A bearing fault diagnosis method based on LSTM-GAN and convolutional neural network under small sample variable working conditions

Abstract

In order to improve the identification accuracy of bearing fault diagnosis, overcome the training difficulties and poor generalization ability of fault diagnosis model under the condition of small samples, this work constructs the LSTM-GAN model by combining long short-term memory network (LSTM) with generative adductive neural network (GAN). Firstly, LSTM is used to build a generator to generate adversarial neural network model, and the feature extraction capability of LSTM is adopted to improve the quality of generated samples. Then, the convolutional neural network (CNN) is improved to enhance its classification ability, and the improved CNN is used to classify faults. Finally, CNN and convolutional autoencoder (CAE) are used to diagnose bearing faults under different working conditions to enhance the diagnostic effect of the model under different working conditions. The results show that LSTM-GAN can capture the feature information in the original data well, and the generated samples can improve the diagnosis accuracy of bearing fault diagnosis under the condition of small samples. The diagnostic model still has high accuracy under different working conditions, which provides support for the research and application of bearing fault diagnosis.