TECH TOC Conference 9 10 April 2019 Artificial ... · Operational excellence key figures Replay -...

TECH TOC Conference

9 – 10 April 2019

Artificial Intelligence, Automation & DigitalizationGerhard Fischer, Siemens AG, Germany

siemens.com/cranesUnrestricted © Siemens AG 2019

Unrestricted © Siemens AG 2019

09.04.2019Page 2 TOC Asia Singapore 2019Information Classification: General

Agenda



Automated Terminal

Requirements to Suppliers

• Increased and defined

safety level

• Increased productivity

Safety

Zero Harm

• Verify performance

• Virtual testing

• Training M&R

Simulation

Risk mitigation

Faster go live

• Predictive maintenance

• Reduction of down times

• Operation optimization

Digital Service

Higher Availability

Integration &

Standardization

Risk & OPEX

reduction

• Hardware minimization e.g.

sensors

• Interface clarity

• Faster go live

Artificial

Intelligence

Performance

• New automation

applications

• Higher performance

• Faster development cycles

1



Automated Terminals

Artificial Intelligence


safety level


Safety

Zero Harm

• Early verification of

performance

• Virtual testing

• Training M&R

Simulation

Risk mitigation

Faster go live

• Predictive Maintenance



Digital Service

Higher Availability

Integration &

Standardization

Risk & OPEX

reduction


sensors


• Faster go live

Artificial

Intelligence

Performance

• New automation

applications



1



Automated Terminals

Simocrane Modules

2



Example

Complexity Targets for Truck Positioning System

Empty 42'

45'

20' Front

20' rear

2 x20' (tight)

40'

2 x20' (extend)

20' Pos 1(Front)

20' Front 20' Rear

20' Pos 3(Rear) 40' Pos 5(Middle)

40'

45'

45' Pos 5(Middle)

Empty 45'

20' Middle

Offloading order tier 1CAS Job order

Possible trailer

configuration

underneath the

ARMG

38

37

36

35

34

33

32

Ca

se

Nr.

39

31 Configuration not valid Configuration not valid Configuration not valid Configuration not valid

Configuration not valid Configuration not valid Configuration not valid Configuration not valid

Configuration not valid Configuration not valid Configuration not validConfiguration valid

Configuration valid

Configuration validConfiguration valid

Configuration valid


Configuration not valid Configuration not validConfiguration not valid

Configuration not valid Configuration not valid

Configuration not valid






Configuration valid

Configuration valid




40'

20' Pos 2 (Front)

20' Front 20' Rear

20' Pos 4(Rear) 40' Pos 6(Middle)

45'

45' Pos 6(Middle)

Offloading order DST 40'/45' Trailer Tier 2CAS Job order

40'/45' Empty

40'

2 x20' + 1 x 20' front

2 x 20' + 1 x 20' rear

2 x40'

4 x20'

1 x20' middle

2 x20'

45'

2x45'

Possible trailer

configuration

underneath the

ARMG

48

47

46

45

44

43

42

40

49

41

Ca

se N

r.

Configuration valid



Configuration valid

Configuration valid

Configuration valid

Configuration validConfiguration valid



Configuration not valid Configuration not valid Configuration not valid




Configuration not valid Configuration not valid


2



Truck Positioning System

Siemens 3D Sensor

3D Sensor unit

Siemens servo motor

Laser

connector

2D Laser

scanner2nd Encoder

Absolute

EncoderSwivel platform

Fine adjustment

2



E.g. Road truck auto landing

via detection of twistlocks/pins

- Laser scanner dot cloud

- Data collection

- Object classification

- Parameter derivation

See also Charles R. Qi et. al.

„PointNet: Deep learning on point

sets for 3D classification and

segmentation“


Pattern Recognition

2




Detection rates Conventional vs Machine Learning

0

0.2

0.4

0.6

0.8

1.0

Solution maturity

D0 initial detection rate

DS = 0.7..0.8

DS saturated detection rate

D0

Conventional algorithm

(programmed use cases)

Machine learning

(AI)

DS = ca. 1

Road truck (RT)

detection rate

2



Automated Terminals Requirements

Faster Go Live through Simulation


safety level


Safety

Zero Harm


performance

• Virtual testing

• Training M&R

Simulation

Risk mitigation

Faster go live




Digital Service

Higher Availability

Integration &

Standardization

Risk & OPEX

reduction


sensors


• Faster go live

Artificial

Intelligence

Performance

• New automation

applications



3



Virtual Model Virtual Commissioning

Mechanical & Sensor Simulation

Always available

Faster prototyping

Lower investment

Faster adaption to changes

Pre Commissioning Tool

Virtual Test Environment

Verification of

Design

Functionality

KPIs

Training platform

Simulation Horizontal Transport

▪ Identify bottlenecks

▪ Avoid over engineering

▪ Match contractual KPIs

Block Management System

▪ Peak smoothening

▪ Faster load & unload

▪ Energy saving

Virtual Logistics

Simulation & Emulation3



Step-wise Deployment Simulation/Emulation

3

TOS

Block Management SystemBMS

ASC/ARMG/ARMG Advanced Automation AA

Load Collision Prevention LCPS

Final Landing System FLS

Crane Management System CMS

Remote OperationCameras RCOS

Optical CharacterRecognition OCR

Basic Crane Control CC- Drive system- Positioning H/T/G- Micromotion- Crane anti-collision- Anemometer- Load measurement

SIMIT

UNITY

Visualization of

crane motion in

UNITY

Remote Operation System RCOS

Overall System

configuration




3

TOS









SIMIT

UNITY

Visualization of

crane motion in

UNITY


Test interface

TOS to BMS

1



UNITY


3

TOS









SIMIT

Visualization of

crane motion in

UNITY


3D modelling in

Unity environment

Of crane using

crane builder‘s

CAD step file

2



SIMIT


3

TOS









UNITY

Visualization of

crane motion in

UNITY


Simatic SIMIT

environment

of crane

automation system

3




3

TOS









SIMIT

UNITY

Visualization of

crane motion in

UNITY


Interaction BMS

with crane

automation system

4




3

TOS









SIMIT

UNITY

Visualization of

crane motion in

UNITY


Interaction BMS

with crane

automation system

and remote

operating station

5




3

TOS









SIMIT

UNITY

Visualization of

crane motion in

UNITY


Interaction TOS ,

BMS, crane

automation system

and remote

operating station

6



Transparency from the start

Early detection of problems

Early optimization of system

Easier design verification

Shorter project “go live”

Virtual Reality

InstallationConstruction Pre-COM COM SAT Design

Virtual SAT

Virtual Installation

Virtual Pre-Commissioning

Virtual Commissioning

Design Construction Installation Pre-COM COM SAT Operation

Operation

Shortened „Go Live“3



Automated Terminals Requirements

Digitalization


safety level


Safety

Zero Harm


performance

• Virtual testing

• Training M&R

Simulation

Risk mitigation

Faster go live




Digital Service

Higher Availability

Integration &

Standardization

Risk & OPEX

reduction


sensors


• Faster go live

Artificial

Intelligence

Performance

• New automation

applications



4



Simocrane CMS

Information Management

Alarm Logging Performance DataReplay

Functionality

Condition

Monitoring

Alarm message

system

Relevant

performance data

generation for further

KPI calculation;

Record all

performed

operations

Fast recording

- analog signals

- binary signals

Diagnosis

- fault diagnosis

- root cause analysis

Operational

excellence key

figures

Replay

- all data

- chronological order

Detect time-critical

switching states

4



Crane Condition Monitoring

Digital Crane „Finger Print“‘

CMS

RCMS

- Monitoring of crane components in time / frequency domain

- Large amount of data over time (1 hour = 1 mil. measuring points)

- Digital “’fingerprint”’ of the crane

4



SIMOCRANE CMS

Predictive Maintenance

Distribution of information

RCMS Clients

Data processing

Container

Terminal

WebClients

KPI

Ms. Excel

Reports

SIMOCRANE RCMS

Cloud for Industry

MindSphereCMS

CMS

CMS

CMS

CMS

KPI monitoring

Data Storage

Process Historian

RCMS V4.4

Microsoft

SQLMicrosoft

SQL

Structured Data

4

Predictive maintenance

KPI dashboard

Edge processing

(TM NPU)



Data/image preprocessing by TM NPU module

and transfered to the neural network

3

Static, trained

neural network

Crane camera images1

2

Results of the inference are sent

from TM NPU to the PLC

Function blocks in

the PLC evaluate

the results4

Simatic AI Application

Basic Processing Module TM NPU

PLC sends sensor data to module1

Designed & trained in open

source machine learning

frameworks:

Tensorflow

Caffe

Other possible inputs:

Audio,

Vibrations,

Signals, …

4



Thank you for your attention!

Gerhard Fischer

Head of Sales Harbour Cranes

Phone: +49 9131 98 4849

Mobile: +49 173 381 3850

E-Mail: [email protected]

siemens.com/answers

mailto:[email protected]

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TECH TOC Conference 9 10 April 2019 Artificial ... · Operational excellence key figures Replay -...

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