Reliability-constrained maintenance optimization for safety-critical wind turbine bearings with distribution-free predictive uncertainty

Abstract

Bearing failures in offshore wind turbines pose asymmetric safety and economic risks, where missed detections may lead to catastrophic events while false alarms trigger costly unnecessary interventions. This study formulates bearing fault diagnosis as a risk-constrained maintenance optimization problem, in which expected operational cost is minimized subject to an explicit probabilistic constraint on the miss rate. To operationalize this constraint without distributional assumptions, we employ a distribution-free uncertainty quantification framework combining probabilistic calibration via temperature scaling (TS), selective classification, and split conformal prediction. TS improves probability reliability, while conformal prediction provides finite-sample coverage guarantees that directly support safety constraint enforcement. Validation on the Fraunhofer LBF wind turbine bearing dataset under strict group-wise cross-validation demonstrates 100% recall for the most safety-critical fault modes (inner race and rolling element defects, exhibiting rapid failure propagation) and empirical coverage exceeding nominal levels. Risk–coverage analysis confirms that uncertainty-aware rejection substantially reduces misclassification risk. A Monte Carlo decision analysis for a 50-turbine offshore wind farm indicates up to 8.5% annual operations and maintenance (O&M) cost reduction and 15% downtime reduction under selective policies, with robustness across sensitivity scenarios. The results demonstrate that distribution-free uncertainty guarantees enable economically rational and safety-compliant maintenance decisions in safety-critical wind energy systems.