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Proceedings Paper

Bayesian anomaly detection in monitoring data applying relevance vector machine
Author(s): Tomoo Saito
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Paper 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.

Paper Details

Date Published: 15 April 2011
PDF: 8 pages
Proc. SPIE 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, 798148 (15 April 2011); doi: 10.1117/12.880403
Show Author Affiliations
Tomoo Saito, Shimizu Corp. (Japan)


Published in SPIE Proceedings Vol. 7981:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011
Masayoshi Tomizuka, Editor(s)

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