基于卷积神经网络的逆变器故障诊断方法

For the risk of inverter failure in the permanent magnet synchronous motor (PMSM) drive system of the electric vehicles during long-term operation, a fault diagnosis method based on convolutional neural network (CNN) is proposed in this paper. Firstly, three-phase stator current data is normalized and filtered to an electric cycle current data, reducing the impact of variable torque and variable speed conditions of the motor drive system on the fault diagnosis effect. Then, the CNN, for the advantage of fault feature extraction and noise immunity, is used for inverter fault diagnosis. In MATLAB/Simulink, the PMSM drive system model of the electric vehicle is built. The fault injection simulation and experimental data is used to construct the dataset for the CNN, and the effectiveness of the proposed fault diagnosis method is verified. In addition, the applicability of the method is explored in the case of training sample dataset sparsification and motor model differentiation.The simulation results show that the fault diagnosis method proposed in this paper is robust and universal under the conditions of noise data and sparse data.