The diagnosis of rolling bearing faults has become an increasingly popular research topic in recent years. However, many studies have been conducted based on sufficient training data. In the real industrial scene, there are some problems in bearing fault diagnosis, including the imbalanced ratio of normal and failure data and the amount of unlabeled data being far more than the amount of marked data. This paper presents a rolling bearing fault diagnosis method suitable for different working conditions based on simulating the real industrial scene. Firstly, the dataset is divided into the source and target domains, and the signals are transformed into pictures by continuous wavelet transform. Secondly, Wasserstein Generative Adversarial Nets-Gradient Penalty (WGAN-GP) is used to generate false sample images; then, the source domain and target domain data are input into the migration learning network with Resnet50 as the backbone for processing to extract similar features. Multi-Kernel Maximum mean discrepancies (MK-MMD) are used to reduce the edge distribution difference between the data of the source domain and the target domain. Based on Case Western Reserve University′s dataset, the feasibility of the proposed method is verified by experiments. The experimental results show that the average fault diagnosis accuracy can reach 96.58%.