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A Diagnosis Method of Bearing and Stator Fault in Motor Using Rotating Sound Based on Deep Learning

Author

Listed:
  • Hisahide Nakamura

    (Research and Development Division, TOENEC Corporation, 1-79, Takiharu-cho, Minami-ku, Nagoya 457-0819, Japan)

  • Keisuke Asano

    (Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan)

  • Seiran Usuda

    (Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan)

  • Yukio Mizuno

    (Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan)

Abstract

Various industrial fields use motors as key power sources, and their importance is increasing. In motor manufacturing, various tests are conducted for each motor before shipping. The no-load test is one such test, in which, for instance, the current flowing into the motor and temperature of the bearing is measured to confirm whether they are within specific values. Reducing labor, cost, and time in identifying an initially defective product requires a simple and reliable method. This study proposes a new diagnosis to identify the motor conditions based on the rotating sound of the motor in the no-load test. First, the rotating sounds of motors were measured using several healthy motors and motors with faults. Second, their sound characteristics were analyzed, and it was found that the characteristic signals appeared in a specific frequency range periodically. Then, considering these phenomena, a diagnostic method based on deep learning was proposed to judge the faults using long short-term memory (LSTM). Finally, the effectiveness of the proposed method was verified through experiments.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1319-:d:508016
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    References listed on IDEAS

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    1. 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.
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