Multiple Fault Diagnosis of PMSM Based on Stator Tooth Flux and Parallel Residual Convolutional Neural Network
Ke Lv, Dong Wang, Wen Huang, Haitao Liu, Yapeng Jiang, Jinghua Hu
Abstract
Inter-turn short circuit fault, insulation fault, eccentricity fault, and demagnetization fault are the most common faults in permanent magnet synchronous motor (PMSM). As these faults degrade reliability and cause serious catastrophes, it is necessary to diagnose these faults. However, in current methods, a fault indicator (FI) can often diagnose only one or two faults, which means that multiple FIs are required for diagnosis above four faults. To diagnose multiple faults with fewer FIs, a fault diagnosis method based on stator tooth flux (STF) of multiple teeth and multiscale kernel parallel residual convolutional neural network (PR-CNN) is proposed in this article. First, the STF of multiple teeth under four faults is analyzed. Then, FI is proposed based on the characteristics of the STF. Finally, the proposed PR-CNN is compared with state-of-the-art CNNs highlighting the superiority in this application. The results indicate that the proposed method can diagnose the above four faults by a single FI with an accuracy of 98.8% and a training data ratio of 40%. This work provides a significant reference for the multiple fault diagnosis of PMSM.