| Summary: | Under operating conditions, bearings have a substantial service life with short failure time periods, which leads to unbalanced dataset and greatly affects the accuracy of deep learning model fault diagnosis. To address this problem, a fault diagnosis method of rolling bearing unbalanced dataset based on two-step transfer learning is proposed in this paper. First, a small amount of data in the source and target domains is used to generate the transition dataset by conditional gradient penalized generative adversarial network (CWGAN-GP). Then, the constructed convolutional neural network model is migrated twice between the source domain dataset, the transition dataset, and the target domain dataset. Finally, a small amount of data from the target domain is used to fine-tune the transferred model to obtain the final fault diagnosis model. The experimental results show that the method has a good diagnostic recognition effect on rolling bearing spalling class faults with unbalanced dataset under different working conditions.
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