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

Seera, Manjeevan, Lim, Chee Peng, Ishak, Dahaman and Singh, Harapajan 2012, Application of the fuzzy min-max neural network to fault detection and diagnosis of induction motors, Neural computing and applications, vol. 23, no. Supplement 1, pp. 191-200.

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Title Application of the fuzzy min-max neural network to fault detection and diagnosis of induction motors
Author(s) Seera, Manjeevan
Lim, Chee Peng
Ishak, Dahaman
Singh, Harapajan
Journal name Neural computing and applications
Volume number 23
Issue number Supplement 1
Start page 191
End page 200
Total pages 10
Publisher Springer
Place of publication London, England
Publication date 2012-12
ISSN 0941-0643
1433-3058
Keyword(s) fault detection and diagnosis
induction motor
fuzzy min-max neural network
motor current signature analysis
pattern classification
Summary 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.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2012, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30050991

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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