Fault diagnosis of car assembly line based on fuzzy wavelet kernel support vector classifier machine and modified genetic algorithm

Qi Wu, Chun Hung Roberts Law, Shuyan Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)


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 (FWv-SVCM) is proposed. To seek the optimal parameters of FWv-SVCM, genetic algorithm (GA) is also applied to optimize unknown parameters of FWv-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, FWv-SVCM method requires fewer samples and has better diagnosing precision.
Original languageEnglish
Pages (from-to)9096-9104
Number of pages9
JournalExpert Systems with Applications
Issue number8
Publication statusPublished - 1 Aug 2011


  • Fault diagnosis
  • Genetic algorithm
  • Support vector classifier machine
  • Triangular fuzzy number
  • Wavelet analysis

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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