Condition monitoring of broken rotor bars using a hybrid FMM-GA model

Manjeevan Seera, Chee Peng Lim, Chu Kiong Loo

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review


A condition monitoring system for induction motors using a hybrid Fuzzy Min-Max (FMM) neural network and Genetic Algorithm (GA) is presented in this paper. Two types of experiments, one from the finite element method and another from real laboratory tests of broken rotor bars in an induction motor are conducted. The induction motor with broken rotor bars is operated under different load conditions. FMM is first used for learning and distinguishing between a healthy motor and one with broken rotor bars. The GA is then utilized for extracting fuzzy if-then rules using the don’t care approach in minimizing the number of rules. The results clearly demonstrate the effectiveness of the hybrid FMM-GA model in condition monitoring of broken rotor bars in induction motors.

Original languageEnglish
Title of host publicationNeural Information Processing - 21st International Conference, ICONIP 2014, Proceedings
EditorsChu Kiong Loo, Keem Siah Yap, Kok Wai Wong, Andrew Teoh, Kaizhu Huang
Number of pages9
ISBN (Electronic)9783319126425
Publication statusPublished - 2014
Externally publishedYes
EventInternational Conference on Neural Information Processing 2014 - Kuching, Malaysia
Duration: 3 Nov 20146 Nov 2014
Conference number: 21st

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Neural Information Processing 2014
Abbreviated titleICONIP 2014
Internet address


  • Condition monitoring
  • Fault diagnosis
  • Fuzzy min-max neural network
  • Genetic algorithms
  • Induction motor

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