Article: Intelligent motor fault diagnosis based on deep learning Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.9 pp.23 - 42 Abstract: In order to improve the effect of intelligent motor fault diagnosis, this paper proposes a motor fault diagnosis method based on deep transfer learning. The parameter identification module and deep neural network were used to solve the problem of accuracy reduction or non-identification of motor fault diagnosis methods based on deep learning neural network caused by motor dynamic parameters such as motor parameter drift and motor aging encountered in actual engineering. According to the experimental results, to a certain extent, it can solve the problem of neural network fault recognition accuracy decline caused by the problem of variable parameters of traditional neural network motor. It can be seen that the method proposed in this paper has certain effects, provides a large amount of engineering measured data for the problem of insufficient samples faced by complex machinery such as motors, and lays a good foundation for subsequent research. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent motor fault diagnosis based on deep learning

Authors: Yongchao Xie; Qing Chen; Jinyan Shi

Addresses: Hunan Railway Professional Technology College, ZhuZhou, 412001, China ' Hunan Railway Professional Technology College, ZhuZhou, 412001, China ' Hunan Railway Professional Technology College, ZhuZhou, 412001, China

Abstract: In order to improve the effect of intelligent motor fault diagnosis, this paper proposes a motor fault diagnosis method based on deep transfer learning. The parameter identification module and deep neural network were used to solve the problem of accuracy reduction or non-identification of motor fault diagnosis methods based on deep learning neural network caused by motor dynamic parameters such as motor parameter drift and motor aging encountered in actual engineering. According to the experimental results, to a certain extent, it can solve the problem of neural network fault recognition accuracy decline caused by the problem of variable parameters of traditional neural network motor. It can be seen that the method proposed in this paper has certain effects, provides a large amount of engineering measured data for the problem of insufficient samples faced by complex machinery such as motors, and lays a good foundation for subsequent research.

Keywords: deep learning; motor; failure; intelligent diagnosis.

DOI: 10.1504/IJICT.2025.145829

International Journal of Information and Communication Technology, 2025 Vol.26 No.9, pp.23 - 42

Received: 12 Oct 2024
Accepted: 02 Jan 2025
Published online: 28 Apr 2025 *

Title: Intelligent motor fault diagnosis based on deep learning

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