Optimal Selective Maintenance Strategy of Drone Nest Subsystem Subject to Fuzzy Uncertainty and Competing Failure

This paper develops a novel selective maintenance optimization framework for drone nest subsystems operating under multi‐mission cycles with limited resources. The study uniquely addresses two interconnected sources of complexity: fuzzy uncertainty arising from imprecise component degradation states and failure thresholds, and competing failure (CF) mechanisms that influence system reliability. A comprehensive maintenance model integrating both factors is proposed, wherein fuzzy residual life is characterized through membership functions and possibility theory, and CFs are captured via a reliability structure that accounts for their interdependence. The optimization objective is to maximize the next mission's success probability while satisfying constraints on maintenance time and cost. The problem is formulated as a fuzzy nonlinear programming model, solved by an efficient algorithm that identifies both the optimal subset of components for maintenance and their corresponding maintenance levels. Experimental results demonstrate that the proposed approach yields superior performance compared to conventional deterministic models and strategies that neglect failure competition. The framework enables more robust and cost‐effective maintenance decisions, thereby improving operational readiness and mission assurance for drone nest systems operating under uncertain and complex failure conditions.