One-class support vector machines: an application in machine fault detection and classification

Authors:
Hyun Joon Shin;Dong-Hwan Eom;Sung-Shick Kim
Affiliations:
Department of Industrial Information and Systems Engineering, Sangmyung University, Cheonan, Choongnam, 300-720, South Korea;Department of Industrial Systems and Information Engineering, Korea University, Seoul 136-701, South Korea;Department of Industrial Systems and Information Engineering, Korea University, Seoul 136-701, South Korea
Venue:
Computers and Industrial Engineering
Year:
2005

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Abstract

Fast incipient machine fault diagnosis is becoming one of the key requirements for economical and optimal process operation management. Artificial neural networks have been used to detect machine faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for machine fault detection and classification in electro-mechanical machinery from vibration measurements using one-class support vector machines (SVMs). In order to evaluate one-class SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data.