Abstract
In practical applications, systems often face variable mission durations influenced by dynamic factors such as maintenance personnel availability, environmental conditions, and real-time operational demands. This paper proposes a condition-based maintenance (CBM) strategy for mission-oriented systems (MOS), addressing the complexities of stochastic mission durations and system degradation. We design a multistate system (MSS) reliability framework that explicitly models dynamic transitions between discrete performance states defined by mission profiles via a discrete-time Markov chain (DTMC) and degradation levels via a Wiener process. Unlike traditional binary-state models, our approach captures degradation state shifts influenced by mission duration, enabling adaptive maintenance policies for systems operating in multistate conditions. The maintenance optimization problem is formulated as a Markov decision process (MDP) via backward dynamic programming to minimize expected maintenance costs. Numerical simulations and sensitivity analyses validate the model's efficacy and adaptability in optimizing maintenance for unmanned aerial vehicles (UAVs). The findings underscore the importance of minimizing maintenance and inspection time, and tailoring strategies to mission characteristics and system costs.