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HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium Nicolas FLIX, October 2018

HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

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Page 1: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

HOW BIG DATA BECOME INFORMATION THEN DECISIONS

FOR ASSET MANAGEMENT

UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

Nicolas FLIX, October 2018

Page 2: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 2

Condition monitoring, Prognosis & Health management 2

How Big Data become Information then Decisions for Asset Management

Internet-of-Rail and HealthHub 1

Page 3: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 3

Condition monitoring, Prognosis & Health management 2

How Big Data become Information then Decisions for Asset Management

Internet-of-Rail and HealthHub 1

Page 4: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 4

The Internet of rail

combines

Rail expertise and

Digital capabilities

Page 5: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 5

TrackTracer and preliminary

detection w/ thresholds

It takes time to master predictive maintenance and asset management

2006 2011

2012

2013

2014

2015

2016

2017

2018 2010

2019

TrainTracer

Motes & investment

in point machines

1st Health Indicator for detection

and diagnostics

HealthHub

Platform

HMI

CatenaryTracer TrainScanner

& Test bench simulations

Physics based

Prognostics

Demand

Optimizer

Asset Management

at system level (ISO 55 000)

ASSET MONITORING DIAGNOSTICS PROGNOSTICS DYNAMIC

MAINTENANCE

TrainTracer HealthHub Asset Management

Page 6: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 6

Factors influencing the type of assets that an organization requires to achieve its objectives:

• Nature and purpose of the organization ;

• Operating context ;

• Financial constraints and regulatory requirements ;

• Needs and expectations of the organization and its stakeholders.

Benefits of asset management:

• Improved financial performance ;

• Informed asset investment decisions ;

• Managed risk ;

• Improved services and outputs ;

• Demonstrated social responsibility ;

• Demonstrated compliance (legal, statutory and regulatory) ;

• Enhanced reputation ;

• Improved organizational sustainability ;

• Improved efficiency and effectiveness.

ASSET MANAGEMENT – ISO 55000 : 2014

Page 7: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 7

What is HealthHub?

Page 8: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 8

Condition monitoring, Prognosis & Health management 2

How Big Data become Information then Decisions for Asset Management

Internet-of-Rail and HealthHub 1

Page 9: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 9

Types of maintenance

PREVENTIVE

CORRECTIVE SYSTEMATIC CONDITION-BASED PREDICTIVE

When it fails Every day Upon low fuel

indication

Upon a measurement (gauge)

and a prognostic

NUMBER OF REFILLS Fewest Many Few Minimal and

planned

CAR AVAILABILITY Lowest Medium High High

BREAKDOWN RISK 100% Low Low Lowest

1 2 0

Page 10: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 10

Increased

System Availability

Reduced Maintenance Cost

(material & labour)

New tools, new processes ,

New ways of working

Periodic

Time or Mileage

based Maintenance

Periodic

Remote monitored

CBM

(Condition-based

Maintenance)

Remote monitored

Predictive Maintenance

Upon failure

Corrective

Maintenance

RCM (Reliability-

Centered

Maintenance)

HealthHub rationale and strategy

Page 11: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 11

KP

Is

HealthHub rationale and strategy

SYSTEMATIC (km & time-based)

CONDITION-BASED

Doors, HVAC, Brakes

Intrinsic Limit

Implementation complexity

PREDICTIVE

Traction, Gearbox

Crack inspection,

Couplers, Wipers

EXAMPLE

RELIABILITY

AVAILABILITY

COST

Remote Condition Monitoring mainly to improve reliability and to increase effectiveness & efficiency of maintenance tasks

Prognostics & Health Management to move to health-based, dynamic maintenance maximizing asset availability

Page 12: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 12

HealthHub Model

Asset Management at System level (ISO 55 000) with risk/cost model integrated

Maintenance strategy alignment

Health prediction / Machine Learning

Health assessment per Component / Sub-system

OPTIMIZATION

PREDICTION

Rule Engine & User Interface

Data Management

Point

Mach

On-

board

Way-

side

TrackTracer &

CatenaryTracer TrainScanner

TrainTracer

& Motes / others

CONDITION

MONITORING

DATA

CAPTURING

TRAINS INFRASTRUCTURE SIGNALLING

Page 13: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 13

Data Acquisition Tools

Track & CatenaryTracer Measure & diagnose: cracks,

radius, geometry, corrugation on

tracks and wear of wire, height and

stagger of catenary

Motes To monitor vibration

temperature and pressure

of several components

TrainTracer On-board data analysis

and train to ground

connectivity

TrainScanner For automatic train

inspection of wheelsets,

brake pads, pantograph

and train integrity

Page 14: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 14

HealthHub overview

• Integrate Data

• Develop Algorithms

• Create Rules

• Create Reports

• Analyse Statistics

• Provide Support

Central Experts Data Hub

PROJECT

Operation

data

Rule

Engine

EVENT STATUS POSITION

MMIS Service

Orders

Trains

Infrastructure

Signalling

GSM / LTE

Rule

Engine

Page 15: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 15

Sesto, One of our Fleet Support and Data Centres

Page 16: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 16

Sesto Data Centre handles over 480 trains from multiple fleets

218

3

150

3/4

14

5

121

4/5/6

19

7

19

7

17

7

25

11

53

1

SC

HE

DU

LE

D

500 min 240 min

30% 10%

100% 50%

2016 2017

Minutes of

Delay per month

% No Fault Found

% of Trouble

Shooting time *

* estimated

FLEET

COACHES

-50%

-65%

Base FPMK Target FPMK FPMK Obtained

FP

MK

Page 17: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 17

Under deployment

Deployed

HealthHub™ Worldwide Deployment

TrainTracer HealthHub Asset Management

2006 2011 2013 2015 2017 2019

2014 2016 2018 2012 2010

Infrastructure

Trains

Signalling

Page 18: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 18

HealthHub Data Factory: Alstom Smart data work bench

Asset

data

REPORT

published

in HealthHub

Process

Schedule

Purpose

Crunch data

Publish reports

Fine tune rules

Performance

80 reports per day

10 Tera Bytes analyzed each day

Process 1 Billion values per minute

HealthHub

Data

Factory

WEATHER HISTORY DAILY ALERTS CONDITION REPORTS DAILY ALERTS CONDITION REPORTS

Page 19: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 19

HealthHub achieved benefits

Reduced safety hazards Reducing risky intervention underframe and on the roof

Railway safety improved (automated integrity check)

Increased availability of the rail system Up to 30% decrease in train immobilisation time

Service Affecting Failure reduction on Trains / Infra

and Signalling.

Deployed on our maintenance contracts In place on Reims / WCML / NTV Maintenance.

Proposed for every new project

Useful life of the assets extended

+15% and more in the future

Cost reduced 15% reduction in material consumption

Increased staff productivity +25% in maintenance intervals (more in the future)

and better anticipation

Extended interval between maintenance

tasks Planned major maintenance moved from 20kMiles

to 50kMiles on going

TrainTracer HealthHub Asset Management

2006 2011 2013 2015 2017 2019

2014 2016 2018 2012 2010

Page 20: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 20

HealthHub data analysis and visualisation principle

REAL TIME DATA

MM

IS

Fleet Management

KPI & telematics

AS

SE

T M

AN

AG

EM

EN

T

& P

RE

DIC

TIV

E M

AIN

TE

NA

NC

E

Fleet usage optimization

Diagnostic/Prognostic

TrainTracer

TrackTracer

CatenaryTracer

Motes

TrainScanner

Point Machine DYNAMIC

MAINTENANCE

PLANNING

Rule engine

Alstom data scientists

HealthHub database

HealthHub Platform

Page 21: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 21

HealthHub Model

Asset Management at System level (ISO 55 000) with risk/cost model integrated

Maintenance strategy alignment

Health prediction / Machine Learning

Health assessment per Component / Sub-system

OPTIMIZATION

PREDICTION

Rule Engine & User Interface

Data Management

Point

Mach

On-

board

Way-

side

TrackTracer &

CatenaryTracer TrainScanner

TrainTracer

& Motes / others

CONDITION

MONITORING

DATA

CAPTURING

ROLLING STOCK INFRA SIGNALING

Page 22: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 22

PHM development Process

PHM user

requirem.

Technical

design

Virtual

prototyping

Bench

prototyping

Demonstrator

design

Field

Demonstrator

Identification

of key physical

parameters

and

target failure

mechanisms

Healthy condition

in context

Degraded condition

in context

HEALTH

INDICATORS

Test bench

Virtual

prototype

PHYSICS OF FAILURES & MACHINE LEARNING

Field data

from

Demonstrator

Technical docs

Experience

FMMEA

LCCA

Expert knowledge

Preliminary

architecture

(hardware & software)

Page 23: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 23

Physics of Failures and Machine Learning

Machine Learning

Extracting information from

data

Feature extraction and

selection:

Detection

Diagnostics (pattern

recognition)

Predicting future values

(prognostics)

Virtual Prototyping

Software-based model to simulate

the dynamics of an asset

Benefits:

High flexibility

Reduced experiment cost

Safe evaluation of extreme

states

Uncertainty

Accurate incorporation of

sources of uncertainty

Monte Carlo simulation

PHM user

requirem.

Technical

design

Virtual

prototyping

Bench

prototyping

Demonstrator

design

Field

Demonstrator

Page 24: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 24

From raw signals to Health Indicator

Raw signals Machine Learning

Model

Healt

h In

dic

ato

r

17 19 21 23 25 27 31 29

1

October 2017

2

0,3

Health Indicator

Too many false alarms or too

few detections

Requires high precision to

capture relevant variations

Alstom Know-how

HealthHub Data Factory

Customer requirements

Maintainer feedback

0 0.5 1 1.5400

600

800

1000

1200

1400

1600

04/11

25/11

30/11

Quantification of distance

between the observed state

and a healthy condition

Page 25: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 25

The Health Indicator: the heart of detection, diagnosis and prognosis

-100

10

-5

-4

-3

-2

-1

0

1

2

3

4

5

-10-5

0

5

10

Distance:

Health

Indicator

The Health Indicator measures

discrepancy between test (observed)

data and training data

The anomaly is visible with the Health

Indicator while the raw signals won’t

always show the anomalies

Page 26: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 26

HealthHub on selected components and behaviours

Track Catenary Point Machines

Track Circuits

Brakes Traction Bogie HVAC Toilets Doors Trains

Infrastructure Signalling

Page 27: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 27

PHM on point machines: case study and health indicator

𝑓1(𝐼) 𝑓2(𝐼) 𝑓3(𝐼) 𝑓4(𝐼) 𝑓5(𝐼) 𝑓𝑛(𝐼) 𝑓 = HI 𝑓…(𝐼)

Data capture

Feature

extraction

Detection

Diagnostics

Creating the Health Index for each machine

Creating the Health Indicator for each machine

Page 28: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 28

PHM on point machines: case study and health Indicator

BENEFITS Detecting potential failures

before they occur

Increasing interval between

interventions: from 1 month to

3 months

Reducing people

mobilization, therefore less

exposure to accidents

Saving time by geolocalizing

potential failures

Nominal behaviour Progressive deterioration Post-maintenance

behaviour

Healt

h In

dic

ato

r

Dec 28 Dec 29 Dec 30 Dec 31 Jan 1 Jan 2 Jan 4 Jan 3

2018

5500

0

Jan 5 Jan 6

2017

Maintenance

Intervention

Data capture

Feature

extraction

Detection

Diagnostics

𝑓 = HI2A 𝑓1(𝐼) 𝑓2(𝐼) 𝑓3(𝐼) 𝑓4(𝐼) 𝑓5(𝐼) 𝑓𝑛(𝐼) 𝑓…(𝐼)

Page 29: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 29

PHM on point machines: case study and health Indicator

Loss of tolerance

Rail deformation

Power supply

Feature values Lubrication

Data capture

Feature

extraction

Detection

Diagnostics

Using specific features to diagnose the problem

𝑓1(𝐼) 𝑓2(𝐼) 𝑓3(𝐼) 𝑓4(𝐼) 𝑓5(𝐼) 𝑓𝑛(𝐼) 𝑓 = HI 𝑓…(𝐼)

Page 30: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 30

PHM on HVAC: Context

-10%

drops

Working hours

Pow

er

(kW

)

0 600.0 300

Cooling Capacity

Coefficient Of Performance

Simulation of

HVAC operation over 591 working hours

Context of HVAC Energy consuming

High maintenance costs

When faulty Reduce fleet availability

Reduce passenger comfort

Clogged filter Cooling capacity -10%

COP degrades

Clean filter vs Clogged filter

Page 31: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 31

Filter life extended by

+91%

Threshold reached

on 9th August

PHM on HVAC: The Health Indicator and prognostics

Extended life time

Healt

h In

dic

ato

r

2017-03 2017-04 2017-05 2017-06 2017-07 2017-08 2017-09

Threshold

Learning window Prognostics horizon

Systematic maintenance

replacement date Prediction date

Predictive maintenance replacement date

Page 32: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 32

Future Perspectives

Adaptive PHM:

PHM algorithms adapt to the changing

environment, context & mission profile Machine learning

Dynamic Maintenance:

Adapt maintenance and assign tasks &

resources dynamically according to

operations needs Machine learning, optimization

Risk-based Asset Management:

Balance between cost (maintenance,

renewals) and risk (service affecting failures)

is achieved based on customer preferences Utility theory, decision theory, cost-benefit

analysis

Resilient Systems:

Systems that self-heal when detecting

degradations Artificial intelligence, control theory, reliability

engineering

TrainTracer HealthHub Asset Management

2006 2011 2013 2015 2017 2019

2014 2016 2018 2012 2010

Page 33: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

© ALSTOM SA, 2015. All rights reserved. Information contained in this document is indicative only. No representation or warranty is given or should be relied on that it is complete or correct or will apply to any particular project. This will depend on the technical and commercial circumstances. It is provided without liability and is subject to change without notice. Reproduction, use or disclosure to third parties, without express written authorisation, is strictly prohibited.

ALSTOM - 11/10/2018 – P 33

It is not about Big Data, but about Smart Data

Alstom started 12 years ago with data analysis

and connected assets

Alstom combines outstanding railway expertise

with advanced digital technologies

Alstom is a world leader in rail maintenance…

… Alstom is your partner in this journey

Key take-aways

Page 34: HOW BIG DATA BECOME INFORMATION THEN DECISIONS …...HOW BIG DATA BECOME INFORMATION THEN DECISIONS FOR ASSET MANAGEMENT UNLV Railroad Infrastructure Diagnosis and Prognosis Symposium

www.alstom.com