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Wavelet transform and neural network techniques for inter-turn short circuit diagnosis and location in induction motor

Author

Listed:
  • Besma Bessam

    (University of Biskra)

  • Arezki Menacer

    (University of Biskra)

  • Mohamed Boumehraz

    (University of Biskra)

  • Hakima Cherif

    (University of El-Oued)

Abstract

It is well known that stator winding faults such the inter-turn short circuit are the most frequent source of breakdowns in induction motors. Early detection of any small inter-turn short circuit and location of the faulty phase at different load would eliminate some subsequent damage to adjacent coils and stator core, reducing then the repair cost. To achieve this purpose, the present paper presents a new method of diagnosis and detection of inter turn short circuit fault using discrete wavelet transform (DWT) and neural networks (NN). This method consists in analyzing the stator current by DWT in order to compute the energy associated with the stator fault in the frequency bandwidth. Then, this energy is used as input for a NN classifier. The results obtained are astonishing and the approach is able to detect any small number of shorted turns and the faulty phase even under different load of the machine.

Suggested Citation

  • Besma Bessam & Arezki Menacer & Mohamed Boumehraz & Hakima Cherif, 2017. "Wavelet transform and neural network techniques for inter-turn short circuit diagnosis and location in induction motor," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(1), pages 478-488, January.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:1:d:10.1007_s13198-015-0400-4
    DOI: 10.1007/s13198-015-0400-4
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    References listed on IDEAS

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    1. Kowalski, Czeslaw T & Orlowska-Kowalska, Teresa, 2003. "Neural networks application for induction motor faults diagnosis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 63(3), pages 435-448.
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    Cited by:

    1. Cherif, Hakima & Benakcha, Abdelhamid & Laib, Ismail & Chehaidia, Seif Eddine & Menacer, Arezky & Soudan, Bassel & Olabi, A.G., 2020. "Early detection and localization of stator inter-turn faults based on discrete wavelet energy ratio and neural networks in induction motor," Energy, Elsevier, vol. 212(C).
    2. Swapnil K. Gundewar & Prasad V. Kane, 2022. "Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 2876-2894, December.

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