Anomaly detection through a bayesian support vector machine

Abstract—This paper investigates the use of a one-class support vector machine algorithm to detect the onset of system anoma-lies, and trend output classification probabilities, as a way to mon-itor the health of a system. In the absence of “unhealthy ” (nega-tive class) information, a marginal kernel density estimate of the “healthy ” (positive class) distribution is used to construct an es-timate 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 ef-fort to manage false alarms. Index Terms—Anomaly detection, Bayesian linear models, Bayesian posterior class probabilities, kernel density estimation, one-class classifier, support vector machine. ACRONYMS PHM Prognostics and Health Management SVM Support vector machine OSH Optimal separating hyperplane PCA Principal component analysis SVD Singular value decomposition GLM Generalized linear model BLM Bayesian linear model PoF Physics of failure KDE Kernel density estimate RBF Radial basis function MAP Maximum a posteriori Minimum volume sets NOTATION Input vector Class label vector Training data matrix Model subspac