Induction Motor Fault Diagnosis Based on Transfer Principal Component Analysis

• 作者: YAN Ruqiang SHEN Fei ZHOU Mengjie
• 来源期刊: 电子学报（英文）
• 年，卷（期）: 2021,（1）
• 所属期刊栏目: SPECIAL FOCUS: DATA-DRIVEN INDUSTRIAL INTELLIGENCE
• 页码范围: 18-25
• 页数: 8页
• 语种: 英文
• DOI: 10.1049/cje.2020.11.003

摘要

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.