Aiming at the problem that the bearing fault diagnosis algorithm based on deep learning has poor diagnosis performance when the fault samples are lack of labels in different working conditions and real environmentsly

an unsupervised domain adaptive bearing fault diagnosis method was proposed to realize the unsupervised fault diagnosis of bearings under different working conditions. Firstly

the bearing fault sample data was preprocessed by fast Fourier transform and the features of bearing faults samples were extracted using convolutional neural network. Then

the feature distributions output of the source domain and the target domain were converged by the method of reversing labels in the generative adversarial network. Finally

the classifier of the source domain was exploited to complete the bearing fault diagnosis task under different working conditions. In order to verify the effectiveness of the proposed method

relevant comprehensive experiments were carried out on the bearing dataset of Case Western Reserve University of American and the bearing dataset of the University of Paderborn in Germany. The experimental results show that the proposed method can use the unlabeled target domain data to complete the transfer task

and it shows a good transfer performance on the two datasets and achieves a high diagnostic accuracy.