Anomaly Detection Through a Bayesian Support Vector Machine

Author

Sotiris, Vasilis A. ; Tse, Peter W. ; Pecht, Michael G.

Author_Institution

Dept. of Mech. Eng., Univ. of Maryland, College Park, MD, USA

Volume

59

Issue

2

fYear

2010

fDate

6/1/2010 12:00:00 AM

Firstpage

277

Lastpage

286

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.

Keywords

Bayes methods; reliability; support vector machines; Bayesian support vector machine; anomaly detection; marginal kernel density estimate; one-class support vector machine algorithm; posterior probabilities; trend output classification probabilities; Bayesian methods; Condition monitoring; Costs; Degradation; Kernel; Physics; Principal component analysis; Prognostics and health management; Support vector machine classification; Support vector machines; Anomaly detection; Bayesian linear models; Bayesian posterior class probabilities; kernel density estimation; one-class classifier; support vector machine;

fLanguage

English

Journal_Title

Reliability, IEEE Transactions on

Publisher

ieee

ISSN

0018-9529

Type

jour

DOI

10.1109/TR.2010.2048740

Filename

5475445