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Increasing Electric Vehicles Reliability by Non-Invasive Diagnosis of Motor Winding Faults

Author

Listed:
  • Konrad Górny

    (Institute of Electrical Engineering and Electronics, Faculty of Control, Robotics and Electrical Engineering, Poznan University of Technology, Piotrowo Street, No. 3a, 60-965 Poznan, Poland)

  • Piotr Kuwałek

    (Institute of Electrical Engineering and Electronics, Faculty of Control, Robotics and Electrical Engineering, Poznan University of Technology, Piotrowo Street, No. 3a, 60-965 Poznan, Poland)

  • Wojciech Pietrowski

    (Institute of Electrical Engineering and Electronics, Faculty of Control, Robotics and Electrical Engineering, Poznan University of Technology, Piotrowo Street, No. 3a, 60-965 Poznan, Poland)

Abstract

The article proposes a proprietary approach to the diagnosis of induction motors allowing increasing the reliability of electric vehicles. This approach makes it possible to detect damage in the form of an inter-turn short-circuit at an early stage of its occurrence. The authors of the article describe an effective diagnostic method using the extraction of diagnostic signal features using an Enhanced Empirical Wavelet Transform and an algorithm based on the method of Ensemble Bagged Trees. The article describes in detail the methodology of the carried out research, presents the method of extracting features from the diagnostic signal and describes the conclusions resulting from the research. Phase current waveforms obtained from a real object as well as simulation results based on the field-circuit model of an induction motor were used as a diagnostic signal in the research. In order to determine the accuracy of the damage classification, simple metrics such as accuracy, sensitivity, selectivity, precision as well as complex metrics weight F1 and macro F1 were used.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2510-:d:544718
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    References listed on IDEAS

    as
    1. Hisahide Nakamura & Yukio Mizuno, 2020. "Method for Diagnosing a Short-Circuit Fault in the Stator Winding of a Motor Based on Parameter Identification of Features and a Support Vector Machine," Energies, MDPI, vol. 13(9), pages 1-15, May.
    2. Wojciech Pietrowski & Konrad Górny, 2020. "Analysis of Torque Ripples of an Induction Motor Taking into Account a Inter-Turn Short-Circuit in a Stator Winding," Energies, MDPI, vol. 13(14), pages 1-19, July.
    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. 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.
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