using search for engineering diagnostics and prognostics
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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Using search for engineering diagnostics and prognostics
Jim Austin
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Overview
Problem domain Drivers - why we need better solutions Example applications Our approach Challenges
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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)
Prognostics and Diagnostics
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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
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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
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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
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Example Applications
Aero-engines & fixed assets Rail – track and carrage Roads
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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)
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Gas turbines
Typical problem Spot failure in good time (!) Spot maintenance issue ahead of time
Data is High frequency Large Complex
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Rail
Monitoring of both track and carriages Over 2000 alerts on a Thomas virgin voyager Aim is to reduce unplanned maintenance
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Rail
Track Look at data from track inspection systems Find if track is bent or broken and needs
maintenance
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Road
Monitoring for congestion problems Data from road ‘loops’ (flow and occupancy) Weather Accident reports
Adjust Traffic lights Variable message signs
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Road
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Hull road bus gate, York
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Road
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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
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SDE and CARMEN
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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
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Data entry
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Services
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Execution log
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Examples
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Search for signals
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Correlationmatrix
Time series data
Dataconverter
Compare
Historicaldata
Known?
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Fault identification
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Correlationmatrix
Time seriesdata
Dataconverter
Compare
Historicaldata
Known?
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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
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Summary
Data now available in large quantities Real opportunities to improve the systems
that are being built
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