Research on fault diagnosis of motor interturn short circuit based on deep learning

Abstract

Fault diagnosis is an effective means to improve the reliability of the electric drive system of new energy vehicles. At present, inter turn short circuit faults in new energy vehicle motors are mainly detected using single fault features and small sample fault datasets. This method has low fault diagnosis accuracy and poor robustness. In this paper, a combined sample expansion strategy of conditional generative adversarial networks for attention mechanism optimization is proposed, and a fault diagnosis method combining improved Convolutional neural network is proposed. First, the combined characteristic data set of third harmonic and negative sequence current is made, and input into Attention CGAN to realize the expansion of original data samples. Then the expanded data and the original data are combined into a new data set, which is input into the improved CNN for training, and finally the fault diagnosis results are obtained. The experimental results show that compared to traditional fault diagnosis methods, the proposed fault diagnosis method based on combined features and data expansion improves the fault diagnosis accuracy by about 6.92%.