EditorialSpecial Issue: PHM for Railway Systems and Mass Transportation

John Andrews, Thomas Bohm, Pierre Dersin, Jorn C. Groos, and Rene Schenkendorf

The railway and mass transportation system is composed ofindustrial goods with substantial capital investments and longlife cycles. This applies to rolling stock like trains, locomo-tives, wagons, and even more to the infrastructure like sig-naling, catenary, tracks, bridges, and tunnels. The lifespanof rolling stock is 30 to 40 years while the infrastructure isused 30 to 60 years even more than 100 years in case of tun-nels and bridges. As in other industrial goods, the cost driversare determined in the early design phases but realized mainlyduring a long time of operation. Maintenance is one of themain cost drivers but essential to a reliable, capable, and –above all – safe operation.

Nowadays, depending on the country about 30 to 60 percentof the life cycle costs of railway rolling stock and infrastruc-tures are made up by maintenance costs. In addition, risingdemands on railroad infrastructure operators regarding prof-itability and punctuality call for advanced concepts of Prog-nostics and Health Management (PHM).

Condition-based preventive maintenance aims at strengthen-ing the rail mode of transport through an optimized schedul-ing of maintenance actions based on the actual and prognos-ticated condition. Prerequisite, therefore, is the almost con-tinuous condition monitoring for thousands of kilometers ofrailway tracks as well as ten thousands of technical systemsand sub-systems. The rapidly expanding possibilities for em-bedded sensors in all types of technical components as wellas in-line railway vehicles are the key enabler for condition-based preventive maintenance in large and distributed rail-way networks. This Special Issue of IJPHM solicits papersthat discuss the development of advanced sensor-based con-dition monitoring, smart data management, intelligent diag-nostic data analysis, degradation models, condition prognosisand maintenance scheduling for railway systems. All systemsconsidered benefit from PHM as summarized below.

Given the complex nature of PHM, it is good to start withan overview. The paper by Atamuradov, Medjaher, Dersin,Lamoureux & Zerhouni gives a broad overview of PHM us-ing examples not only from railway but also from other in-dustries. They wrap up the different challenges scientists andpractitioners may face when working on the topic. Their de-scription of the essential tasks to implement PHM and the

introduction of the most important norms for each task guidethrough the complex matter. Further, the authors explain anddiscuss the advantages and disadvantages of model-based, data-driven and hybrid approaches in PHM for various applica-tions and methods, e.g., Particle Filter in crack propagation,or Hidden Markov Models in hydraulic pumps. Their reviewgoes into more detail with a case study on bogie diagnosticsand prognostics.

Condition-based maintenance (CBM) of rotating machineryis applied in many industries; mass transportation is no ex-ception. The paper by Ashasi-Sorkhabi, Fong, Prakash, andNarasimhan presents an outline of the CBM program and afield pilot study on the gearbox in an automated cable-drivenpeople mover (APM) system at Torontos Pearson airport. Faultdetection is performed in real-time using vibration data fromthe APM gearbox with the baseline condition established bytime-domain condition indicators. For prognostics, these in-dicators are utilized for degradation modeling and subsequentRemaining Useful Life (RUL) estimation using random co-efficient and stochastic models. Parameter estimation is un-dertaken using a hierarchical Bayesian approach. While theircase study primarily focuses on a cable-driven APM gearbox,the underlying theory and the tools developed to undertakediagnostics and prognostics tasks are broadly applicable.

For high-speed tilting trains, the correct functioning of the tiltangle measurements is crucial to ensure safe operation. Forthis reason, two sensors mounted at the front and the rear ofeach vehicle are part of the train control system. The detec-tion of a discrepancy between the two sensors of a vehicleimmediately disables the tilting function of the entire trainto avoid critical safety situations. An integrated health mon-itoring framework to identify faulty sensors is presented inthe paper by De Martin, Dellacasa, Jacazio, and Sorli. Thisframework allows reducing the negative impacts on train op-eration due to speed reductions caused by the failure of a sin-gle sensor. The framework is based on a sophisticated phys-ical model of the control system and validated with experi-mental mission profiles on the Lichtenfels - Saafeld and Bat-tipaglia - Reggio Calabria routes.

Air leakage in braking pipes on trains lead to braking issuestrain delays, respectively. The author Wee presents a data-

International Journal of Prognostics and Health Management, ISSN2153-2648, 2017 061 1

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processing framework for air leakage detection which pre-dicts the severity of air leakages simultaneously. Thus, basedon the severity level proper action plans can be implemented.By evaluating operating data of Dutch Railways the estima-tion of the remaining useful life of the air braking pipe forgiven severity levels is shown. The results have demonstratedthat most air leakages can be detected one to four weeks be-fore the braking failure allowing targeted intervention strate-gies.

Controlling the right friction levels between the wheel andrail in railway systems is critical to ensure a reliable trac-tion. At low friction levels, trains lose traction and causetrack damages. At high friction levels, excessive wear of therail-surface can be expected. To balance the friction level,so-called friction modifiers (carrier with particles) are used.In their work, the authors Oomen, Bosman, and Lugt ana-lyze experimentally three commercial friction modifiers re-garding their friction characteristic and wear behavior. Theresults reveal that the level of friction is a function of totalrolling distance, effective sliding length, and sum velocity.The most dominant factor depends on the friction modifierand the working mechanism for friction stabilization. More-over, wear rates are dependent on the type of friction modifierused.

Fiber Optic Sensing (FOS) has become a popular techniqueto monitor large distributed structures. The authors Arakaki,Raghavan, and Schuh push the boundaries for the applicationof FOS in railways. Their high resolution, low-cost opticalreadout could obtain a sensitivity of less than 90fm at fre-quencies up to 80kHz (claimed >10x the state-of-the-art), aswell as be scaled up to monitor many multiplexed sensors.This enabled them to overcome issues of past work. In par-ticular, they correctly distinguished a broken rail from insu-lation joints and detected rail surface abrasion in their labora-tory setting.

Switches (or turnouts) are essential for a flexible operationbut often responsible for high maintenance cost and delays.The manuscript by Bohm introduces an RUL prediction forswitches based on several data sources, e.g., in service con-tinuous condition monitoring, weather, failure documenta-tion. The RUL prediction is transformed into a classifica-tion problem of multiple classes with the effect of reducinguncertainty. The Artificial Neural Networks (aNN) and Sup-port Vector Machines (SVM) are used to predict the RUL inthe form of classes. Showing that the performance of aNNand SVM heavily depends on their parametrization, the au-thor derives those parameters maximising the prediction re-sults. This enables one of the two classification techniquesto precisely reveal future failures of the switch engine earlyenough to prevent them.

The manuscript by Brahimi, Medjaher, Leouatni, and Zer-houni provides a comprehensive review of the current state

of PHM for railway overhead contact lines. This includes thekey sensors and monitoring approaches as well as the algo-rithms for detection, diagnosis, and prognosis. The authorspresent the design context of a PHM solution for overheadcontact lines and discuss the critical system components aswell as failure modes. Based on state of the art principles andthe technical needs for predictive maintenance the recent re-search challenges are outlined to improve PHM for overheadcontact lines further.

In their work, the authors Palmqvist, Olsson, and Hiseliusstudy and quantify how weather, timetable, operational andinfrastructure related variables influence the punctuality ofthe railway system. The study is based on a dataset contain-ing detailed timetables and records of all 32.4 million trainmovements for all trains in Sweden during the year of 2015,over 1.1 million departures, a comprehensive register of over80000 infrastructure elements, and almost 87 million weatherobservations. After identifying the most influencing factorson the punctuality intervention strategies can be derived re-garding timetables, change of operational parameters and in-frastructure design. This paper presents solutions to improvethe railway system that go beyond diagnostics and prognos-tics to round up the possibilities of PHM.

Besides the technical aspects, the paper by Singh, Majum-dar, and Kyriakidis analysis human errors occurring duringrailway maintenance activities. In detail, the human error as-sessment and reduction technique (HEART) is employed todetermine the probability of human errors for typical mainte-nance tasks, while fault tree analysis is used to define poten-tial mistakes throughout the maintenance process. Their find-ings might be a valuable source to revise policies and guide-lines in terms of improved and secure railway maintenancetasks.

We, the Guest Editors, are confident that this Special Issue onPHM for Railway Systems and Mass Transportation providesexciting insights into PHM solutions. The dissemination ofthe presented approaches and results will advance the stateof research and development in this field. The proposed con-cepts hopefully lead to a safer, more reliable and cost-efficientrailway transportation. We would like to express our sin-cere appreciation to the authors for their efforts in preparingand submitting these outstanding manuscripts. Likewise, wewould like to thank the reviewers for their time and thoughtfulfeedback on the submitted papers.

JOHN ANDREWS Guest EditorRoyal Academy of Engineering and Network Rail Chairof Infrastructure Asset ManagementDirector of the Lloyds Register Foundation Resilience En-gineering Research GroupUniversity of Nottingham, United Kindom

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THOMAS BOHM Guest EditorChief Data ScientistKonuxMunich, Germany

PIERE DERSIN Guest EditorPHM DirectorALSTOM Digital MobilitySt-Ouen, France

JORN C. GROOS Guest EditorTeam LeaderInstitute of Transportation SystemsGerman Aerospace Center (DLR)Braunschweig, Germany

RENE SCHENKENDORF Guest EditorPostdoctoral Researcher / Team LeaderInstitute of Energy and Process Systems EngineeringTU Braunschweig, Germany

BIOGRAPHIES

John Andrews has a degree in Mathemat-ics from the University of Birmingham anda PhD in Mechanical Engineering. Prior tohis appointment at the University of Not-tingham he worked for 20 years at Lough-borough University where his final post wasProfessor of Systems Risk and ReliabilityThe prime focus of his research has been

on methods for evaluating the system resilience, unavailabil-ity, unreliability and risk. Recent work has concentrated ondegradation modelling and the effects of maintenance, in-spection and renewal on railway asset performance. In 2005,he founded the Proceedings of the Institution of MechanicalEngineers, Part O: Journal of Risk and Reliability of whichwas the Editor-in-chief for 10 years. He is also a mem-ber of the Editorial Boards for 6 other international journalsin this field including: Reliability Engineering and SystemSafety and Quality and Reliability Engineering International.In 2011 he co-edited, along with Christophe Berenguer andLisa Jackson, Maintenance Modelling and Applications, pub-lished by ESReDA (European Safety Reliability and Data As-sociation). John has over 300 publications in the field of Risk,Reliability and Maintainability Engineering. In recent yearsJohn has been awarded the Moss Prize, the Ludwig MondPrize, the Donald Julius Groen Prize and the Charles SharpeBeecher Prize from the IMechE.

Thomas Bohm (Boehm) studied at Uni-versity of Magdeburg in Germany and theEPFL Lausanne in Switzerland. He holds aMaster in Computer Science in Engineeringand has joined the DLR (German AerospaceCenter) Institute of Transportations Systems

in 2007. Since then, his main field of research is condi-tion based maintenance and health management using statis-tics and data mining. In 2010, Thomas became leader ofthe Life Cycle Management team with whom he further ad-vanced the topic of efficient, predictive maintenance of rail-way infrastructure. He has several publications in this fieldand is session chairman of the International Congress of Con-dition Monitoring and Diagnostic Engineering Management.Thomas worked in and led several international and nationalprojects as well as development projects for diagnostic andprognostic algorithms for Deutsche Bahn and its asset mon-itoring platform DIANA. In 2015, he became Chief Officerand Head of the Department Data Management and Knowl-edge Discovery. In this role, Thomas was responsible for sen-sor system development, data management, monitoring, andsituation interpretation for road traffic and autonomous driv-ing as well as railway system. Since 2017, he is the ChiefData Scientist at KONUX, a company focusing on analyticsand predictive maintenance.

Pierre Dersin was born and raised in Bel-gium and graduated from the MassachusettsInstitute of Technology (MIT) with a Ph.D.in Electrical Engineering after receiving aMasters degree in Operations Research alsofrom MIT. He worked on reliability of largeelectric power networks, as part of the LargeScale System Effectiveness Analysis Pro-

gram sponsored by the US Department of Energy, from MITand Systems Control, Inc. After some time with FABRICOM(Belgium and U.S), involved with fault diagnostic systemsfor factory automation, he joined, in 1990, ALSTOM Trans-port in Paris,where he has occupied several positions, mainlyinvolved with RAMS and Maintenance, and he founded theRAM Center of Excellence . He is now RAM (Reliability-Availability-Maintainability) Director and PHM (Prognostics& Health Management) Director of ALSTOM Digital Mobil-ity. He has contributed a number of communications and pub-lications in scientific conferences and journals in the fieldsof RAMS, PHM, automatic control and electric power sys-tems (including IEEE Transactions on Automatic Control,IEEE Transactions on Power Apparatus & Systems, ESREL,almost every RAMS Symposium since 2008, several of theFrench Lambda-Mu conferences, the 2012 IEEE-PHM Con-ference and WSC 2013). He was a keynote speaker at theEuropean PHM Conference (Nantes) in 2014 and at the Prog-nostics and system Health Management conference in Harbin(China) in 2017. He served on the IEEE Reliability SocietyAdCom from 2012 to 2017 and as Vice-President, TechnicalActivities, in 2017. He chairs the IEEE Reliability SocietyTechnical Committee on Systems of Systems, and is also amember of the IEEE Future Directions Committee. He is thelead author of 4 chapters, including one on PHM, in the forth-

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coming Handbook of RAMS in Railways: Theory & Practice(Taylor & Francis).

Jorn C. Groos received his diploma in geo-physics from the University of Karlsruhe,Germany, in 2007 and his Dr. rer. nat. fromthe faculty of physics of the Karlsruhe Insti-tute of Technology in 2010. Since 2014 heis with the German Aerospace Center DLRat the Institute of Transportation Systems inBraunschweig, Germany. Jorn is leading the

Asset and System Monitoring group within the DepartmentData Management and Knowledge Discovery. The GermanAerospace Center is Associate Member of the European JointUndertaking Shift2Rail and Jorn is responsible for the DLRcontributions to the Innovation Programme 3 Cost-Efficientand Reliable High-Capacity Infrastructure. His current re-search interests include data acquisition, data analysis, and

machine learning for condition monitoring of railway assets.

Rene Schenkendorf received his Dipl.-Ing.and Dr.-Ing. in Engineering Cybernet-ics from the Otto-von-Guericke-UniversityMagdeburg, Germany in 2007 and 2014, re-spectively. From 2007 until 2012 he hadbeen a Ph.D. student at the Max Planck In-stitute for Dynamics of Complex Techni-cal Systems in Magdeburg, Germany. From

2013 until 2016 he was with the German Aerospace Centerat the Institute of Transportation Systems in Braunschweig,Germany. Since 2016 Rene is the head of the Process Sys-tems Engineering group at the Institute of Energy and ProcessSystems Engineering at the Technische Universitat Braun-schweig, Germany. His current research interests include dataanalysis, statistical process control, uncertainty analysis, andmodeling in PHM.

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