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Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors

Author

Listed:
  • Maciej Skowron

    (Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland)

  • Marcin Wolkiewicz

    (Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland)

  • Teresa Orlowska-Kowalska

    (Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland)

  • Czeslaw T. Kowalski

    (Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland)

Abstract

This paper presents a comparative study on the application of different neural network structures to early detection of electrical faults in induction motor drives. The diagnosis inference of the stator inter-turn short-circuits and broken rotor bars is based on the analysis of an axial flux of the induction motor. In order to automate the fault detection process, three different structures of neural networks were used: multi-layer perceptron, self-organizing Kohonen network and recursive Hopfield network. Tests were carried out for various levels of stator and rotor failures. In order to assess the sensitivity of the applied neural detectors, the tests were carried out for variable load conditions and for different values of the supply voltage frequency. Experimental results of the elaborated neural detectors are presented and discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2392-:d:241888
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    References listed on IDEAS

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    1. Luqman Maraaba & Zakariya Al-Hamouz & Mohammad Abido, 2018. "An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors," Energies, MDPI, vol. 11(3), pages 1-18, March.
    2. 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. Konrad Górny & Piotr Kuwałek & Wojciech Pietrowski, 2021. "Increasing Electric Vehicles Reliability by Non-Invasive Diagnosis of Motor Winding Faults," Energies, MDPI, vol. 14(9), pages 1-14, April.
    2. Remus Pusca & Raphael Romary & Ezzeddine Touti & Petru Livinti & Ilie Nuca & Adrian Ceban, 2021. "Procedure for Detection of Stator Inter-Turn Short Circuit in AC Machines Measuring the External Magnetic Field," Energies, MDPI, vol. 14(4), pages 1-22, February.
    3. 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.
    4. Lien-Kai Chang & Shun-Hong Wang & Mi-Ching Tsai, 2020. "Demagnetization Fault Diagnosis of a PMSM Using Auto-Encoder and K-Means Clustering," Energies, MDPI, vol. 13(17), pages 1-12, August.
    5. Jordi Burriel-Valencia & Ruben Puche-Panadero & Javier Martinez-Roman & Angel Sapena-Baño & Martin Riera-Guasp & Manuel Pineda-Sánchez, 2019. "Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines," Energies, MDPI, vol. 12(17), pages 1-18, August.
    6. Przemyslaw Pietrzak & Piotr Pietrzak & Marcin Wolkiewicz, 2024. "Microcontroller-Based Embedded System for the Diagnosis of Stator Winding Faults and Unbalanced Supply Voltage of the Induction Motors," Energies, MDPI, vol. 17(2), pages 1-22, January.

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