Uncertainty-Aware Automated Maintenance Decision-Making for Elevator Mechatronic Systems

Abstract

To address the fundamental challenge of translating probabilistic health assessments into reliable maintenance decisions for elevator mechatronic systems, this paper proposes the first uncertainty-aware dual-threshold optimization framework that systematically bridges Bayesian perception with optimal decision-making. Leveraging posterior health distributions (mean and variance) output by the Bayesian Fusion for Elevator Health Assessment, the method constructs supervised learning pairs from multi-year historical records. A three-objective composite loss function-integrating matching accuracy, uncertainty coverage, and decision separability-is optimized via Bayesian optimization to calibrate optimal thresholds. Critically, the 95% confidence interval is explicitly embedded as a constraint, ensuring high-uncertainty equipment remains safely distant from decision boundaries. Validated on one-year operational data from 100 elevators, the framework achieves 84.0% matching accuracy (17.0% improvement), 0.71 maintenance cost index, and 0.12 safety risk index, delivering Pareto-optimal trade-offs among decision accuracy, maintenance economy, and operational safety. The offline learning and online deployment paradigm requires no additional hardware and integrates directly into existing supervision platforms.