The jour he journey i ney in r n railw ailway ay ana analytics - PowerPoint PPT Presentation

The jour he journey i ney in r n railw ailway ay ana analytics ytics po power ered by ed by AI: AI: Towar ards ds railw ailway ay 4.0 4.0 Professor Diego Galar Lulea University of technology Head of Maintenance & Reliability, Tecnalia

Data driven models in railway is well trodden territory

But here be the dragons!!, approaches fail to scale

What analytics can be performed on railway?

Analytics and expectations also change

Types of data analytics

Descriptive analytics

Types of data analytics

Diagnostic analytics RELEVANT FEATURES DATA PREPARATION UNIFIED Feature Data DATA selection Reduction FORMAT Historical and live data DATA MINING BLOCK Optimal thresholds UPDATE MAINTENANCE PLAN PROCESS For features PREDICTIVE ADVANCED MAINTENANCE PREDICTION ESTIMATION PROGNOSTICS

Types of data analytics

Predictive analytics:RUL prediction Feature of item n crosses boundary in time t+dt Bearing mounted by Contractor 2 Bearing mounted by Contractor 1 Feature of item n crosses boundary in time t RUL considering two features

Types of data analytics

Types of data analytics

The way forward

Where analytics should be performed? 16

Edge agents versus cloud centralized

AI workflow @edge

Huge gap between data science and O&M

What can I see in my data? Forecasting Now casting 3) What will happen 1) What has happened in the future 2) What is happening 4) When will it happen

Domain knowledge and physics sometimes is not in the data

The method, let us twin reality

The twin as a service provider 29

The picture of Dorian Gray

Digital Twin: A virtual instance for services

Digital Twin Solution Architecture

Di Digital gital twin twin bas based ed on O on OT

Digital Di gital twin twin bas based ed on O on OT Internet eMaintenanc e Cloud Server Machine Maintenance On board Wireless System Analytics Data Information Knowledge

Di Digital gital twin twin bas based ed on O on OT

Wha hat a t abou bout IT t IT sys systems? tems?

Tax axono onomies mies and and ontologies ontologies 1 2 Rule-1 FailureMode(?x) ^ hasHappened(?x, true) ^ Device(?y) ^ happenedAt(?x, ?y) ^ FailureMode(?z) ^ theEndFffectIs(?z, ?x) ^ FailureMode(?a) ^ theHighEffectIs(?z, ?a)?theDirectFailureCauseIs(?x, ?a) ^ hasHappened(?a, true) 2 1

TRANSFORMA TRANSFORMATIVE TIVE MAI MAINTE NTENAN ANCE CE SOL SOLUTIO UTIONS NS Inte Integration & A tion & Applica pplication of tion of Tec echno hnologies logies IT OT

Digital Digital twin twin OT/IT T/IT inte integration tion OT IT

Digital twin Digital twin OT/IT inte T/IT integration tion Internet eMaintenanc e Cloud Server Technical services Truck scheduling On board Wireless System Machine Maintenance Analytics Data Information Knowledge

The he Way ay Forw orwar ard All Digital Data Computing Power Growth Context Engines Sensemaking Algorithms Time

Conte Context xt-aw awar are e Mainten Maintenanc ance e Decisi Decision on Suppo Support t Solution Solution Digital twin based on context Information Knowledge Context models models models Data Fusion Big Data Context Maintenanc e & Modelling & sensing & Data Integration Analysis adaptation

Let us b Let us be car e careful big eful bigger = smar ger = smarter? ter? • tolerate errors? • discover the long tail and corner cases? • more data, more error (e.g., semantic heterogeneity) • still need humans to ask right questions, lack of analytics

Bl Blac ack Sw k Swan Lo an Losse sses • Loss Distribution • Tail events are rare – very little data • Typically strong model assumptions

Da Data d ta driv riven en or mode or model based? l based?

Ev Evolution olution of of the the Pr Proce ocess ss Knowledge Capture Design & Validation of products Technological Advance Digital Mockup Digital twin 3D 2D Integration of Product Design and O&M 80s 2000 2016….. 90s

Hybrid Hybrid & C & Conte ontext Dri xt Driven en Se Services vices Physics Hybrid models Data of failure driven based Context Driven Services Context Awareness

Di Digital gital twin twin hyb hybrid rid IT ET OT

Di Digital gital twin twin hyb hybrid rid

Hybrid Digital T Hybrid Digital Twin win OFFLINE PROCESS: VIRTUAL COMMISSIONING Historical Cloud computing Diagnosis records Physics- Synthetic Pre- Feature Hybrid based model data processing extraction engine Maintenance Prognosis planning CONTEXT Maintenance action Physical CM Pre- Feature Failure Trend Risk Risk mitigation asset data processing extraction detection analysis assessment / actuator ONLINE PROCESS: OPERATIONAL LEVEL ANALYSIS 51

Application of railway twins O&M information Data loop Virtual commissioning services Design feedback loop Virtual assets Service/Repair Shop

Some hints

Concluding remarks • Digital twins and Hybrid models are needed for virtual commisioning to deliver O&M services • O&M based on Data driven solutions can lead to catastrophic failures • Life extension is not possible with big data analytics • Manufacturers must provide the integration of systems and data • Digital twin 4.0 will consider evoltionary models and normality dynamics

die diego go.galar@l .galar@ltu.se tu.se die diego go.galar@tecnalia. .galar@tecnalia.com com