Fault Diagnosis of Inter-Turn Fault in Permanent Magnet-Synchronous Motors Based on Cycle-Generative Adversarial Networks and Deep Autoencoder

Fault Diagnosis of Inter-Turn Fault in Permanent Magnet-Synchronous Motors Based on Cycle-Generative Adversarial Networks and Deep Autoencoder

This paper addresses the issue of the difficulty in obtaining inter-turn fault (ITF) samples in electric motors, specifically in permanent magnet-synchronous motors (PMSMs), where the number of ITF samples in the stator windings is severely lacking compared to healthy samples. To effectively identif...

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Bibliographic Details
Main Authors: Wenkuan Huang, Hongbin Chen, Qiyang Zhao
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Applied Sciences
Subjects:
permanent magnet-synchronous motor
cycle-generation adversarial network
deep autoencoder
dataset expansion
inter-turn fault
fault diagnosis
Online Access:https://www.mdpi.com/2076-3417/14/5/2139

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