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Condition monitoring of broken rotor bars using a hybrid FMM-GA model
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.
History
Title of book
Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part IIIVolume
8836Series
Lecture notes in computer scienceChapter number
47Pagination
381 - 389Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319126425Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2014, SpringerExtent
83Editor/Contributor(s)
C Loo, K Yap, K Wong, A Teoh, K HuangUsage metrics
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No categories selectedKeywords
Condition monitoringFault diagnosisFuzzy min-max neural networkGenetic algorithmsInduction motorScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Information SystemsComputer Science, Theory & MethodsComputer ScienceINDUCTION-MOTORSNEURAL-NETWORKSCLASSIFICATIONMAINTENANCEFAULTS
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