期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 18, 期 1, 页码 143-159出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/S0888-3270(03)00071-2
关键词
support vector machine (SVM); kernel-induced feature space; condition monitoring; fault diagnosis; sheet metal stamping
This paper presents a new method for fault diagnosis using a newly developed method, support vector machine (SVM). First, the basic theory of the SVM is briefly reviewed. Next, a fast implementation algorithm is given. Then the method is applied for the fault diagnosis in sheet metal stamping processes. According to the tests on two different examples, one is a simple blanking and the other is a progressive operation, the new method is very effective. In both cases, its success rate is over 96.5%. In comparison, the success rate of the popular artificial neural network (ANN) is just 93.3%. In addition, the new method requires only few training samples, which is an attractive feature for shop floor applications. (C) 2003 Elsevier Ltd. All rights reserved.
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