Engineering Research Express, Volume 8, 13 Feb 2026, DOI: 10.1088/2631-8695/ae3b06

A Meta-Transfer Learning-Guided Approach for Remaining Useful Life Prediction of Rolling Bearings with Small-Sample Data

Yudan Duan¹, Zhichao Yang¹, Shuyuan Gan¹, Yuqin Liu¹, Daoming She¹ and Michael Pecht^2
1School of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, China
²Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA

For more information about this article and related research, please contact Prof. Michael Pecht.

Abstract:

As a key transmission component of rotating machinery, the life prediction and health management of rolling bearings are crucial for achieving intelligent operation and maintenance of equipment and ensuring the reliability of the system. A small-sample remaining useful life (RUL) prediction approach for rolling bearings based on meta-transfer learning is proposed in this paper. By fusing model-agnostic meta-learning (MAML) and domain adversarial neural networks (DANN), a MAML-DANN transfer learning (MDTL) framework is constructed to address the dual challenges of few-shot adaptation and cross-domain alignment. To enhance MAML’s small-sample adaptability, an outer-loop cosine annealing weight allocation strategy is designed to dynamically balance training priorities between task adaptation and domain alignment. For DANN, a feature spectrum penalty (FSP) regularization is introduced to constrain singular values of source/target domain features, preserving domain-invariant degradation information without compromising prediction performance.Combined with the maximum mean discrepancy (MMD) loss function, the model further reduces cross-domain distribution differences. Validated on IEEE PHM 2012 and ABLT-1A datasets, the proposed MDTL method reduces average RMSE by at least 31.45% compared to baselines (e.g., MAML, MAML-MMD). The results demonstrate its superiority in small-sample and variable-condition bearing RUL prediction, providing a practical solution for industrial health management.

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