Using search for engineering diagnostics and prognostics Jim Austin.

Using search for engineering diagnostics and prognostics

Jim Austin

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

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?

Fault identification

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Correlationmatrix

Time seriesdata

Dataconverter

Compare

Historicaldata

Known?

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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