Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23638
Title: Fault diagnosis of car assembly line based on fuzzy wavelet kernel support vector classifier machine and modified genetic algorithm
Authors: Wu, Q
Law, R 
Wu, S
Keywords: Triangular fuzzy number
Support vector classifier machine
Wavelet analysis
Genetic algorithm
Fault diagnosis
Issue Date: 2011
Publisher: Pergamon Press
Source: Expert systems with applications, 2011, v. 38, no. 8, p. 9096-9104 How to cite?
Journal: Expert systems with applications 
Abstract: This paper presents a new version of fuzzy wavelet support vector classifier machine to diagnosing the nonlinear fuzzy fault system with multi-dimensional input variables. Since there exist problems of finite samples and uncertain data in complex fuzzy fault system, the input and output variables are described as fuzzy numbers. Then by integrating the fuzzy theory, wavelet analysis theory and v-support vector classifier machine, fuzzy wavelet v-support vector classifier machine (FW nu-SVCM) is proposed. To seek the optimal parameters of FW nu-SVCM, genetic algorithm (GA) is also applied to optimize unknown parameters of FW nu-SVCM. A diagnosing method based on FWv-SVCM and GA is put forward. The results of the application in car assembly line diagnosis confirm the feasibility and the validity of the diagnosing method. Compared with the traditional model and other SVCM methods, FW nu-SVCM method requires fewer samples and has better diagnosing precision.
URI: http://hdl.handle.net/10397/23638
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2010.12.109
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