Car assembly line fault diagnosis model based on triangular fuzzy Gaussian wavelet kernel support vector classifier machine and genetic algorithm

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 some problems of Gaussian noise 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, Gaussian loss function and ν-support vector classifier machine, fuzzy Gaussian wavelet ν-support vector classifier machine (TFGW v-SVCM) is proposed. To seek the optimal parameters of TFGW v-SVCM, genetic algorithm (GA) is presented to optimize the unknown parameters of TFGW v-SVCM. A diagnosing method based on TFGW v-SVCM and GA is presented. 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, TFGW v-SVCM method requires fewer samples and has better diagnosing precision.