LLM-informed stage-aware and physics-guided remaining useful life prediction for rolling bearings

Remaining useful life (RUL) prediction of rolling bearings is a fundamental task in prognostics and health management. Existing data-driven approaches mainly rely on static training objectives and purely signal-level learning, which limits their ability to adapt to different degradation stages and to incorporate high-level degradation knowledge. An large language model (LLM)-informed RUL prediction framework is proposed, integrating LLM-based degradation reasoning, stage-aware adaptive learning, and physics-guided constraint regularization. Instead of directly processing raw vibration signals, the LLM is employed as a degradation reasoning module that infers structured degradation priors, including degradation stage, degradation trend, and physically meaningful constraints, from semantic health representations. These priors are subsequently embedded into a task-specific neural predictor through a stage-aware adaptive loss updating strategy and physics-inspired constraint losses. By explicitly modeling stage-dependent learning objectives and enforcing physically consistent degradation behavior, the proposed framework achieves more reliable lifecycle-wide RUL prediction. Experimental results demonstrate that the proposed method consistently outperforms existing data-driven approaches in terms of prediction accuracy and stability.