Induction Motor Fault Diagnosis Based on Transfer Principal Component Analysis

This paper presents a transfer learning‐based approach for induction motor fault diagnosis, where the Transfer principal component analysis (TPCA) is proposed to improve diagnostic performance of the induction motors under various working conditions. TPCA is developed to minimize the distribution di...

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Bibliographic Details
Published inChinese Journal of Electronics Vol. 30; no. 1; pp. 18 - 25
Main Authors Ruqiang, Yan, Fei, Shen, Mengjie, Zhou
Format Journal Article
LanguageEnglish
Published Published by the IET on behalf of the CIE 01.01.2021
Subjects
Fault diagnosis
Induction motors
Transfer learning
Transfer principal component analysis
Various working conditions
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Summary:This paper presents a transfer learning‐based approach for induction motor fault diagnosis, where the Transfer principal component analysis (TPCA) is proposed to improve diagnostic performance of the induction motors under various working conditions. TPCA is developed to minimize the distribution difference between training and testing data by mapping cross‐domain data into a shared latent space in which domain difference can be reduced. The trained model can achieve a good performance in testing data by using the learned features consisting of common latent principal components. Experimental results show that the proposed approach outperforms traditional machine learning techniques and can diagnose induction motor fault under various working conditions effectively.
ISSN:1022-4653
2075-5597
DOI:10.1049/cje.2020.11.003