Using search for engineering
diagnostics and prognostics
Jim Austin
Overview
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Problem domain
Drivers - why we need better solutions
Example applications
Our approach
Challenges
Slide 2
Prognostics and
Diagnostics
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Find out what is wrong with some thing
Find out what may be about to happen
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Use data to achieve this, but deliver knowledge
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Wide applicability (not just engineering)
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Slide 3
Engineering problems
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Asset monitoring
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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
Slide 4
Engineering problems
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Response
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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
Slide 5
Drivers
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Why now?
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Data to Knowledge is a prime motivator
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Sensors are now robust small and reliable
Data collection is very cost effective (2Tb <
£200)
Large computing capability is now possible
Most easy wins have been achieved
Green agenda is forcing issues
Slide 6
Example Applications
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Aero-engines & fixed assets
Rail – track and carrage
Roads
Slide 7
Gas turbines
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High speed, rotating systems
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Typically very reliable
Used for air travel as well as pumps and
generators (oil and gas, marine, air, power)
Slide 8
Gas turbines
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Typical problem
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Spot failure in good time (!)
Spot maintenance issue ahead of time
Data is
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High frequency
Large
Complex
Slide 9
Rail
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Monitoring of both track and carriages
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Over 2000 alerts on a Thomas virgin voyager
Aim is to reduce unplanned maintenance
Slide 10
Rail
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Track
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Look at data from track inspection systems
Find if track is bent or broken and needs
maintenance
Slide 11
Road
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Monitoring for congestion problems
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Data from road ‘loops’ (flow and occupancy)
Weather
Accident reports
Adjust
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Traffic lights
Variable message signs
Slide 12
Road
Hull road bus gate, York
13
Road
Slide 14
Our approach
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Use historic data as a prediction of now
and the future
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Basically search the historic data
Use AURA neural network
Have a set of systems within Signal Data
Explorer
Share data and services through portals
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Building on CARMEN
Slide 15
SDE and CARMEN
Slide 16
Data compatibility
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Neural Data Format – NDF
Allow interoperability between:
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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
Slide 17
Data entry
Slide 18
Services
Slide 19
Execution log
Slide 20
Examples
Slide 21
Search for signals
Data
converter
Correlation
matrix
Time series
data
Historical
data
Compare
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Known?
Fault identification
Data
converter
Time series
data
Correlation
matrix
Historical
data
Compare
23
Known?
Challenges
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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
Slide 24
Summary
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Data now available in large quantities
Real opportunities to improve the systems
that are being built
Slide 25