Bayesian anomaly detection in monitoring data applying relevance vector machine

Tomoo Saito

doi:10.1117/12.880403

15 April 2011 Bayesian anomaly detection in monitoring data applying relevance vector machine

Tomoo Saito

Proceedings Volume 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011; 798148 (2011) https://doi.org/10.1117/12.880403
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2011, San Diego, California, United States

Abstract

A method for automatically classifying the monitoring data into two categories, normal and anomaly, is developed in order to remove anomalous data included in the enormous amount of monitoring data, applying the relevance vector machine (RVM) to a probabilistic discriminative model with basis functions and their weight parameters whose posterior PDF (probabilistic density function) conditional on the learning data set is given by Bayes' theorem. The proposed framework is applied to actual monitoring data sets containing some anomalous data collected at two buildings in Tokyo, Japan, which shows that the trained models discriminate anomalous data from normal data very clearly, giving high probabilities of being normal to normal data and low probabilities of being normal to anomalous data.

Citation Download Citation

Tomoo Saito "Bayesian anomaly detection in monitoring data applying relevance vector machine", Proc. SPIE 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, 798148 (15 April 2011); https://doi.org/10.1117/12.880403

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