Mechanism-Informed Health Representation and Causal Stage-Aware Remaining Useful Life Prediction for Rolling Bearings

Accurate bearing prognostics require health representations that distinguish persistent degradation from transient vibration responses. This study develops a sequential framework comprising a mechanism-informed gated health indicator (MIG-HI), causal evidence-driven transition identification (CETI) and a stage-conditioned dual-branch long short-term memory network (SCDB-LSTM). MIG-HI groups multidomain vibration descriptors into structural, energy, impulsiveness, fault mechanism, and cross-channel evidence and regulates impulsive contributions using mechanism and channel support. CETI combines level, trend and persistence evidence to establish a formal first prediction time (FPT). This reference links an offline degradation-aligned normalized prognostic target with a causally updated stage coefficient. SCDB-LSTM combines branch-specific representations through a shared temporal encoder and conditions regression on the stage coefficient. The evaluation described here includes condition-specific prediction experiments under three PHM 2012 operating conditions and detailed health-indicator analyses on five Condition 1 bearings. MIG-HI achieves the highest trendability among the compared indicators (0.4967), although it does not dominate monotonicity or prognosability. The target is dimensionless and does not directly estimate remaining operating time. Cross-condition transfer, disturbance-specific robustness and calibrated predictive uncertainty require further validation.