IEEE Trans. on Reliability, Vol. 59, No. 2, pp. 277-286, June 2010

Anomaly Detection Through a Bayesian Support Vector Machine

Vasilis A. Sotiris and Michael Pecht, Fellow, IEEE
Center for Advanced Life Cycle Engineering (CALCE)
University of Maryland
College Park, MD20742 USA.

Peter W. Tse
Department of Manufacturing Engineering & Engineering Management
City University of Hong Kong,
Kowloon, Hong Kong


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.

Index Terms—Anomaly detection, Bayesian linear models, Bayesian posterior class probabilities, kernel density estimation, one-class classifier, support vector machine.

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