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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2013

01.12.2013 | Original Article

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

verfasst von: Manjeevan Seera, Chee Peng Lim, Dahaman Ishak, Harapajan Singh

Erschienen in: Neural Computing and Applications | Sonderheft 1/2013

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Abstract

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.
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Metadaten
Titel
Application of the fuzzy min–max neural network to fault detection and diagnosis of induction motors
verfasst von
Manjeevan Seera
Chee Peng Lim
Dahaman Ishak
Harapajan Singh
Publikationsdatum
01.12.2013
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe Sonderheft 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-1310-x

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