Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10523112 | Computers & Industrial Engineering | 2005 | 14 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Engineering
Industrial and Manufacturing Engineering
Authors
Hyun Joon Shin, Dong-Hwan Eom, Sung-Shick Kim,