eMaintenance for Railway

19th Nordic Seminar on Railway Technology

14th-15th September 2016

Clarion Hotel Sense, Luleå

What is eMaintenance?

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Presentation Outline

• The eMaintenance Grail

• Hypes & Trends

• The Railway Cloud

• Conclusions

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Hypes & Trend

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Hypes & Trends

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Hypes & Trends in Railway

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Services

Cloud

computing

Digitalisati

on

Analytics

Big Data

IoE

Context-

adaptive

Business

Intelligence

Safety &

Security

Smart

Asset

Crowd-

sourcing

IoT

APPs

Sharing

Storage

Holo-

graphic

Virtual

reality

Augmented

reality

…

Distributed

computing

Deep

learning

Quantum

computing

Artefacts

• What does these mean for Railway?

• How to manage and utilise these computing artefacts?

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What Do We Want to Achieve

By using advanced

computing & information logistics!

Context Assumptions Actions Results

Are we doing things right?

Are we doing right things?

How do we decide right things?

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Changes in Business Models

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Product Focus Customer Focus

“Total Care Solutions”

“Power-per-hour”

“Performance Based

Logistics”

“Profit without machines”

“Gold Care Services”

“Creating additional value

for our customers”

“Functional Products”

[ramin.karim@ltu.se]

Services creates

additional value

to products!

Complexity in Asset Management – Lifecycle Perspective

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Concept ProductionDevelopment Utilization Support Retirement

(ISO/IEC, 2002; IEC 2001)

Concept ProductionDevelopment Utilization Support Retirement

Concept ProductionDevelopment Utilization Support Retirement

Concept ProductionDevelopment Utilization Support

Maintenance Decision-Making

• Provide Business Intelligence (BI) for enhanced maintenance

decision-making!

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Maintenance DNA – A System Perspective

AnalyticsData

Mechanical

components

Electrical

components

Software

components

Human

components

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Forecasting

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Smart Asset

• Ability to reason, discover

meaning, generalise, or learn from

past experience (EB, 2009)

• Intelligent transport services and

systems should, among other

things, be able to adapt to new

situations (Candell et al., 2009)

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Smart Business

Smart Operation & Maintenance

Smart Control System

Smart Asset

Internet of Things (IoT)

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Source: Cisco

Internet of Everything (IoE)

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Source: Cisco

Cloud Computing in Railway

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IoT & Cloud

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Artefacts

• What does these mean for Railway?

• How to manage and utilise these computing artefacts?

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An Approach to New Know in Maintenance

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Maintenance Analytics (MA) – A Framework

• Now casting

– 1) What happened in the past

– 2) Why something happened

• Forecasting

– 3) What will happen in the future

– 4) What need to be done next

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(Karim et al., 2016)

What do railway maintenance benefits?

Computing artefact

• More processing capability

• More storage capability

• More communication capability

• More integration/fusion capability

• More of more…

Expected Impacts on maintenance

• Intelligence to Asset

• Fact-based decision support

• Enhance analytics

• Distribute analytics

• Provide real-time DS, from batch to streaming analytics

• From centralised to distributed

• Not only work-order

• Improved logistics

• Improved control system

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The Railway Cloud

A cloud-based analytics platform

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eMaintenance

On-going projects

The Railway Cloud objective

• Maintenance Decision Support– When, what, how, who

– Information integration & service fusion

• Support to Integrated Logistic Support (ILS)

• Enablement of Predict-and-Prevent (PAP) instead of Fail-and-Fix (FAF)

• Prediction of Remaining Useful Life (RUL)

• Reduction of No-Fault-Found (NFF)

• Enablement of knowledge discovery and information reuse

• Reduction of costs during a system lifecycle

• Increased asset dependability

• ...

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Context-aware eMaintenance Decision Support Solution

Data Fusion &

Integration

Big Data

Modelling &

Analysis

Context sensing

& adaptation

Information

modelsKnowledge

models

Context

models

Maintenance

Data

The Conceptual Model

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(Karim et al., 2014)

Context modelling

• Modelling

– Decisions

– Activities

– Actors

• Context

– Describing

– Modelling

– Sensing

– Matching

• Modelling of visualisation & interaction

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Overarching Railway Cloud Architecture

• Cloud Services– IaaS (Infrastructure)

• Virtual Server SRV

– PaaS (Platform)• Development tools

– SaaS (Apps)• Data Acquisition SRV

• Data Transformation SRV

• Data Integration SRV

• Data Quality SRV

• Data Storage SRV

• Data Processing SRV

• Data Visualisation SRV

• Local Services– Service desk

– Overall management

– Project coordination

– Provision of Non-cloudified SW

– Client-depended tools, e.g. visualisation SW and HW

• Project Specific Tools

• Sensors

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Conclusions

Big Data Cloud

Context-

adaptation

IoT/IoE

Crowd-

sourcing

AnalyticsGovernance

NoSql

Sensors &

CloudsVelocity

Volume

Variety

Structured &

unstructured

Behaviour

Cloud First

Mobile First

SecurityVisuali-

sation

HologramsRepairability

Survivability

OO SOA

Machine

Learning

Information

Logistics

Sensor

fusion

System

thinking

Diagnostics Prognostics CM System

CMMSCM

ComponentDigitalisation

Sensor

technology

Manage-

ment

Virtual

reality

Augmented

realityOntology

Taxonomy

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Challenges

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• We need a

research discipline

dealing with

computing

challenges such

as:

A Future Scenario

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IoT for

Vehicles

Thank You for Your Attention!

So what is eMaintenance?

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