PMSM Demagnetization and Eccentricity Fault Diagnosis Method Based on Improved ResNet
To meet the demand of permanent magnet synchronous motor fault diagnosis in recent years and improve the accuracy of fault diagnosis,a diagnosis method of permanent magnet synchronous motor is proposed based on multi-scale feature fusion and atrous convolution pyramid model,which can directly diagnose the motor fault through the stator current data.Firstly,the multi-scale feature fusion module is used to extract the features of different scales and resolutions of images to improve the information utilization rate of a single image.At the same time,by adding the attention mechanism to the feature fusion module,the feature weights of different channels in the network are highly consistent,which further ensures the ability of the network to extract image features.Finally,the atrous convolution kernel is introduced into the spatial pooling pyramid to construct the atrous spatial pyramid pooling,which not only solves the problem of repeated extraction of the same feature by the network,but also enhances the receptive field of the model and improves the diagnostic accuracy of the model for different faults.The experimental results show that this method has high diagnostic accuracy for different types of motor faults.Compared with those of the traditional intelligent algorithm,the accuracy and loss function of the proposed algorithm are improved obviously.
万方数据
维普
