Diagnosis of Interturn Short Circuit of Permanent Magnet Synchronous Motor Based on Stacked Normalized Sparse Autoencoder. Issue 1 (1st March 2022)

• Record Type: Journal Article
• Authors: Chen, Yunfeng; Li, Yuanjiang
• Is Part Of: Journal of physics. Volume 2218:Issue 1(2022)
• Publication Date: 2022-03-01
• Subjects: Inter Turn Short Circuit -- Stacked Normalized Sparse Autoencoder -- Permanent Magnet Synchronous Motor -- Fault diagnosis.; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2218/1/012011
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Regarding the problems such as the traditional auto-encoders' tendency to learn the similar features during the feature extraction and limited capability of feature learning in the shallow network models, the paper puts forward an interturn short circuit diagnosis method for Permanent Magnet Synchronous Motor (PMSM) based on the Stacked Normalized Sparse Autoencoder (SNSAE). The method is to guarantee the representativeness of the extracted features, acquire stronger anti-interference capability, and is more suitable for training diversified and large-capacity mechanical equipment failure data. Four types of Inter-Turn Short-Circuit (ITSC) fault data were collected on the PMSM fault diagnosis experimental platform for testing. The experimental outcome suggests that the SNSAE algorithm can effectively extract the fault features, and the fault diagnosis effect is better than the traditional intelligent diagnosis method, and its diagnosis accuracy is as high as 98.91%.