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Impact of the Convolutional Neural Network Structure and Training Parameters on the Effectiveness of the Diagnostic Systems of Modern AC Motor Drives

Author

Listed:
  • Maciej Skowron

    (Department of Electrical Machines and Drives, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland)

  • Czeslaw T. Kowalski

    (Department of Electrical Machines and Drives, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland)

  • Teresa Orlowska-Kowalska

    (Department of Electrical Machines and Drives, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland)

Abstract

Currently, AC motors are a key element of industrial and commercial drive systems. During normal operation, the machines may become damaged, which may pose a threat to the users. Therefore, it is important to develop a fault detection method that allows for the detection of a fault at an early stage. Among the currently used diagnostic systems, applications based on deep neural structures are dynamically developed. Despite many examples of applications of deep learning methods, there are no formal rules for selecting the network structure and parameters of the training process. Such methods would make it possible to shorten the implementation process of deep networks in diagnostic systems of AC machines. The article presents a detailed analysis of the influence of deep convolutional network hyperparameters and training procedures on the precision of the interturn short-circuits detection system. The studies take into account the direct analysis of phase currents through the convolutional network for induction motors and permanent magnet synchronous motors. The research results presented in the article are an extension of the authors’ previous research.

Suggested Citation

  • Maciej Skowron & Czeslaw T. Kowalski & Teresa Orlowska-Kowalska, 2022. "Impact of the Convolutional Neural Network Structure and Training Parameters on the Effectiveness of the Diagnostic Systems of Modern AC Motor Drives," Energies, MDPI, vol. 15(19), pages 1-22, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7008-:d:923844
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    References listed on IDEAS

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    1. Lucia Frosini, 2020. "Novel Diagnostic Techniques for Rotating Electrical Machines—A Review," Energies, MDPI, vol. 13(19), pages 1-26, September.
    2. 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.
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