To address the situation of class imbalance where normal samples are abundant and fault samples are scarce under actual working conditions leading to the problem that the fault diagnosis model based on deep learning has poor diagnostic capability,a fault diagnosis model based on adaptive synthetic sampling (ADASYN) and Swin Transformer was proposed.An adaptive synthetic sampling method was used to improve the data distribution and to address the imbalance between faulty and normal sample categories in actual operating conditions.A deep transfer learning method was used to equip the model with the shallow weights needed to discriminate rolling bearing faults,and the deep weights were obtained by back propagation training.Afterwards,the model was used for bearing failure testing and debugging.Finally,the model was used in a real-world test of bearing faults to check its diagnostic capability under actual operating conditions.The experimental results show that the proposed model has a diagnostic accuracy of 97% and is well suited to the diagnosis of rolling bearing faults in the case of category imbalance.