Complex system fault diagnosis based on a fuzzy robust wavelet support vector classifier and an adaptive Gaussian particle swarm optimization

Research output: Journal article publicationJournal articleAcademic researchpeer-review

45 Citations (Scopus)

Abstract

This paper proposes a robust loss function that penalizes hybrid noise (i.e., Gaussian noise, singularity points, and larger magnitude noise) in a complex fuzzy fault-diagnosis system. A mapping relationship between fuzzy numbers and crisp real numbers that allows a fuzzy sample set to be transformed into a crisp real sample set is also presented. Furthermore, the paper proposes a novel fuzzy robust wavelet support vector classifier (FRWSVC) based on a wavelet base function and develops an adaptive Gaussian particle swarm optimization (AGPSO) algorithm to seek the optimal unknown parameter of the FRWSVC. The results of experiments that apply the hybrid diagnosis model based on the FRWSVC and the AGPSO algorithm to fault diagnosis demonstrate that it is both feasible and effective. Tests comparing the method proposed in this paper against other fuzzy support vector classifier (FSVC) machines show that it outperforms them.
Original languageEnglish
Pages (from-to)4514-4528
Number of pages15
JournalInformation Sciences
Volume180
Issue number23
DOIs
Publication statusPublished - 1 Dec 2010

Keywords

  • Adaptive mutation
  • Fault diagnosis
  • FW v-SVC
  • Gaussian mutation
  • Particle swarm optimization
  • Wavelet kernel function

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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