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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