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Neural Computing and Applications

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01-12-2013 | Original Article

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

Authors: Manjeevan Seera, Chee Peng Lim, Dahaman Ishak, Harapajan Singh

Published in: Neural Computing and Applications | Special Issue 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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Title: Application of the fuzzy min–max neural network to fault detection and diagnosis of induction motors
Authors: Manjeevan Seera
Chee Peng Lim
Dahaman Ishak
Harapajan Singh
Publication date: 01-12-2013
Publisher: Springer London
Published in: Neural Computing and Applications / Issue Special Issue 1/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-1310-x

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