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Pattern Matching in DAME using AURA technology

Pattern Matching in DAME using AURA technology. Jim Austin, Robert Davis, Bojian Liang, Andy Pasley University of York. Overview. Context AURA technology DAME pattern matching problem AURA solution Search performance Next steps. Context. Vibration data from all engines in flight

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Pattern Matching in DAME using AURA technology

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  1. Pattern Matching in DAME using AURA technology Jim Austin, Robert Davis, Bojian Liang, Andy Pasley University of York

  2. Overview • Context • AURA technology • DAME pattern matching problem • AURA solution • Search performance • Next steps

  3. Context • Vibration data from all engines in flight • Detection of unusual vibration patterns • Novelties, anomalies • Automatic or manual • Search for similar vibration behaviour • Need to search large volumes of historical vibration data • Investigate search results and associated data • Service data records • CBR tools: Sheffield

  4. AURA technology • AURA • Proven technology for searching large data sets • Ability to scale and maintain performance • Easily parallelised • Examples • Address matcher • Molecular matcher • Operation • Vectors compared to stored examples • Uses bit level comparison methods • Correlation Matrix Memory operations

  5. AURA architecture CandidateSelector binary AURA SearchEngine Search Data Adaptor Input pattern Store & Search Store Output pattern Store Result Results Indexer Store & Search Candidate Engine (Back check) Indexes or Data

  6. AURA storage & recall binary AURA SearchEngine Input pattern Output pattern 2 1 0 2 0 0 0 Correlation Matrix Memories * *

  7. AURA software • AURA re-designed • To improve performance of the AURA library in terms of both memory usage and search times • 3 fold reduction in memory • 3 fold reduction in search time • To make the library easy to use • Simple API • Typically only 4 or 5 API calls used • Enable implementation as an OGSI GT3 service • To engineer the library to commercial software standards • Comprehensive user guide and reference manual

  8. Pattern matching problem • Vibration data from sensors forms Z-mod data. • Tracked orders extracted from Z-mod data Tracked order Frequency Amplitude Time Time

  9. Pattern matching problem • Novelty or anomaly identified in tracked order data by feature detectors Forms Query sub-sequence

  10. Pattern matching problem • Search for sub-sequences similar to the query in a large volume of tracked order data. • Need to investigate all possible alignments • Benchmark method is sequential scan • Noisy data: imprecise matching required • Various possible similarity measures • Euclidian distance • Correlation

  11. AURA solution EncodedQuery AURA SearchEngine Encoded Time Series Candidate Matches QueryTime Series Stored Time series AURABackcheck Results

  12. AURA solution • Encoding: reduction in dimensionality • e.g. from 100pts to 10 values. • Approximate search • From ~ 1,000,000s of alignments down to ~1000s of candidate matches • Backcheck • From ~1000s candidate matches to 100 or fewer results

  13. Encoding technique • Piecewise Aggregate Approximation • Values encoded using integer bins

  14. Search efficiency • Approximate search using AURA • Fast method of discarding poor matches • AURA search typically an order of magnitude or more faster than sequential scan. • Candidate matches typically <1% of total. • Back check stage very efficient due to reduction in volume of data • typically 1% or less of processing time for full sequential scan.

  15. Data size • Assume • Fleet of 100 aircraft, 4 engines each • Flying 10 hours per day • 5 data points per tracked order per second • 4 bytes per data point • Totals • approx. 100 GigaBytes per year per tracked order • Roughly 10 tracked orders of interest so… • Total approx. 1 TeraByte per year

  16. Search performance • Deployed system assumptions • 100 CPUs 2GHz each with 1GByte RAM. • One per aircraft • Each search needs to check 25,000,000,000 alignments of the query per year of tracked order data. • Sequential scan • Measured at approx. 2 seconds for 5,000,000 alignments of a 100 data point query (one CPU). • Extrapolates to approx. 500 seconds to search 5 years of data assuming 1 CPU per aircraft • This is too slow!  Need to support multiple searches and searches on more than one tracked order.

  17. Search performance • Using AURA and PAA based approach • Search time reduced by approx an order of magnitude. • Can search 5 years of data for 100 aircraft in approx:50 seconds • Believe this to be a workable solution  • But response times potentially slower than this • Need to handle a number of searches in parallel • Communications and other overheads

  18. Next steps • Technology • Refine similarity measures and encoding methods. • Architecture • Develop additional services to distribute and organise the search • Support multiple searches in parallel • Measurement • Perform scaling trials on engine data • Obtain better estimates of overall performance • Multiple searches • Overheads

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