Abstract: Abstract： Aiming at the problem that the number of bearing fault samples is small and unbalanced in the actual work process， a bearing fault diagnosis method based on Conditional Wasserstein Generative Adversarial Network （CWGAN-GP） and Convolutional Neural Network （CNN） is proposed. First， a CWGAN-GP generative adversarial network is constructed by combining conditional generative adversarial network （CGAN） and gradient penalized Wasserstein distance-based generative adversarial network （WGAN-GP）. Then， a small number of bearing fault data samples are input into CWGAN-GP， in order to obtain high-quality samples similar to the original samples. When the network reaches the Nash equilibrium， the generated samples and the original samples are mixed to generate a new sample set. Finally， the new sample set is input into the convolutional neural network to learn the sample features for fault diagnosis. The experimental results show that the diagnostic accuracy of the diagnostic method proposed in this paper exceeds 99%， which is higher than other diagnostic methods， effectively improving the diagnostic accuracy and enhancing its generalization ability.

Key words: Key words： fault diagnosis, deep learning, bearing, generative adversarial network, convolutional neural network