An open-circuit fault diagnosis method for PMSM drive system based on multi-branch 1D-CNN

Abstract

In this paper, a fault diagnosis method based on multi-branch one-dimensional convolutional neural network (1D-CNN) is proposed for the open-circuit fault of the inverter IGBT and current sensor in permanent magnet synchronous motor (PMSM) drive system. Firstly, to avoid adding additional sensors, a three-phase current acts as a highly responsive signal for fault detection. Secondly, a multi-branch 1D-CNN diagnosis model is built to solve the complexity and uncertainty problems caused by traditional artificial feature extraction and selection. Then, the residual connection is introduced into the diagnostic model to alleviate the disappearance of the network gradient and accelerate the convergence speed. Finally, the three-phase current is directly input into the model for training and classification. The test results show that the method can identify the open-circuit fault of the IGBT and current sensor within the sample length of a fundamental cycle, and the accuracy rate is 99.84%. Compared with other traditional machine learning methods, it has been proven to have higher diagnostic accuracy and good anti-noise ability.