Application of the fuzzy min-max neural network to fault detection and diagnosis of induction motors

Manjeevan Seera, Chee Peng Lim, Dahaman Ishak, Harapajan Singh

Research output: Contribution to journalArticleResearchpeer-review

21 Citations (Scopus)


In this paper, an application of the motor current signature analysis (MCSA) method and the fuzzy min-max (FMM) neural network to detection and classification of induction motor faults is described. The finite element method is employed to generate simulated data pertaining to changes in the stator current signatures under different motor conditions. The MCSA method is then used to process the stator current signatures. Specifically, the power spectral density is employed to extract harmonics features for fault detection and classification with the FMM network. Various types of induction motor faults, which include stator winding faults and eccentricity problems, under different load conditions are experimented. The results are analyzed and compared with those from other methods. The outcomes indicate that the proposed technique is effective for fault detection and diagnosis of induction motors under different conditions.

Original languageEnglish
Pages (from-to)191-200
Number of pages10
JournalNeural Computing and Applications
Issue numberSUPPL1
Publication statusPublished - 2013
Externally publishedYes


  • Fault detection and diagnosis
  • Fuzzy min-max neural network
  • Induction motor
  • Motor current signature analysis
  • Pattern classification

Cite this