Quantify Uncertainty Beyond Covariate Shift in RUL Estimation by Conformal Prediction

Recent advancements in Automation Science and Engineering (ASE) have improved Prognostics and Health Management (PHM) methods for estimating the remaining useful life (RUL) of industrial equipment. While accurate RUL prediction is crucial for machinery reliability, deep learning (DL) models often neglect real-world uncertainty. Uncertainty quantification (UQ) provides essential confidence measures for risk-aware maintenance, yet faces challenges like covariate shift and reliance on optimization or distribution assumptions. To address these issues, we introduce an Adaptive Conformalized RUL Predictor (ACRP) that surpasses traditional methods. Our approach combines conformal prediction with a group-weighted strategy guided by domain knowledge to achieve UQ in a model-free, distribution-free manner with rigorous theoretical guarantee, ensuring reliable prediction intervals that meet efficiency and validity criteria under covariate shift. It is adaptable to any model, and extensive experiments demonstrate its robustness and versatility, enhancing the trustworthiness of AI-driven PHM solutions in industrial environments.