Daoming She1, Yangyang Luo1, Yitian Wang1, Shuyuan Gan1, Xiaoan Yan1, and Michael G. Pecht2
1 School of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, China
2 Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD, USA
For more information about this article and related research, please contact Prof. Michael Pecht.
Abstract:
As a key component of rotating machinery, the operation, maintenance, and health management of bearings are of great significance, yet challenges such as the low bearing remaining useful life (RUL) prediction accuracy and the poor generalization persist. To address the above issues, this paper proposes a meta-transfer-driven method for cross-domain RUL prediction of rolling bearings with limited data. Firstly, the features with strong monotonicity and trendability in the time domain, frequency domain and time–frequency domain of bearing are selected. Then a meta-transfer learning framework is built based on task adaptation, incorporating the affine transformation parameters in the inner loop to enable adaptive model updates. In addition, the multi-kernel maximum mean difference (MK-MMD) is employed to minimize the differences between the two different domains. Finally, two cases validate the superior prediction results and generalization performance of the presented method.
This article is available for free until July 11, 2025 online here.