Reliability-calibrated mission-window assessment for bearing maintenance decisions under varying operating conditions

Abstract

Maintenance decisions for degrading bearings depend on whether an asset can complete the next planned operating interval. The study assesses mission-window survival probabilities and their suitability for maintenance decisions. XJTU-SY provides the primary run-to-failure data, and PRONOSTIA provides external experimental validation. Threshold, linear, ensemble, history-aware and calibrated models are compared under bearing- and condition-disjoint splits using discrimination, calibration and decision-risk measures. Advanced and calibrated variants improve selected cases but do not uniformly outperform random forest or root-mean-square thresholds; model rankings also vary by evaluation criterion. The validation uses experimental run-to-failure bearing datasets rather than industrial field deployment. PRONOSTIA provides external experimental validation, but horizon units are acquisition file indices and may not have the same physical-time meaning as XJTU-SY. Calibrated probabilities can support continued operation, inspection or intervention after site-specific validation of horizons, thresholds and failure consequences. The study unifies horizon-specific reliability targets, disjoint shift evaluation, probability calibration and maintenance-facing risk metrics rather than proposing a new predictor.