Fault diagnosis using support vector machine with an application in sheet metal stamping operations
✍ Scribed by Ming Ge; R. Du; Guicai Zhang; Yangsheng Xu
- Publisher
- Elsevier Science
- Year
- 2004
- Tongue
- English
- Weight
- 382 KB
- Volume
- 18
- Category
- Article
- ISSN
- 0888-3270
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✦ Synopsis
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.