In practical engineering applications,the short duration of rolling bearing fault states leads to imbalanced datasets,making it difficult to use deep learning algorithms for fault diagnosis. In this paper,a n infrared diagnosis method for rolling bearing faults based on the combination of the Wasserstein distance based gradient penalty generative adversarial network (WGAN GP) and a support vector machine based convolutional neural network (CNN SVM) is proposed. The imbalanced dataset is constructed from infrared thermal images,and WGAN GP is used to augment the imbalanced data to achieve dataset balance,after which the CNN SVM model is then applied to the dataset to extract deep features and complete fault classification. The experimental results show that the model combining WGAN GP with CNN SVM performs well under imbalanced datasets,with better fault diagnosis capability compared to other models,and reduces the time spent in the fault classification stage by more than 16.89%.