1. Induction Motor Fault Diagnosis Based on Transfer Principal Component Analysis
- Author
-
Yan Ruqiang, Zhou Mengjie, and Shen Fei
- Subjects
Computer science ,business.industry ,Applied Mathematics ,020206 networking & telecommunications ,Pattern recognition ,02 engineering and technology ,Fault (power engineering) ,Domain (software engineering) ,Transfer (computing) ,Principal component analysis ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,Electrical and Electronic Engineering ,Transfer of learning ,business ,Induction motor ,Test data - Abstract
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.
- Published
- 2021