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Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks

Author

Listed:
  • Pawel Ewert

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

  • Teresa Orlowska-Kowalska

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

  • Kamila Jankowska

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

Abstract

Permanent magnet synchronous motors (PMSMs) are becoming more popular, both in industrial applications and in electric and hybrid vehicle drives. Unfortunately, like the others, these are not reliable drives. As in the drive systems with induction motors, the rolling bearings can often fail. This paper focuses on the possibility of detecting this type of mechanical damage by analysing mechanical vibrations supported by shallow neural networks (NNs). For the extraction of diagnostic symptoms, the Fast Fourier Transform (FFT) and the Hilbert transform (HT) were used to obtain the envelope signal, which was subjected to the FFT analysis. Three types of neural networks were tested to automate the detection process: multilayer perceptron (MLP), neural network with radial base function (RBF), and Kohonen map (self-organizing map, SOM). The input signals of these networks were the amplitudes of harmonic components characteristic of damage to bearing elements, obtained as a result of FFT or HT analysis of the vibration acceleration signal. The effectiveness of the analysed NN structures was compared from the point of view of the influence of the network architecture and various parameters of the learning process on the detection effectiveness.

Suggested Citation

  • Pawel Ewert & Teresa Orlowska-Kowalska & Kamila Jankowska, 2021. "Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks," Energies, MDPI, vol. 14(3), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:712-:d:490020
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    References listed on IDEAS

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    1. Marcin Skora & Pawel Ewert & Czeslaw T. Kowalski, 2019. "Selected Rolling Bearing Fault Diagnostic Methods in Wheel Embedded Permanent Magnet Brushless Direct Current Motors," Energies, MDPI, vol. 12(21), pages 1-19, November.
    2. Zia Ullah & Bilal Ahmad Lodhi & Jin Hur, 2020. "Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG," Energies, MDPI, vol. 13(15), pages 1-17, July.
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    Cited by:

    1. Daniel A. Magallón & Carlos E. Castañeda & Francisco Jurado & Onofre A. Morfin, 2021. "Design of a Neural Super-Twisting Controller to Emulate a Flywheel Energy Storage System," Energies, MDPI, vol. 14(19), pages 1-23, October.
    2. Kamila Jankowska & Mateusz Dybkowski, 2021. "A Current Sensor Fault Tolerant Control Strategy for PMSM Drive Systems Based on C ri Markers," Energies, MDPI, vol. 14(12), pages 1-18, June.
    3. Xiaohua Song & Jing Liu & Chaobo Chen & Song Gao, 2022. "Advanced Methods in Rotating Machines," Energies, MDPI, vol. 15(15), pages 1-3, July.
    4. Hisahide Nakamura & Keisuke Asano & Seiran Usuda & Yukio Mizuno, 2021. "A Diagnosis Method of Bearing and Stator Fault in Motor Using Rotating Sound Based on Deep Learning," Energies, MDPI, vol. 14(5), pages 1-15, March.

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