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

Seera,M, Lim,CP and Loo,CK 2014, Condition monitoring of broken rotor bars using a hybrid FMM-GA model. In Loo, CK, Yap, KS, Wong, KW, Teoh, A and Huang, K (ed), Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III, Springer, Berlin, Germany, pp.381-389, doi: 10.1007/978-3-319-12643-2_47.

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Title Condition monitoring of broken rotor bars using a hybrid FMM-GA model
Author(s) Seera,M
Lim,CPORCID iD for Lim,CP orcid.org/0000-0003-4191-9083
Loo,CK
Title of book Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III
Editor(s) Loo, CK
Yap, KS
Wong, KW
Teoh, A
Huang, K
Publication date 2014
Series Lecture notes in computer science ; v.8836
Chapter number 47
Total chapters 83
Start page 381
End page 389
Total pages 9
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) Condition monitoring
Fault diagnosis
Fuzzy min-max neural network
Genetic algorithms
Induction motor
Summary 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.
ISBN 9783319126425
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-12643-2_47
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2014, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070509

Document type: Book Chapter
Collection: Centre for Intelligent Systems Research
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