Domain Generalization of RUL Prediction for Bearings via Multisource Domain Mix-Up Transfer

Accurate prediction of the remaining useful life for bearings is essential to predictive maintenance and health management. Nonetheless, the initial operating phase often suffers from limited and incomplete data, which poses significant challenges to the development of robust life prediction models. To address this problem, this article introduces a multi-source domain mix-up transfer (MDMT) model, integrated with a data reproduction mechanism. The proposed approach utilizes a bidirectional gated mechanism to capture temporal dependencies from time-frequency domain signals and uncover intrinsic degradation dynamics. A domain mix-up strategy is incorporated to promote information fusion across multiple bearings, while the data reproduction mechanism integrates new source-domain data, enabling extraction of generalized degradation features across domains. Extensive experiments on the XJTU-SY bearing dataset and the C-MAPSS aircraft engine dataset show that the proposed method achieves noticeable improvement in performance under scenarios where target domain data are not involved in training. The MDMT model demonstrates potential for practical prognostics and predictive maintenance.