Simulation-driven unsupervised fault diagnosis of rolling bearing under time-varying speeds

Simulation models incorporating fault mechanisms can acquire sufficient samples with rich fault information, providing an effective solution to deep learning-driven bearing fault diagnosis in case of sample scarcity. However, the simulation models in the previous studies are mainly designed for constant speed conditions and cannot generate effective source data aligning with the variable speed conditions. Moreover, the fault impacts of bearing exhibit time-varying characteristics under variable speed conditions, causing the obstacle of the fault feature representation of the deep learning model. Therefore, this article investigates an analytical fault simulation model-driven unsupervised fault diagnosis of rolling bearing under time-varying speeds. The simulation model can customize high-quality data that match the specific variable speed conditions. The proposed network can extract robust discriminable feature representation by designing multiscale enhanced temporal convolution transformer network and enables the feature alignment of the target and simulation samples under a pseudolabel guided domain adaptation training strategy. The effectiveness and superiority of the proposed method in addressing the bottleneck problems of bearing unsupervised fault diagnosis under variable speed conditions, including condition matching data generation and time-varying feature representation, is demonstrated using two different bearing datasets.