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Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case StudyThe world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods
Document ID
20100033686
Document Type
Conference Paper
Authors
Das, Santanu (California Univ. Santa Cruz, CA, United States)
Srivastava, Ashok N. (NASA Ames Research Center Moffett Field, CA, United States)
Matthews, Bryan L. (SGT, Inc. Moffett Field, CA, United States)
Oza, Nikunj C. (NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 25, 2013
Publication Date
July 25, 2010
Subject Category
Air Transportation and Safety
Report/Patent Number
ARC-E-DAA-TN-1240
Meeting Information
KDD-2010: 16th ACM SIGKDD Confernece on Knowledge, Discovery and Data Mining(Washington, DC)
Funding Number(s)
CONTRACT_GRANT: NNA08CG83C
CONTRACT_GRANT: NAS2-03144
Distribution Limits
Public
Copyright
Public Use Permitted.

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