Remaining Useful Life Prediction of Rolling Bearings Based on Federated Domain Generalization

Objectives: To address the difficulty of data sharing under privacy constraints and the performance degradation of conventional federated models caused by pronounced inter-client data heterogeneity in rolling bearing remaining useful life prediction, an FDG-based framework is developed for this task. Methods: The proposed framework jointly optimizes client-side feature learning and server-side aggregation. On the client side, a domain-adversarial learning mechanism together with a gradient reversal strategy is introduced to suppress domain-related information in degradation representations and enhance domain-invariant feature learning. On the server side, a distribution-aware dynamic aggregation strategy is designed to adaptively assign aggregation weights by jointly considering client predictive performance and feature distribution discrepancies, thereby mitigating the adverse effects of non-IID data on model aggregation. Conclusions: A federated training scenario is constructed using the PHM 2012 and XJTU-SY datasets, which involve two different bearing types. Experimental results show that, without requiring raw data to leave local clients, the proposed framework improves the accuracy and generalization capability of rolling bearing remaining useful life prediction.