To address the challenge of model training difficulties caused by the scarcity of labeled training samples in practical applications, this study fully leverages the combination of simulation and real data for fault diagnosis.
A simulation-reality domain mixup adaptation method (SR-DMA) is proposed for cross-domain bearing fault diagnosis. Firstly, a bearing fault simulation model in a non-stationary state is established to generate simulation data, which is used as the source dataset. Secondly, the domain mixup adaptation method is developed to enhance the performance of intelligent fault diagnosis by utilizing class-aware information.
The effectiveness and practicality of SR-DMA are validated by two bearing cases. The results show that SR-DMA can fully adapt to the deep feature distribution of simulation and reality data, improving the accuracy of bearing fault diagnosis compared to other methods.
(1) A simulation-reality domain mixup adaptation method (SR-DMA) is proposed for cross-domain bearing fault diagnosis. (2) A bearing fault simulation model in a non-stationary state is established to generate simulation data. (3) The domain mixup adaptation method is developed to enhance the performance of intelligent fault diagnosis by utilizing class-aware information.
