An Optimal-Subdomain Generalization Method for Remaining Useful Life Prediction of Machinery Under Time-Varying Operation Conditions

Machinery often operates under time-varying conditions, which can lead to distribution discrepancies in degradation samples. However, most existing domain generalization-based methods for predicting remaining useful life (RUL) are applied to constant operation conditions, and they may demonstrate performance deteriorations across alternating operation conditions. This article proposes an optimal-subdomain generalization method for RUL prediction. It considers the local fluctuations of monitoring signals under time-varying operation conditions and performs subdomain generalization to overcome the distribution discrepancies of samples. First, the RUL labels are discretized into pseudolabels representing different health states. To guide the discretization, an optimal discretization strategy is proposed to establish the relationship between the generalization error and degradation samples. Second, the optimal subdomain generalization model is utilized to extract subdomain representations among different operation conditions for the estimation of RULs. Finally, the run-to-failure experiments on hub-bearings are conducted for demonstration. The prediction results with high accuracy show advantages of the proposed method for RUL prediction.