A Trustworthy Large Language Model-Based Method for Bearing Predictive Maintenance

Abstract

Large language models (LLMs) can integrate multisource information and generate structured maintenance recommendations. However, their outputs may underestimate risk and fluctuate over time, reducing the practical executability of decisions. To address these issues, this study proposes a trustworthy LLM-based method for bearing predictive maintenance. First, gradient boosting is combined with quantile regression to predict remaining useful life (RUL), followed by operating-condition shrinkage bias correction and width-scaled conformal calibration. The calibrated RUL information and leakage-free features then drive an LLM that determines health stage, risk level, maintenance action, and human-review request. Safety constraints, temporal continuity constraints, and event-level human-machine collaborative review are subsequently applied to produce the final maintenance decision. Comparisons with conventional logistic regression and random forest models delineate the performance boundaries of the LLM, while cross-bearing generalization experiments examine the method’s applicability to different bearings. The proposed method integrates probabilistic RUL information, structured LLM decision-making, multilayer constraints, and human review into a stable, reliable, and traceable maintenance decision system. It provides a solution for trustworthy predictive maintenance of critical mechanical components and establishes an end-to-end pathway from sensed data to maintenance decisions.