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Using search for engineering diagnostics and prognostics

Using search for engineering diagnostics and prognostics. Jim Austin. Overview. Problem domain Drivers - why we need better solutions Example applications Our approach Challenges. Prognostics and Diagnostics. Find out what is wrong with some thing Find out what may be about to happen

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Using search for engineering diagnostics and prognostics

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  1. Using search for engineering diagnostics and prognostics Jim Austin

  2. Overview Problem domain Drivers - why we need better solutions Example applications Our approach Challenges

  3. Prognostics and Diagnostics • Find out what is wrong with some thing • Find out what may be about to happen • Use data to achieve this, but deliver knowledge • Wide applicability (not just engineering)

  4. Engineering problems • Asset monitoring • Large numbers of sensors • Many types of sensors • Distributed sensors and systems • Possibly hostile domains • Large data rates • Slow connections • Data incomplete, noisy hard to characterise

  5. Engineering problems • Response • Needs to be rapid • Qualified response (i.e. how good) • Must include users in the loop, not yet automatic • Conclusion must be justified – able to dig into problem

  6. Drivers • Why now? • Sensors are now robust small and reliable • Data collection is very cost effective (2Tb < £200) • Large computing capability is now possible • Data to Knowledge is a prime motivator • Most easy wins have been achieved • Green agenda is forcing issues

  7. Example Applications Aero-engines & fixed assets Rail – track and carrage Roads

  8. Gas turbines • High speed, rotating systems • Typically very reliable • Used for air travel as well as pumps and generators (oil and gas, marine, air, power)

  9. Gas turbines • Typical problem • Spot failure in good time (!) • Spot maintenance issue ahead of time • Data is • High frequency • Large • Complex

  10. Rail • Monitoring of both track and carriages • Over 2000 alerts on a Thomas virgin voyager • Aim is to reduce unplanned maintenance

  11. Rail • Track • Look at data from track inspection systems • Find if track is bent or broken and needs maintenance

  12. Road • Monitoring for congestion problems • Data from road ‘loops’ (flow and occupancy) • Weather • Accident reports • Adjust • Traffic lights • Variable message signs

  13. Road Hull road bus gate, York

  14. Road

  15. Our approach • Use historic data as a prediction of now and the future • Basically search the historic data • Use AURA neural network • Have a set of systems within Signal Data Explorer • Share data and services through portals • Building on CARMEN

  16. SDE and CARMEN

  17. Data compatibility • Neural Data Format – NDF • Allow interoperability between: • Multi-channel systems data (.mcd). • Comma delimited (.csv). • Alpha map (.map). • Neural event (.nev). • NeuroShare native (.nsn). • Nex (.nex). • PC spike2 (.smr). • Plexon data (.plx). • TDT data format (.stb) • Supported in visualisation tool (SDE), soon in services

  18. Data entry

  19. Services

  20. Execution log

  21. Examples

  22. Search for signals Data converter Correlation matrix Time series data Historical data Compare Known?

  23. Fault identification Data converter Correlation matrix Time series data Historical data Compare Known?

  24. Challenges Best practice in data collection – build system when you know how to process it! Better tools, for analysis of signals, images and text (three main groups). Better collaborative technologies, new in industry sector User adoption of the technology

  25. Summary • Data now available in large quantities • Real opportunities to improve the systems that are being built

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