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

Hisahide Nakamura, Keisuke Asano, Seiran Usuda and Yukio Mizuno
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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

Energies, 2021, vol. 14, issue 5, 1-15

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.

Keywords: diagnosis; bearing fault; short-circuit fault; short-time Fourier-transform (STFT); long short-term memory (LSTM) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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