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Data Mining for Anomaly DetectionThe Vehicle Integrated Prognostics Reasoner (VIPR) program describes methods for enhanced diagnostics as well as a prognostic extension to current state of art Aircraft Diagnostic and Maintenance System (ADMS). VIPR introduced a new anomaly detection function for discovering previously undetected and undocumented situations, where there are clear deviations from nominal behavior. Once a baseline (nominal model of operations) is established, the detection and analysis is split between on-aircraft outlier generation and off-aircraft expert analysis to characterize and classify events that may not have been anticipated by individual system providers. Offline expert analysis is supported by data curation and data mining algorithms that can be applied in the contexts of supervised learning methods and unsupervised learning. In this report, we discuss efficient methods to implement the Kolmogorov complexity measure using compression algorithms, and run a systematic empirical analysis to determine the best compression measure. Our experiments established that the combination of the DZIP compression algorithm and CiDM distance measure provides the best results for capturing relevant properties of time series data encountered in aircraft operations. This combination was used as the basis for developing an unsupervised learning algorithm to define "nominal" flight segments using historical flight segments.
Document ID
20130011182
Acquisition Source
Langley Research Center
Document Type
Contractor Report (CR)
Authors
Biswas, Gautam (Vanderbilt Univ. Nashville, TN, United States)
Mack, Daniel (Vanderbilt Univ. Nashville, TN, United States)
Mylaraswamy, Dinkar (Honeywell International, Inc. Golden Valley, MN, United States)
Bharadwaj, Raj (Honeywell International, Inc. Golden Valley, MN, United States)
Date Acquired
August 27, 2013
Publication Date
March 1, 2013
Subject Category
Avionics And Aircraft Instrumentation
Report/Patent Number
NASA/CR-2013-217973
NF1676L-16156
Funding Number(s)
CONTRACT_GRANT: NNL09AD44T
WBS: WBS 534723.02.03.07
CONTRACT_GRANT: NNL09AA08B
Distribution Limits
Public
Copyright
Public Use Permitted.

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