Stage-Aware Multi-Task Learning with Causal Degradation-Prior Fusion for Remaining Useful Life Prediction

Remaining useful life (RUL) prediction of wind-turbine bearings is challenged by nonstationary wind loads, multistage degradation, substantial lifetime dispersion, and strict deployment constraints. Conventional single-task regressors apply a unified feature-to-RUL mapping over the entire life cycle and therefore struggle to characterize stage transitions and bearing-specific degradation progress. To address these issues, this paper proposes a stage-aware multi-task RUL prediction method with causal degradation-prior fusion and collaborative distillation. During training, a high-capacity reference representation path transfers inter-sample relational structures and task-level degradation knowledge to a compact feature encoding path, while only the compact path is retained for inference. Based on the compact representation, a multi-task module jointly performs four-stage classification, stage-conditioned RUL regression, and continuous remaining-life estimation; predicted stage probabilities softly fuse the stage-conditioned outputs. A causal prior-fusion module further integrates a bearing-specific healthy-state anchor, causally identified first prediction time, cumulative damage, and the stage-aware prediction to adapt the RUL trajectory to individual degradation processes. Experiments on the IEEE PHM 2012 and XJTU-SY datasets demonstrate that the proposed method provides accurate and robust RUL prediction across different bearing degradation processes. Moreover, the compact inference path maintains efficient implementation, supporting its potential use in practical wind-turbine condition-monitoring applications.