Strukton rail big data expo 2016
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Transcript of Strukton rail big data expo 2016
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David Vermeij21 september 20161.0
POSS® Switch Analyticsnew ways to prevent failures
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Presentation
• Introduction
• Welcome to Strukton’s world
• POSS® monitoring system
• Switches (point machines)
• Data science forpredictive maintenance
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Introduction
D.J. (David) Vermeij MSc.Manager R&D - Strukton Rail
Education1992-1998 TU Delft : fac. of civil engineering, dep. of railway engineering
Experience1998-2006 Movares : project manager HSL2006-2012 Strukton Rail : tendermanager maintenance (procurement)2012-2015 Strukton Rail : contract manager maintenance (operation)2015-present Strukton Rail : manager R&D
Responsible for the innovation program at Strukton RailPartner in European Research Projects
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Strukton
1921 Established under the name: “ NV Spoorwegbouwbedrijf ”, as a subsidiary company of Dutch Railways (NS)
1974 Renamed to “ Strukton ” after the merger with the Danish company Christians & Nielsen
2010 Acquisition by Oranjewoud NV, a private entity of Sanderink Invest
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Strukton’s World
Railsystems
813
CivilInfra-structure618
Technique & Buildings
345
Revenu per market 2014(millions of Euros)
Strukton Rail develops, builds, installs and maintains rail systems, ensuring optimum availability, reliability, safety and measurability .
• Maintenance, renovation and new construction of railways and rail systems
• Machines• Railway safety• Railway acquisition and data management• Train systems
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Railway maintenance
Facts and Figures24/7 maintenance (NL)
Operational Asset Management
• Define & Select: Your network and your network needs
• Measure & Monitor: How to get the data you need
• Data Management & Interface: How to manage data and make it user-friendly
• Analyse & Interpret: How to make sense of all that d ata
• Organise & Plan: How to put that data to good use
• Maintain & Feedback: How to keep improving your network.
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The challenge
Introduction of Performance based contracts (PGO) :
• Competition : need for continuous improvement
• Focus on availability : reduction of failures
• Focus on costs : preventive maintenance
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POSS® monitoring
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Worldwide
Strukton’s Preventive Maintenance and Fault Diagnosis System
Over 10.000 assets are monitored by POSS®
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point monitoring
Frequently obstructed movements due to:
• Poor adjustment of rolling construction
• Lack of grease on slide chairs
• Bent blades
• Electrical problems
(worn-out brushes, motor, etc.)
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Fault diagnosis
Locking problem Motor problem
Functional point model
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Data analysis
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The next step
Railway maintenanceTo achieve further reduction in failuresand optimize maintenance (costs),we need to make the next step towards predictive maintenance
To enable this we need a set of tools to detect and predict failures in a veryearly stage in order to be able to plan/act (as possessions are restricting).
The predictions are to be accurate, reliable and provide information about whatfailure mode is going to occur.
Data Science ?
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2015: Introducing Data Science
2015 : Pilot on POSS dataGoal: Discover what Data Science can bring to railway maintenance
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2015/2016: POC
2015/2016 : Proof of ConceptGoal: development of a failure prediction model
Testcase:algoritm based on T-7dys behaviour
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2016: Production
2016 : Development towards ProductionGoal (1): System architecture and code
Goal (2): Development of a ‘pipeline’
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2016: Failure prediction
2016 : Failure predictionGoal (3): Improvement of algoritm
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Lessons learned
• There´s lots of data
• Valuable information can be extracted
• (mechanical) failures can be predicted
• Enormous potential,we´ve just started
Think big, start small
Connecting data science todomain knowledge is essential
There’s always a correlation…. The challenge is to predict whatfailure mode will occur
Reliability and availability of (labelled) data is crucial
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Movie
46299 - Strukton - Point Monitoring_15092016_1080P.mp 4
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Contact
www.struktonrail.com
Inquiries:Yves Kusters
Mobile : +31 – 6 [email protected]
Westkanaaldijk 23542 DA UtrechtThe Netherlands
www.struktonrail.com