Intelligent Diagnosis of PMSM Faults Based on Vision Transformer
Aiming at the problem of small sample data of fault signal during motor operation,this paper pro-posed an intelligent fault diagnosis method of permanent magnet synchronous motor(PMSM)based on Vision Transformer.Firstly,the one-dimensional time-series signal data acquired by the sensor was converted into two-dimensional Gram matrix and RPM matrix image data by the Gram Matrix(Gram)and Relative Position Matrix(RPM)methods,and then the matrix image data were used as inputs to AlexNet,VGG16,ResNet and Vision Transformer networks for fault diagnosis respectively.After experimental validation,our method successfully identified and classified eight states of the PMSM such as normal,bearing fault,and demagneti-zing fault.Using the Gram matrix image achieves an accuracy of 99.2%,while using the RPM matrix im-age achieves 99.6%.These accuracies are higher than those achieved by convolutional networks such as AlexNet,VGG16,and ResNet in fault classification.This demonstrated our method effectively enhanced the accuracy of fault diagnosis for PMSM.