Open AccessDiagnosis of Interturn Short Circuit of Permanent Magnet Synchronous Motor Based on Stacked Normalized Sparse Autoencoder
Author(s) -
Yunfeng Chen,
Yuanjiang Li
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2218/1/012011
Subject(s) - autoencoder , fault (geology) , computer science , interference (communication) , artificial intelligence , permanent magnet synchronous motor , synchronous motor , pattern recognition (psychology) , feature extraction , feature (linguistics) , short circuit , magnet , deep learning , engineering , voltage , electrical engineering , telecommunications , channel (broadcasting) , linguistics , philosophy , seismology , geology
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%.