase in a certain sub-mode, local quality-relevantmodels are established to achieve an accurate modeling and monitor-ing scheme. Finally, Bayesian fusion is introduced as the ensemblestrategy to determine the final probability of faulty conditions. For per-formance evaluation, a numerical example and a simulated fed-batchpenicillin fermentation process are provided. The monitoring resultsshow the effectiveness of the proposed method.

◁ PSaB-29Data-driven RUL Prediction of High-speed Railway Traction SystemBased on Similarity of Degradation Feature

Zhu, Kaiqiang Nanjing Univ. of Aeron. and Astron.Zhang,Chuanyu Nanjing Univ. of Aeron. and Astron.Lu,Ningyun Nanjing Univ. of Aeron. and Astron.Jiang,Bin Nanjing Univ. of Aeron. and Astron.

The remaining useful life (RUL) prediction of high-speed railway tractionsystem is of great significance for ensuring the safe and efficient opera-tion of high-speed railway trains. Due to the complex structure of high-speed railway traction system, it is difficult to reveal system-level degra-dation mechanism; thus data-driven RUL prediction becomes dominan-t. A data-driven RUL prediction method based on similarity of degra-dation features and Long Short Term Memory (LSTM) network is pro-posed in this paper. The seq2seq structure of the LSTM neural net-work is adopted to extract the multivariate features of the degradationtrajectory. Based on these features, a similarity-based RUL predictionmethod is proposed. Verification is conducted on the test bench platfor-m of the CRH2 traction system. The results can show that the proposedmethod can extract reasonable degradation features and output moreaccurate RUL prediction results.

◁ PSaB-30Wind Turbine Periodic Intermittent Fault Detection Based on FractionalOrder Chaotic System

Gao, Bingpeng Xinjiang Univ.Wang, Weiqing Xinjiang Univ.Ji, Xinru Xinjiang Univ.

For the wind turbine vibration signal periodic intermittent fault detectionproblem, we put forward a new detection method：first to extract thewind turbine vibration original signal fundamental frequency based onMorlet wavelet analysis, and then get a new set of signal baseband re-moved as the fractional terminable vibration system input. Whether thewind turbine has periodic intermittent faults or not is judged by whetherthe phase trajectory is in a terminable state. The system is highly sen-sitive to the periodic signal but insensitive to the random perturbation.The experiment proves that the method has good function of early faultprediction.

◁ PSaB-31

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A Kernel Canonical Correlation Analysis-Based Fault Detection Methodwith Application to a Hot Tandem Rolling Mill Process

Qi, Tianjing Univ. of Scie. and Techn. BeijingZhang, Kai Univ. of Scie. and Techn. BeijingPeng, Kaixiang Univ. of Scie. and Techn. BeijingZhao, Shanshan Univ. of Scie. and Techn. Beijing

In process industries like the hot tandem rolling mill (HTRM) process,faults occurred in different places will cause different impacts on thefinal product qualities. Thus, a comprehensive process maintenanceshould be considered to guarantee a stable and reliable productionprocess. Different from traditional fault detection (FD) methods thatcan only determine the maintenance time by means of the successfuldetection time, this paper proposes a multi-step FD method that alsoreveals the hazard of the fault, so that different maintenance levels canbe given to process engineers. Firstly, the HTRM process is dividedinto up, middle and down stream. Through the further decomposition ofthe kernel canonical correlation analysis (KCCA) model, the variationof each stream of the process data in multiple subspaces are obtained.Then, statistical fusion with Bayesian inference is used to make the fi-nal detection decision, and the maintenance level is determined by thearea where the fault occurred. Finally, the application to a HTRM pro-cess is given to demonstrate the performance and effectiveness of theproposed method.

◁ PSaB-32A Fault Detection and Identification Method Based on Mixed Logic Dy-namic Model for Three-phase Inverter Using Single Current Sensor

Zhong, Ningfan Shandong Univ. of Scie. and Techn.Zhai, Yanqiang Shandong Univ. of Scie. and Techn.Zhang, Zhenhai Shandong Univ. of Scie. and Techn.

A fault detection and identification (FDI) method based on mixed logicdynamic model for three-phase inverter using single current sensor isproposed in this paper. Single current sensor is used to measure andreconstruct the output currents of a three-phase inverter. A mixed logicmodel of the inverter is further established to estimate the output cur-rent in real-time under different fault conditions. The estimated currentsand the measured currents can be compared and residuals betweenthem can also be calculated in real-time to detect and locate the faults.Compared with existing FDI methods, method proposed in this papercan reduce the uncertainty brought by current sensors by using singlecurrent sensor, and improve the accuracy of fault diagnosis by usingmixed logic model of the inverter. The algorithm proposed in this paperis easy to be implemented, and can be used in versatile applications.Simulation results show the effectiveness of the FDI method.

◁ PSaB-33Robust Control of Single Phase PWM Rectifier with Parametric Uncer-tainties

Motaz Musa Ibrahim Southwest Jiaotong Univ.Ma, Lei Southwest Jiaotong Univ.Qin, Na Southwest Jiaotong Univ.

Application of power-electronic transformers to train traction system isan obvious trend of development. This promote light-weight and small-volume design, and thus enables energy-saving and higher speed oftrains. On the other hand, fragile power-electronic components areconnected to the grid because traditional transforms are removed. Ro-bustness and fault-tolerant capability of power-electronic transforms be-come crucial. In this paper H∞controller is proposed for inner-loopcontrol for a single-phase PWM rectifier with parametric uncertainties,frequency perturbation and AC current disturbance. Impact of this con-trol strategy on supply current, output voltage ripple and dq-axis cur-rents is studied. A comparison with linear quadratic regulator (LQR) ispresented to prove the robustness of the proposed controller.

◁ PSaB-34Research on co-design between security control and communication ofa class of nonlinear CPS under cyber attack

Zhao, li Lanzhou Univ. of Tech.Li, Wei Lanzhou Univ. of Tech.Li, Yajie Lanzhou Univ. of Tech.Shi, Yahong Lanzhou Univ. of Tech.

The co-design problem is investigated for a class of nonlinear CPS un-der cyber attack, physical component failure and limited communicationresources by introducing DETCS. Firstly, in the circumstances of cyber

attack and physical component failure, the defense idea of active fault-tolerant combine with passive attack-tolerant is proposed , and on thebasis, a nonlinear CPS security control model is established that in-tegrates trigger condition, actuator fault and cyber attack. Secondly,based on time delay system theory, the design of robust attack-tolerantobserver which can estimate the being attack state and fault in realtime, as well as a method of co-compute between fault-tolerant, attack-tolerant controller and event trigger matrix are given respectively. Thus,the goals of active fault-tolerant, passive attack-tolerant control and themethod of saving network communication resource are achieved. Fi-nally, a simulation example is given to verify the effectiveness and fea-sibility of the theoretical results.

◁ PSaB-35A Real-Time Anomaly Detection Approach Based on Sparse Distribut-ed Representation

Wang,Weikai Donghua Univ.Zhao, Chenwei Donghua Univ.Hao, Kuangrong Donghua Univ.Tang, Xuesong Donghua Univ.Wang, Tong Donghua Univ.

As a hot topic in process industries, the problem of anomaly detectionhas been researched for years. A lot of model-based and data-basedapproaches were developed to monitor and diagnose faults. As knownto us, the data-driven are more suitable for a modern industrial pro-cess that commonly associated with complex, coupled and large-scalesubsystems. In such case, it is hardly to construct an exact model.In existing data-driven approaches, the statistics-based and the graphtheory-based are typical technologies. But, a fatal flaw of them is online.Some of them works well in offline scenario, however, the performanceof online is contrary to that such as Bayesian network. Thanks to ourbrain, the most complex and rigorous organ in nature copes with quan-tities of information every moment. A novel and intelligent idea calledsparse distributed representation (SDR) has been proposed to encodeeach element of online data, which is inspired by the information pro-cessing way of cerebral cortex. In this paper, a further exploring on SDRis carried out. We propose a theoretical foundation for resolution that isa very important item for SDR to encode each digit exactly. In addition,we also provide a calculation method for its processing boundaries. Ul-timately, we take this approach to detect real-time anomaly data likeconcept drift, and achieve good simulation performance.

◁ PSaB-36HAZOP quantitative analysis of the balise based on the improvedCUOWGA–Sharpley value

Chu, Xintong Southwest Jiaotong Univ.Tong, Yin Southwest Jiaotong Univ.Guo, Jin Southwest Jiaotong Univ.

The traditional HAZOP analysis method over-rely on the experts’subjective evaluation and lacks the consideration about risk correlationsbetween risk points. In order to solve these problems, a HAZOP quanti-zation analysis method based on the improved CUOWGA operator andthe Sharpley value is proposed. Taking the balise as an example, first,use HAZOP to implement a qualitative analysis of the balise function-al layered model. Second, establish a fuzzy evaluation set to quantifythe expert evaluation as a confidence interval. Then use the CUOWGAoperator to assemble the evaluation and to reduce the subjectivity. Usethe Sharpley value to calculate the risk weight after considering the riskcorrelation. Finally, a safety quantitative assessment of the balise sys-tem can be obtained. The assessment shows that the failure probabilityof the balise system is 28.84%, and the risk level is“Tolerable”. Thegreatest impact on the system is the ATP transmits information sub-functions failure. The result is consistent with the practical application,which shows that the method is effective.

◁ PSaB-37Thermodynamic Mechanism and Data Hybrid Driven Model Based Ma-rine Diesel Engine Turbocharger Anomaly Detection with PerformanceAnalysis

He, Xiao CSSCWang, Jia Beijing Univ. of Chemical Techn.Wei, Muheng CSSCQiu, Bohua CSSCYang, Ying Peking Univ.

For the marine low-speed diesel engine, the performance evaluation

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based turbocharger anomaly detection plays an important role in thecondition monitoring process. A thermodynamic mechanism and datahybrid driven model is proposed for the turbocharger anomaly detectionin this paper. The turbocharger efficiency, as the Key Performance Indi-cator (KPI), is modeled using the thermodynamic mechanism method.In the monitoring process, the turbocharger healthy baseline is estab-lished with the data-driven method, which divides the efficiency spaceinto “Normal Area”and “Fault Area”. Results of the presentedmethod are verified by the studies of the real low-speed diesel enginemonitoring case on a large ocean-going bulk carrier.

◁ PSaB-38Fault-tolerant Control for Networked Control Systems with Partly Un-known Transition Probabilities

Wang, Yanfeng Harbin Engineering Univ.Wang, Peiliang Harbin Engineering Univ.Li, Zuxin Harbin Engineering Univ.Ttalbi, Mohamed Harbin Engineering Univ.Wang, Yuling Harbin Engineering Univ.

The problem of fault-tolerant controller for uncertain discrete-time net-worked control systems against actuator possible fault is researched inthis paper. The time-delay is modeled as a finite state Markov chain ofwhich the transition probabilities are partly known. By introducing ac-tuator fault indicator matrix, the closed-loop system model is obtainedby means of state augmentation technique. The sufficient and neces-sary conditions on the stochastic stability of the closed-loop system aregiven. The method of calculating the mode-dependent controller gainis also proposed. A numerical example is presented to illustrate theeffectiveness of the proposed method.

◁ PSaB-39Research on fault classification method based on deep belief network

Wei, Yuqin Shanghai Jiao Tong Univ.Weng, Zhengxin Shanghai Jiao Tong Univ.

In recent years, deep learning has shown unique advantages and po-tentials in feature extraction and pattern recognition, attracting moreand more attention from academics and engineering researchers. Thispaper proposes a chemical process fault detection and diagnosismethod based on Deep Belief Network (DBN)-Dropout model. DBNcan directly use process monitoring data, through unsupervised featurelearning and supervised fine-tuning, to build a deep network model toachieve real-time monitoring of the process. In addition, Dropout tech-nology was introduced to reduce over-fitting of deep network. The finalpart of the paper compares the performance of this method with theperformance of traditional neural network method using the TennesseeEastman process benchmarking platform.

◁ PSaB-40Deep Convolution Neural Networks for the Classification of Robot Exe-cution Failures

Liu, Ying Naval Univ. of EngineeringWang, Xiuqing Naval Univ. of EngineeringRen, Xuemei Naval Univ. of EngineeringFeng Lyu Naval Univ. of Engineering

Deep convolution neural networks (DCNNs) are popular deep neuralnetworks and are widely used in object recognition, handwriting recog-nition, and image processing and so on. In this paper, manipulator-faultclassifier based on DCNNs is proposed, and the sensor data from theforce and torque sensors are preprocessed and reconstructed into anew form, which is suitable for the input of DCNNs. The experimen-tal results show that the designed classifier can distinguish time-seriessensor data from manipulator’s normal state and various fault stateseffectively. The proposed method is helpful to take measures to let themanipulator recover from the fault state to normal working sate, and isuseful to enhance the executive ability of manipulators.

◁ PSaB-41Random-Sampling-Based Performance Evaluation Method of Fault De-tection and Diagnosis for Railway Traction System

Fang, Dikai Central South Univ.Peng, Tao Central South Univ.Yang, Chao Central South Univ.Chen, Zhiwen Central South Univ.Tao, Hongwei Central South Univ.

A random-sampling-based performance evaluation method to compre-

hensively and objectively reflect fault detection and diagnosis (FDD) isproposed. This method is used to test and evaluate the FDD algorithm-s to be used in traction control system(TCS). Firstly, the needed faultinformation of fault injection models to simulate fault scenarios is di-vided into fault location, fault type, and fault parameter. Then, varioussampling methods are used to hierarchically sample fault informationto generate a specific fault injection model. As a consequence, someFDD algorithms can be executed and tested in these simulated faultscenarios and their test results will also be recorded. Finally, accord-ing to a large number of tests and their generating results, three-levelevaluation indexes are constructed to evaluate the mentioned FDD al-gorithms. The proposed method is implemented in MATLAB/Simulinkand embedded into the developed fault-injection software for TCS, andthe simulation results more objectively and comprehensively evaluatethe performance of the tested FDD algorithms, which will be useful forresearches to select and improve satisfied algorithms.

◁ PSaB-42Active Fault Tolerant Control for HVAC System Based on GIMC andFeedforward Compensation

Guo, Weijie Nantong Univ.Qiu, Aibing Nantong Univ.Li, Xue Nantong Univ.

In this paper, a high performance active fault-tolerant control methodbased on generalized internal model control(GIMC) and feed-forwardcompensation is developed. Firstly, the multiplicative actuator fault isestimated in real time by the use of the adaptive technology, and thenthe robustification controller of GIMC is constructed based on the ob-tained fault information to restrain the impact of these faults. Then thefeed-forward compensation unit is designed to guarantee that the sys-tem can work as the fault-free situation. The advantages of the controlstructure are as follows: when the system is in the nominal situation, therobustification controller and feed-forward compensation unit are notactivated, and the system is controlled by the performance controllerto ensure high performance; When the fault occurs, the robustificationcontroller and feed-forward compensation unit can accurately make acompensation for the effects brought by those faults, and make it re-cover to the fault-free performance quickly. Finally, a HVAC system ischosen to verify the effectiveness and superiority of the new active faulttolerant control structure.

◁ PSaB-43Data-driven based ToMFIR Design with Application to Incipient FaultDetection in High-speed Rail Vehicle Suspension System

Wu, Yunkai Jiangsu Univ. of Sci. & Tech.Jiang,Bin Nanjing Univ. of Aeron. & Astron.Zhu, Zhiyu Jiangsu Univ. of Sci. & Tech.Zeng,Qingjun Jiangsu Univ. of Sci. & Tech.

Suspension system, the base of the traction system, whose reliabil-ity is of critical importance to the safety of the CRH (China RailwayHigh-speed) train. Incipient FDD (Fault Detection and Diagnosis) ofsuspension faults can improve the reliability and avoid serious failuresin the entire traction systems. Such that, the incipient FDD of springand damper, the core component of suspension system is urgently de-manded. In this paper, a discrete mathematical model for suspensionsystem of a three-car multiple unit is established firstly, based on which,the track irregularities (deterministic disturbances) and then the incip-ient reduction of spring and damper coefficient (incipient componen-t faults) are further modeled. After that, a data-driven total measur-able fault information residual (ToMFIR) based incipient fault detectionmethod is proposed for the possible incipient spring and damper faults.Finally, the proposed theoretical results are applied to a CRH supen-sion simulation system. Simulation results show that the incipient faultdetection scheme proposed in this paper provides high sensitivity to in-cipient suspension faults (spring and damper coefficients reduction of15applications.

◁ PSaB-44CNN Based Process Monitoring of Spatially Distributed System

Ma,Fangyuan Beijing Univ.of Chem. Tech.Lin, Dexi Sinochem Quanzhou Petrochemical Co., Ltd,Zhong, Jingtao Sinochem Quanzhou Petrochemical Co., Ltd,Han, Xianyao Beijing Univ.of Chem. Tech.Wang, Jingde Beijing Univ.of Chem. Tech.

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Sun, Wei Beijing Univ.of Chem. Tech.

With the wide use of distribution control system (DCS) in process in-dustry, large amount of data have been collected, which have greatlyaccelerated the development of data-driven process monitoring meth-ods. In these methods, the autocorrelation and cross-correlation of dataare mainly considered, especially the cross-correlation has been inten-sively investigated in last several decades. However, in these chemicalproduction processes, most transfer and reaction are conducted in athree-dimensional equipment, in which some key performance indica-tors are spatially distributed, which hasn’t been paid special attentionin previous study. In this work, a convolutional neural network model isestablished to extract the spatial correlation among variables togetherwith their autocorrelation and cross correlation. A process monitoringmodel is then established based on this convolutional neural network.A pre-reforming reactor of hydrogen production units is investigated asa case study. The results show that the process deviation can be de-tected at least 3 hour earlier than human operator.

◁ PSaB-45Adaptive fuzzy control for a flexible air-breathing hypersonic vehiclebased on tracking differentiator

Feng, Cong Beihang Univ.Wang, Qing Beihang Univ.Zhang, Shen China Academy of Space Tech.

This paper presents an adaptive fuzzy sliding mode controller for a flex-ible air-breathing hypersonic vehicle based on track differentiator. First,a control-oriented model is derived and decomposed into velocity andaltitude subsystems. With the system transformation, the altitude sub-system is converted into the normal cascade structure. A novel slidingmode control scheme is proposed which avoid the complicated recur-sive design procedure of backstepping. In the control scheme, the un-known nonlinear functions, adaptive fuzzy logic systems are designedto estimate them. Meanwhile, a tracking differentiator is used to esti-mate the newly generated states of the transformed altitude subsystem.The sliding mode surfaces are proved to be asymptotically accessibleand both velocity and altitude subsystems can converge to their com-mand signals. Finally, simulation results are demonstrated to show theeffectiveness of the proposed control scheme.

◁ PSaB-46Transistor Temperature Balancing Method for Three-level InvertersBased on FCS-MPC

Peng, Tao Central South Univ.Xie, Feiran Central South Univ.Yang, Chao Central South Univ.Yang, Chunhua Central South Univ.

The there-level NPC inverter is one of the most widely used inverter-s. Its main shortcoming is the uneven temperature distribution causedby uneven power loss distribution. For this reason, this paper propos-es a temperature balance control (TBC) method based on finite controlset model predictive control (FCS-MPC), which can effectively improvethe temperature distribution of inverters. Firstly, the evaluation functionof the FCS-MPC is established, which includes current tracking, pow-er tracking and constraints. Then according to the discrete model ofthe system, the output current and the transistor power loss predictionvalue corresponding to all the effective switching states of the next sam-pling period are calculated. Finally, in all the effective switching states,the state corresponding to the minimum evaluation function value is s-elected. The switch state will be used as the control signal for the nextsampling period. The simulation results show that the proposed TBCmethod can not only balance the temperature distribution, but also haslittle effect on the system control performance.

◁ PSaB-47Finite Time Convergence Incremental Nonlinear Dynamic InversionBased Attitude Control for Flying-Wing Aircraft with Actuator Fault

Han, Wuhan Nanjing Univ. of Aeronautics and AstronauticsZhang, Shaojie Nanjing Univ. of Aeronautics and Astronautics

This paper proposes a finite time convergence incremental nonlineardynamic inversion (FINDI) based two-loop fault-tolerant attitude controlscheme for nonlinear flying-wing aircraft. A classic nonlinear dynamicinversion (NDI) controller is designed for the attitude loop, and a FINDIcontroller is designed for the angular rate loop. The proposed controllercan track the reference signal in finite time and get better performance

than regular incremental nonlinear dynamic inversion (INDI) scheme indealing with the time delay problem. Considering the configuration ofthe control surfaces, control allocation strategy is introduced to com-pensate multiple actuator faults of the aircraft. Simulation results areillustrated to demonstrate the effectiveness of the present approach.

◁ PSaB-48Incipient Fault Diagnosis of Sucker Rod Pumping System Using Kull-back Leibler Divergence Based Improved Kernel Principal ComponentAnalysis

Cai, Peipei China Univ. of PetroleumDeng, Xiaogang China Univ. of PetroleumCao, Yu-ping China Univ. of Petroleum

The sucker rod pumping (SRP) system plays an important role in theoil exploitation industry. It is a challenging task to detect its incipientchanges for avoiding the serious faults and significant losses. In thispaper, an incipient fault diagnosis method using Kullback Leibler di-vergence based improved kernel principal component analysis (KLD-KPCA) is proposed for the early diagnosis of the SRP system incipi-ent faults. Considering that the basic KPCA model can not detect theincipient process changes sensitively, the Kullback Leibler divergenceis introduced to measure the changes of kernel principal components,which leads to the improved kernel principal components called KLDcomponents. Two monitoring statistics based on KLD components areconstructed to detect the incipient changes. To solve the fault variableidentification problem in the KLD-KPCA monitoring model, a nonlinearcontribution plot is developed to isolate the faulty variables. The appli-cation on one simulated SRP system demonstrates that the proposedmethod can detect the incipient changes of the SRP system runningstate effectively than the traditional KPCA method, and the designedKLD-KPCA contribution plot is helpful to locate the fault variables.

◁ PSaB-49Disturbance-observer-based Adaptive H∞Fault-tolerant Control forHigh-speed Trains

Lin, Xue Beijing Jiaotong Univ.Gao, Shigen Beijing Jiaotong Univ.Dong, Hairong Beijing Jiaotong Univ.

This paper focuses on the tracking control problem of high-speed train-s subject to unknown actuator faults and multi-source disturbances. Adisturbance observer is designed to estimate the modeled disturbanceswith unknown states. Based on the disturbance observer, an adaptiveH∞fault-tolerant control algorithm is proposed. The stability of the sys-tem by applying the proposed fault-tolerant control strategy is provedby the Lyapunov stability theory, and the simulation results verify thefeasibility and effectiveness of the proposed control strategy.

◁ PSaB-50A Descriptor System Approach for False Data Injection Attacks TowardPower System

Ding, Zhou Nantong Univ.Qiu, Aibing Nantong Univ.Li, Xue Nantong Univ.

This paper investigates the problem of false data injection attacks to-ward power system from the viewpoint of dynamic system. Firstly, bycombining generator swing model and power flow equation, the powersystem is modeled as a descriptor linear system, which is monitoredby a conventional observer. Then, a necessary and sufficient conditionfor perfect attack is given in terms of unstable eigenvector by eigen-decomposing the system matrices. Based on this, an attack schemeis proposed under which the attacker can destabilize the power systemwhile pass the failure detector. Compared with the existing static attackmethods, the proposed scheme is more concealed and easier to imple-ment. A simulation on IEEE 9-bus is finally presented to demonstratethe feasibility and effectiveness of our method.

◁ PSaB-51Multiblock ICA-PCA and Bayesian Inference based Distributed ProcessMonitoring

Wang, Hongyang Beijing Univ. of Chemical Tech.Chen, Xiaolu Beijing Univ. of Chemical Tech.Wang, Jing Beijing Univ. of Chemical Tech.Liu, Qiang Northeastern Univ.

As large-scale industrial processes become more and more complex,traditional centralized multivariate statistical methods are not ideal for

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monitoring such processes. In this paper, a distributed process monitor-ing framework based on independent component analysis (ICA), prin-cipal component analysis (PCA) and Bayesian inference is proposed. Itovercomes the shortcoming that the monitoring variables of the princi-pal component analysis model are required to satisfy the assumption ofindependent Gaussian distribution. First, the performance-driven multi-block ICA-PCA method and genetic optimization algorithm are usedfor optimal process decomposition. Then, Bayesian comprehensiveinference statistics is calculated to fuse the information of local ICA-PCA models. System anomalies can be discovered using the decisionsmade by the fusion center. Finally, the feasibility and effectiveness ofthe proposed method are verified by Tennessee Eastman (TE) process.

◁ PSaB-52Observer-based Sliding Mode Fault Tolerant Control for Spacecraft At-titude System with Actuator Faults

Wang, Qing Beihang UniversityLiang, Xiaohui Beihang UniversityRan, Maopeng Nanyang Technological UniversityDong, Chaoyang Beihang University

In this paper, the problem of fault tolerant control (FTC) is studied forthe rigid spacecraft attitude systems with exogenous disturbances andactuator faults simultaneously. Firstly, an iterative learning-based ob-server is developed, which can estimate the actuator faults with highprecise. Then, employing the fault estimate information of the de-signed observer, a sliding-mode FTC scheme is proposed to ensurethe closed-loop attitude system asymptotically stable and reject to theexternal disturbance. Finally, the effectiveness of the proposed methodis demonstrated by the number simulation.

◁ PSaB-53Fault detection and diagnosis based on a new ensemble kernel princi-pal component analysis

Li, Xintong Tianjin Univ.Rui, Felizardo Tianjin Univ.Xue, Feng Tianjin Univ.Qin, Lida Tianjin Univ.Kai, Song Tianjin Univ.

Kernel principal component analysis is a technique applied for moni-toring nonlinear processes. However, compute control limit based onGaussian distribution can deteriorate its performance. Kernel densityestimation is applied to solve the aforementioned issue. In convention-al KPCA, a kernel based model depends on a single Gaussian kernelfunction selected empirically, which means a single model correspond-s to a single Gaussian kernel function. It may be effective for certainkinds of fault but not for others which leads to a poor detection perfor-mance. Different Gaussian kernel functions may be needed for eachkind of fault. To solve these issue, in this work, a novel ensemble kernelprincipal component analysis-Bayes (EKPCA-Bayes) is proposed. Theensemble learning with Bayesian inference strategy were applied intoconventional KPCA. At last, the fault diagnosis performance is testedfor the first time through contribution plot to find out the root cause vari-ables. The proposed method was tested in the Tennessee Eastman(TE) benchmark process for fault detection and fault diagnosis as well.

◁ PSaB-54Sensor Fault Estimation for Lipschitz Nonlinear System with Distur-bance

Han, Jian Northeastern Univ.Liu, Xiuhua Northeastern Univ.Wei, Xinjiang Northeastern Univ.Hu, Xin Northeastern Univ.Zhang, Huifeng Northeastern Univ.

This paper addresses the problem of sensor fault estimation for Lips-chitz nonlinear system with disturbance and immeasurable system s-tate. A new system is constructed by treating sensor fault as a specialstate. And the fault estimation observer is designed to estimate the s-tate of the new system, which include the sensor fault and the originalsystem state. Both the modelable disturbance and the unmodelabledisturbance are considered. Disturbance observer is designed to es-timate the modelable disturbance, and 𝐻∞ performance is introducedto reduced the effect of the unmodelable disturbance. At the end of thispaper, the simulation shows the effectiveness of the proposed method.

◁ PSaB-55Subspace Alignment based Adapted GLRT Detector and Its Application

in Marine Current TurbineZhang, Milu Shanghai Maritime Univ.Wang, Tianzhen Shanghai Maritime Univ.Tang, Tianhao Shanghai Maritime Univ.

Most traditional methods can detect signals in noise by detecting thechange of mean value of test statistics. But these methods assumethat the signal parameter is known without considering unpredictabili-ty of the actual industrial process. Generalized Likelihood Ratio Test(GLRT) detector, one of most widely used detector, can replace un-known parameters with the results of maximum likelihood estimation.However, the classical detector cannot change with the working con-ditions. Also it can’t solve the troubles of dynamic variation using priorknowledge, which lead to a very high rate of missing detection. To over-come this shortcoming, an adapted GLRT detector is proposed basedon subspace alignment to highlight weak signal characteristics, thena mechanism changes time series model is established by DetrendedFluctuation Analysis (DFA). Experimental signals are used to illustratethe effectiveness of the proposed method using three-phase stator cur-rent of Marine Current Turbine (MCT) under complex working condi-tions. The results show that the proposed method can extract weakfeatures, and has good feature portability.

◁ PSaB-56Design of a Fault Diagnosis System for the ”JiaoLong” Deep-seaManned Vehicles

Chen, Xu Tsinghua Univ.He, Xiao Tsinghua Univ.

With increasing number of deep-sea manned submersibles being onservice, fault diagnosis for their control systems has become an indis-pensable task. Current hardware solutions of fault diagnosis are usuallydesigned for a specific category of devices, which would lead to hugemanual and economic costs when applied to deep-sea submersiblescomposed of subsystems varying in interface. In order to avoid the dif-ficulties, a wireless-based fault diagnosis hardware solution is proposedwhich is applicable for systems with different electrical features. More-over, it provides several functions including fault diagnosis, simulationof fault injection and direct control of actuators, and it not only possess-es scalability for further analysis of target system but also expandabilityfor applications besides deep-sea submersibles.

◁ PSaB-57Stacking Model-based Method for Traction Motor Fault Diagnosis

Peng, Tao Central South Univ.Ye, Chenglei Central South Univ.Chen, Zhiwen Central South Univ.

An ensemble learning method is proposed to improve low classificationaccuracy and weak anti-noise ability in traction motor fault diagnosis.This paper adopts stacking model to stack SVM, Xgboost and Light-gbm. Comparison between stacking and commonly used single modelis further conducted in the cases of the noise and without noise. Theresults show the advantages of the stacking model-based method.

◁ PSaB-58A Semi-supervised Constraints Propagation Based Method for Fault Di-agnosis

Liao, Guobo Chongqing Univ.Zhou, Han Chongqing Univ.Li, Yanxia Chongqing Univ.Yin, Hongpeng Chongqing Univ.Chai, Yi Chongqing Univ.

Fault detection and identification could minimize unexpected degrada-tion of system and further avoid dangerous situation. Due to the rapiddevelopment of sensor technology as well as the Internet, exponen-tial data could be collected, resulting in that data-driven based faultdiagnosis method receives increasing attention. However, most work-s often learned low dimensional representations so that they could-n’t preserve the real local geometric structure of original data. Thismight degrade fault diagnosis capabilities. In this paper, a novel semi-supervised constraints propagation based method for fault diagnosiswas proposed. The key point was to spread the linking information ofsupervised data to its neighbors via constraints propagation. Accord-ingly, the propagated similarity matrix could correctly reflect the struc-ture of the samples. Further, with the aid of propagated matrix, sam-ple indexes were learned via singular value decomposition and support

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vector machine were utilized to identify the type of faults. The effective-ness of the proposed methods was demonstrated through the experi-mental results, compared with other popular fault diagnosis methods.

◁ PSaB-59Turnout Fault Diagnosis Algorithms of Full-Electronic Interlocking Sys-tem Based on BP Adaboost

Chen, Guangwu Lanzhou Jiaotong Univ.Yu, Yijian Lanzhou Jiaotong Univ.Xing, Dongfeng Lanzhou Jiaotong Univ.Yang, Juhua Lanzhou Jiaotong Univ.

With the rapid development of Chinese railways, railway station signalcontrol system has developed rapidly with the help of the fourth gener-ation of all-electronic interlocking system. According to the control cir-cuit and switching state in switch module of electronic interlocking sys-tem and monitor switching current, analysis the monitoring machine ofswitch active current, the characteristic input value of switch is extract-ed and switch fault model is established. Firstly, data training and test isclassified by BP neural network, then strong classifier is constructed byoptimized Adaboost, the matching classification between turnout char-acteristic quantity and turnout fault type is carried out. After simulation,when BP neural network algorithm is used alone, the fault diagnosisrate is 90.2%, while the strong classification effect of BP Adaboost al-gorithm can improve accuracy of turnout fault diagnosis by 95.8%, andthe accuracy of latter is 5% higher than that of the former. The methodvalidity is verified, which provides important research significance forturnout fault diagnosis of all-electronic interlocking system.

◁ PSaB-60A Small Leakage Detection Approach for Gas Pipelines based on CNN

Li, Jie Chongqing Univ. of Sci. and Tech.Liu, Yao Chongqing Univ. of Sci. and Tech.Chai, Yi Chongqing Qingshan Industry Co.He, Hongli Chongqing Univ. of Sci. and Tech.Gao, Min Chongqing Univ. of Sci. and Tech.

With the rapid development of Chinese railways, railway station signal

control system has developed rapidly with the help of the fourth gener-ation of all-electronic interlocking system. According to the control cir-cuit and switching state in switch module of electronic interlocking sys-tem and monitor switching current, analysis the monitoring machine ofswitch active current, the characteristic input value of switch is extract-ed and switch fault model is established. Firstly, data training and test isclassified by BP neural network, then strong classifier is constructed byoptimized Adaboost, the matching classification between turnout char-acteristic quantity and turnout fault type is carried out. After simulation,when BP neural network algorithm is used alone, the fault diagnosisrate is 90.2%, while the strong classification effect of BP Adaboost al-gorithm can improve accuracy of turnout fault diagnosis by 95.8%, andthe accuracy of latter is 5% higher than that of the former. The methodvalidity is verified, which provides important research significance forturnout fault diagnosis of all-electronic interlocking system.

◁ PSaB-61Actuator Fault Detection Filter Design for Continuous-time SwitchedSystems in Finite Frequency Domain

Zhu, Dewen Lanzhou Jiaotong Univ.Du, Dongsheng Lanzhou Jiaotong Univ.Chen, Haoshuang Lanzhou Jiaotong Univ.Wen, Runting Lanzhou Jiaotong Univ.

This paper is concerned with the problem of the fault detection (FD)filter design for continuous-time switched systems with actuator faults.The actuator faults and the unknown disturbances are considered to bein finite frequency domain. By using the switched Lyapunov functionand the average dwell-time (ADT) techniques, efficient conditions areobtained, which can realize the residual signal sensitive to the fault androbust to the unknown disturbances. Linear matrix inequalities (LMIs)conditions are proposed to design the fault detection filter, which canguarantee the finite frequency 𝐻− and 𝐻𝑖𝑛𝑓𝑡𝑦 performance index. Fi-nally, a practical example is provided and simulation results are con-ducted to demonstrate the effectiveness of the proposed approach.

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