Journal of Transaction on Reliability, June 2010

Anomaly Detection Through a Bayesian Support
Vector Machine

Vasilis A. Sotiris
Peter Tse
Michael Pecht

Prognostics Health Management Group
Center for Advanced Life Cycle Engineering (CALCE)
Department of Mechanical Engineering
University of Maryland
College Park, MD 20742

Abstract:

This paper investigates the use of a one-class support vector machine algorithm to detect the onset of system anomalies, and trend output classification probabilities, as a way to monitor the health of a system. In the absence of unhealthy (negative class) information, a marginal kernel density estimate of the healthy (positive class) distribution is used to construct an estimate of the negative class. The output of the one-class support vector classifier is calibrated to posterior probabilities by fitting a logistic distribution to the support vector predictor model in an effort to manage false alarms.

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