Bearing Remaining Useful Life Prediction Using Client Selection and Personalized Aggregation Enhancement in Federated Learning

Bearings are crucial components of rotating machines, and accurately predicting their remaining useful life is paramount for ensuring machine safety and maintenance. Existing prognostic studies predominantly rely on limited monitoring data collected under specific operating conditions for modeling, often overlooking valuable degradation characteristics contained under other conditions. To tackle these challenges, this study proposes a federated learning (FL)-based prognostic method that aims to collaboratively construct personalized prognostic models for bearings operating under different conditions within the FL framework. Specifically, a client selection strategy is initially adopted to identify clients with closely related high-level degradation features, guiding effective aggregation among relevant clients. This strategy significantly improves the accuracy and convergence of the prediction model. Subsequently, based on the obtained similarity parameters, a personalized aggregation enhancement scheme is proposed to aggregate models in the subgroup of selected clients, further enhancing the prognostic performance of the prediction model. This study represents a novel attempt at constructing personalized prognostic models in scenarios involving data heterogeneity. Experimental results on two bearing data sets jointly verify the improved accuracy and convergence of the proposed method.