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Neural networks application for induction motor faults diagnosis

Author

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  • Kowalski, Czeslaw T
  • Orlowska-Kowalska, Teresa

Abstract

The paper deals with diagnosis problems of the induction motors in the case of rotor, stator and rolling bearing faults. Two kinds of neural networks (NN) were proposed for diagnostic purposes: multilayer perceptron networks and self organizing Kohonen networks. Neural networks were trained and tested using measurement data of stator current and mechanical vibration spectra. The efficiency of developed neural detectors was evaluated. Feedforward NN with very simple internal structure, used for the detection of all fault kinds, gave satisfactory results, which is very important in practical realization. Experiments with Kohonen networks indicated that they could be used for the initial classification of motor faults, as an introductory step before the proper neural detector based on multiplayer perceptron is used. The obtained results lead to a conclusion that neural detectors for rotor and stator faults as well as for rolling bearings and supply asymmetry faults can be developed based on measurement data acquired on-line in the drive system.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:matcom:v:63:y:2003:i:3:p:435-448
    DOI: 10.1016/S0378-4754(03)00087-9
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    Citations

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    Cited by:

    1. Maciej Skowron & Marcin Wolkiewicz & Teresa Orlowska-Kowalska & Czeslaw T. Kowalski, 2019. "Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors," Energies, MDPI, vol. 12(12), pages 1-20, June.
    2. Josue A. Reyes-Malanche & Efrain Ramirez-Velasco & Francisco J. Villalobos-Pina & Suresh K. Gadi, 2024. "Short-Circuit Fault Diagnosis on the Windings of Three-Phase Induction Motors through Phasor Analysis and Fuzzy Logic," Energies, MDPI, vol. 17(16), pages 1-14, August.
    3. Vikas Singh & Purushottam Gangsar & Rajkumar Porwal & A. Atulkar, 2023. "Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 931-960, March.
    4. Bouzid, M. & Champenois, G., 2013. "An efficient, simplified multiple-coupled circuit model of the induction motor aimed to simulate different types of stator faults," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 90(C), pages 98-115.
    5. Ana L. Martinez-Herrera & Edna R. Ferrucho-Alvarez & Luis M. Ledesma-Carrillo & Ruth I. Mata-Chavez & Misael Lopez-Ramirez & Eduardo Cabal-Yepez, 2022. "Multiple Fault Detection in Induction Motors through Homogeneity and Kurtosis Computation," Energies, MDPI, vol. 15(4), pages 1-11, February.
    6. Maciej Skowron & Teresa Orlowska-Kowalska & Marcin Wolkiewicz & Czeslaw T. Kowalski, 2020. "Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor," Energies, MDPI, vol. 13(6), pages 1-21, March.
    7. 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.

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