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Anomaly Detection Using Non-Parametric Information

[+] Author Affiliations
Anil Varma, Piero Bonissone, Weizhong Yan, Neil Eklund, Naresh Iyer, Stefano Bonissone

GE Global Research Center, Niskayuna, NY

Kai Goebel

NASA Ames Research Center, Moffett Field, CA

Paper No. GT2007-28011, pp. 813-821; 9 pages
  • ASME Turbo Expo 2007: Power for Land, Sea, and Air
  • Volume 1: Turbo Expo 2007
  • Montreal, Canada, May 14–17, 2007
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-4790-X | eISBN: 0-7918-3796-3
  • Copyright © 2007 by General Electric Company


Diagnostics and prognostics is particularly challenging in systems with a restricted suite of sensors; e.g., in aircraft engines where harsh operating conditions, weight considerations, and regulatory concerns limit the number of sensors. In this paper, we investigate anomaly detection techniques subject to these constraints. Specifically, we use as input to these techniques only controller-generated, log-data for the system. While such log-data is not designed to carry predictive information related to system health, we show that it is possible to extract early warning signals related to the failure of the system by looking for the presence or onset of anomalous or novel patterns in the log-data. We present preliminary results obtained by the application of this approach to some complex systems. We also provide a roadmap for extending this approach by the incorporation of minimal amount of system-specific knowledge of the kind that is typically available for complex systems. This extension is expected to strengthen the applicability of the approach to diagnostic and prognostic analysis at the level of the system components, as well as to the estimation of the root cause of a detected system anomaly.

Copyright © 2007 by General Electric Company



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