Fault diagnosis using support vector machine with an application in sheet metal stamping operations

被引：114
|
作者：
Ge, M [1 ]
Du, R [1 ]
Zhang, GC [1 ]
Xu, YS [1 ]
机构：
[1] Chinese Univ Hong Kong, Dept Automat & Comp Aided Engn, Hong Kong, Hong Kong, Peoples R China
来源：
MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2004年 / 18卷 / 01期
关键词：
support vector machine (SVM); kernel-induced feature space; condition monitoring; fault diagnosis; sheet metal stamping;
D O I：
10.1016/S0888-3270(03)00071-2
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
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.
引用
收藏
页码：143 / 159
页数：17
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