Fault Diagnosis Method of Permanent Magnet Synchronous Motor Based on WCNN and Few-Shot Learning

Abstract

With the continuous development of actuator technology, the Electro-Mechanical Actuator (EMA) is gradually becoming the first choice in the aviation field. Permanent Magnet Synchronous Motor (PMSM) is one of the core components of EMA, and its healthy state determines the working performance of EMA. In this paper, the interturn short-circuit fault of PMSM is taken as the typical fault, and a new fault diagnosis framework is proposed based on a wide-kernel convolutional neural network (WCNN) and few-shot learning. Firstly, the wide convolution kernel is added as the first layer to extract short-time features while automatically learning deeply oriented features oriented to diagnosis and removing useless features. Then, the twin neural network is introduced to establish a wide kernel convolutional neural network, which can also achieve good diagnostic results under a few-shot learning framework. The effectiveness of the proposed method is verified by the general data set. The results show that the accuracy of few-shot learning is 9% higher than that of WCNN when the fault data are small. Finally, a fault test platform was built for EMA to collect three-phase current data under different fault states, and the collected data were used to complete the fault diagnosis of PMSM. With limited data, the accuracy of few-shot learning increased by 8% on average compared with WCNN, which has good engineering value.