Bättre beslutsstöd genom eUnderhåll

Resultatkonferens 2015

Ramin Karim

Presentation Outline

• Background – Maintenance decision support – eMaintenance as enabler

• Sub-projects

– ePilot – Trend analysis – ePilot – eMaintenance infra-structure

• Conclusions & questions

Ramin Karim

Maintenance decision-making • eMaintenance provides Business Intelligence (BI) for

enhanced maintenance decision-making!

Ramin Karim

Decision-making needs

eMaintenance supports the learning-process by utilising advanced computing & information logistics!

Context Assump- tions Actions Results

Are we doing things right?

Are we doing right things?

How do we decide right things?

Ramin Karim

eMaintenance - WHY • 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 • ...

Ramin Karim

eMaintenance - HOW

Context-aware eMaintenance Decision Support Solution

Data Fusion & Integration

Big Data Modelling &

Analysis

Context sensing & adaptation

Information models

Knowledge models

Context models

Maintenance Data

Ramin Karim

Sub-projects - Trend analysis - eMaintenance infra-structure

Ramin Karim

Project objectives

• ePilot – eMaintenance infra-structure – To develop and implement an

eMaintenance infra-structure for data and information sharing

• ePilot – Trend analysis – To develop an Applicaiton for trend analysis

of SJ-vehicles

Ramin Karim

ePilo

t eM

aint

enna

ce in

fra-

stru

ctur

e eP

ilot X

XX

Ramin Karim

Some of the data Providers

-80 -60 -40 -20 0 20 40 60 80-35

-30

-25

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-5

0

5

10

[mm]

[mm]

wheel 1wheel 3wheel 4wheel 5wheel 6

-80 -60 -40 -20 0 20 40 60 80-30

-25

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-15

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-5

0

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10

15

[mm]

[mm]

S1002 wheel 5

eMaintenance Railway Cloud

Wheel Profile

Wheel Forces

• ID • Flange (height, thickness, slope)

• Rim thickness • Tread hollow • …

• ID • Vertical • Horizontal • Angle-of-attack • …

Vehicle Data

• ID • Mileage • …

Wheel Query Service

Force Data Analysis Service

Context Adaptation

Service

Data Fusion Service

MS

MS MS

MS

MD MD MD

Remaining Useful Life

Dynamic Maintenance Programme

Performance Measurement

Maintenance Support

Maintenance Planning

Proc

ess

Serv

ice

Dat

a

Example of eMaintenance Service Demonstrators

Expected impacts • Enablement of precise maintenance • Enablement of online diagnostics &

prognostics • Improved knowledge and information

sharing • Implementation of a collaborative platform • Enablement of a ’Railway App Platform’ • Materialisation of research contribution to

real-world applicaitons

Ramin Karim

Förväntade effektmål Färre störningar Kortare driftstopp Förbättrad tillgänglighet Säkrare och effektivare underhåll Förbättrad kvalitet

• Genom effektiv informationslogistik för

beslutsstöd

Ramin Karim

Thank You!