emaintenance for Railway 19 th Nordic Seminar on Railway Technology 14 th -15 th September 2016 Clarion Hotel Sense, Luleå

Transcription

1 emaintenance for Railway 19 th Nordic Seminar on Railway Technology 14 th -15 th September 2016 Clarion Hotel Sense, Luleå

2 What is emaintenance?

3 The emaintenance Grail Presentation Outline Hypes & Trends The Railway Cloud Conclusions

4 Hypes & Trend

5 Hypes & Trends

6 Hypes & Trends in Railway Smart Asset IoT Deep learning IoE Analytics APPs Virtual reality Business Intelligence Digitalisati on Services Sharing Safety & Security Big Data Crowdsourcing Holographic Contextadaptive Cloud computing Storage Quantum computing Distributed computing Augmented reality

7 Artefacts What does these mean for Railway? How to manage and utilise these computing artefacts?

8 What Do We Want to Achieve Context Assumptions Actions Results Are we doing things right? Are we doing right things? How do we decide right things? By using advanced computing & information logistics!

9 Changes in Business Models Product Focus Customer Focus Services creates additional value to products! Creating additional value for our customers Power-per-hour Performance Based Logistics Total Care Solutions Gold Care Services Profit without machines Functional Products

10 Complexity in Asset Management Lifecycle Perspective Concept Development Production Utilization Suppo Concept Development Production Utilization Support Retirement Concept Development Production Utilization Support Retirement Concept Development Production Utilization Support Retirement (ISO/IEC, 2002; IEC 2001)

11 Maintenance Decision-Making Provide Business Intelligence (BI) for enhanced maintenance decision-making!

12 Maintenance DNA A System Perspective Data Analytics Mechanical components Electrical components Software components Human components [ramin.karim@ltu.se] 12

13 Forecasting

14 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) Smart Business Smart Operation & Maintenance Smart Control System Smart Asset

15 Internet of Things (IoT) Source: Cisco

16 Internet of Everything (IoE) Source: Cisco

17 Cloud Computing in Railway

18 IoT & Cloud

19 Artefacts What does these mean for Railway? How to manage and utilise these computing artefacts?

20 An Approach to New Know in Maintenance

21 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 (Karim et al., 2016) [ramin.karim@ltu.se] 21

22 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

23 The Railway Cloud A cloud-based analytics platform [ramin.karim@ltu.se] 23

24 emaintenance On-going projects

25 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 [ramin.karim@ltu.se] 25

26 The Conceptual Model Context-aware emaintenance Decision Support Solution Information models Knowledge models Context models Maintenance Data Data Fusion & Integration Big Data Modelling & Analysis Context sensing & adaptation (Karim et al., 2014)

27 Modelling Decisions Activities Actors Context Describing Modelling Sensing Matching Context modelling Modelling of visualisation & interaction

28 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 Noncloudified SW Client-depended tools, e.g. visualisation SW and HW Project Specific Tools Sensors

29 Conclusions Management CM Component CMMS Digitalisation Virtual reality Sensor technology Machine Diagnostics Prognostics CM System Learning Taxonomy Information Logistics Sensor fusion OO SOA System thinking Ontology Augmented reality Governance IoT/IoE Big Data Cloud Analytics Security Visualisation Contextadaptation Crowdsourcing Sensors & Clouds Velocity Cloud First Repairability Holograms Volume Mobile First Structured & unstructured Survivability Variety Behaviour NoSql

30 Challenges We need a research discipline dealing with computing challenges such as: [ramin.karim@ltu.se] 30

31 A Future Scenario IoT for Vehicles

32 Thank You for Your Attention! So what is emaintenance?