Research on Fault Diagnosis of IPMSM for Electric Vehicles Based on Multi-Level Feature Fusion SPP Network

Abstract

At this stage, the fault diagnosis of the embedded permanent magnet synchronous motor (IPMSM) mostly relies on the analysis of related signals when the motor is running. It requires designers to deeply understand the motor drive system and fault characteristic signals, which leads to a high threshold for fault diagnosis. This study proposes an IPMSM fault diagnosis method based on a multi-level feature fusion spatial pyramid pooling (SPP) network, which can directly diagnose motor faults through motor operating current data. This method uses the finite element software Altair Flux to build symmetrical normal motor and demagnetization faulty motor models, as well as an asymmetrical eccentric fault model; conduct a joint simulation with MATLAB-Simulink to obtain fault current data; convert the collected current data into grayscale images, using the data set expansion method to form training and test data sets; and improve the convolutional neural network (CNN) network structure, that is, adding jump connections after each pooling layer and adding a spatial pyramid pooling layer after the last pooling layer to form a new CNN structure. Experimental results show that the new CNN can extract different levels and different scales of motor fault features hidden in the image, and can effectively diagnose different types of IPMSM faults. Compared with the traditional CNN, the new CNN has a higher fault diagnosis accuracy, up to 98.16%, 2.3% higher.