Remaining Useful Life Estimation for Aircraft Engines with Risk-Aware Prediction Intervals via Conformalized Quantile Regression

Abstract

In aerospace maintenance, remaining useful life (RUL) prediction is critical for flight safety, system availability, and long-term sustainment. While data-driven and machine learning (ML) approaches have improved RUL accuracy, most methods provide only point estimates and either omit uncertainty quantification (UQ) or rely on fixed, fleet-wide safety margins. Without reliable uncertainty estimates, even accurate point predictions offer limited value for safety-critical maintenance decisions. This paper presents a comprehensive framework for RUL prediction that jointly addresses point estimation, uncertainty quantification, and aerospace risk preferences. The framework combines a gradient boosting regressor (GBR) for point predictions with asymmetric conformalized quantile regression (CQR) to produce prediction intervals that communicate uncertainty. The asymmetric formulation of CQR allocates miscoverage unequally between interval bounds to reduce the likelihood of overly optimistic predictions, thereby aligning interval construction with the preference to avoid late maintenance intervention. The framework is evaluated on NASA's Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) benchmark dataset. Across all four benchmark subsets, the framework achieves test RMSE values of 13.26-16.85 with empirical coverage of 88-92% at 90% nominal coverage. These results demonstrate accurate point predictions and well-calibrated uncertainty intervals aligned with the requirements of safety-critical maintenance planning.