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

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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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Acknowledgments

The authors gratefully acknowledge the partial financial support of the FRGS grants (No. 6711229 and 6711195) for this work.

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Authors and Affiliations

  1. Deakin University, Geelong, VIC, Australia

    Chee Peng Lim

  2. Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia

    Dahaman Ishak

  3. Universiti Teknologi MARA, Permatang Pauh, Penang, Malaysia

    Harapajan Singh

  4. Intel Technology, Bayan Lepas, Penang, Malaysia

    Manjeevan Seera

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  1. Manjeevan Seera
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  2. Chee Peng Lim
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  3. Dahaman Ishak
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  4. Harapajan Singh
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Correspondence to Chee Peng Lim.

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Seera, M., Lim, C.P., Ishak, D. et al. Application of the fuzzy min–max neural network to fault detection and diagnosis of induction motors. Neural Comput & Applic 23 (Suppl 1), 191–200 (2013). https://doi.org/10.1007/s00521-012-1310-x

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