The purpose of this paper is to propose and systematically evaluate coordinated maintenance policies for multi-component systems undergoing stochastic degradation in the presence of visit-level economic dependencies. By explicitly incorporating condition monitoring and system-level decision mechanisms, the study aims to assess the performance gains achievable over conventional corrective and fixed-interval preventive maintenance strategies. A unified analytical and Monte Carlo simulation framework is employed to quantify the impact of increasing levels of coordination on expected maintenance cost, intervention structure, and visit frequency, thereby demonstrating the effectiveness of opportunistic and condition-based maintenance in improving lifecycle cost efficiency and operational stability.
This paper presents a rigorous analytical and simulation-based investigation of maintenance policy design for multi-component systems subject to heterogeneous stochastic degradation and failure processes. In such systems, maintenance decisions are inherently interdependent due to visit-level setup costs, which generate strong economic coupling among component-level interventions. Maintenance strategies that disregard these dependencies and rely on isolated decision-making may therefore result in inefficient intervention timing and unnecessarily high lifecycle costs. To explicitly capture these interactions, four maintenance policies with increasing levels of coordination and condition awareness are systematically analyzed: Policy (0) corrective maintenance, Policy (1) fixed-interval preventive maintenance, Policy (2) semi-urgent condition-based maintenance, and Policy (3) opportunistic condition-based maintenance. Each policy is formally specified through explicit decision rules based on component age, stochastic degradation state, and system access events. Performance is evaluated over a finite planning horizon using Monte Carlo simulation, enabling consistent estimation of expected total maintenance cost, replacement composition, maintenance visit frequency, and associated confidence intervals. The numerical results reveal a clear and robust ordering in policy performance. Policy (1) achieves limited cost reductions relative to Policy (0) by reducing failure-driven interventions. Policy (2) delivers further improvements by allowing maintenance actions to be scheduled closer to component end-of-life while avoiding emergency interventions. The most substantial performance gains are obtained under Policy (3), which systematically exploits maintenance access opportunities to coordinate multiple component replacements within a single visit. This coordinated strategy yields pronounced reductions in total maintenance cost and maintenance visit frequency, while also enhancing operational regularity. Overall, the findings demonstrate that maintenance policies which explicitly integrate condition monitoring and economic dependencies at the system level provide significant improvements in cost efficiency and intervention structure. The proposed framework offers a robust and scalable foundation for the design, comparison, and implementation of coordinated maintenance strategies in complex industrial systems.
The numerical results establish a clear and consistent ordering of maintenance policy performance. Fixed-interval preventive maintenance provides limited cost improvements relative to corrective maintenance by reducing the incidence of failure-induced interventions. Condition-based maintenance yields further gains by aligning maintenance actions more closely with component degradation states, thereby limiting premature replacements and emergency repairs. The most substantial performance improvements are obtained under opportunistic condition-based maintenance, which leverages maintenance access opportunities to coordinate multiple component replacements within a single visit. This coordinated strategy leads to pronounced reductions in expected maintenance cost and visit frequency, alongside improved intervention regularity at the system level.
The present study is subject to certain limitations. The analysis assumes perfect condition observability and known stochastic degradation processes, which may not fully capture measurement uncertainty or modeling errors encountered in practical applications. Moreover, the evaluation is conducted over a finite planning horizon and does not explicitly incorporate system availability constraints or production-related losses. Notwithstanding these limitations, the results underscore important implications for maintenance planning, highlighting the substantial benefits of integrating condition information and visit-level economic dependencies into system-level decision-making. The proposed framework provides a robust basis for future extensions toward more realistic and industrially applicable coordinated maintenance models.
The results of this research offer practical guidance for the management of maintenance activities in complex multi-component systems. They demonstrate that substantial improvements in cost efficiency and intervention planning can be achieved by adopting system-level maintenance policies that explicitly account for economic dependencies among components. In particular, opportunistic condition-based maintenance allows maintenance access opportunities to be systematically exploited, enabling the consolidation of multiple component replacements within a single intervention. The proposed framework provides a decision-support basis for practitioners seeking to integrate condition monitoring data into coordinated maintenance strategies, thereby reducing unplanned interventions and improving overall maintenance effectiveness.
The implementation of coordinated and condition-based maintenance policies has broader societal relevance through their contribution to the reliability, safety, and sustainability of industrial systems. By reducing the incidence of unexpected failures and emergency interventions, such approaches enhance operational safety and mitigate risks to personnel and surrounding communities. More predictable and efficiently planned maintenance activities can also improve working conditions by limiting reactive interventions and associated stress for maintenance staff. Furthermore, improved asset utilization and extended component lifetimes contribute to more sustainable resource use, supporting environmental responsibility and the long-term resilience of critical infrastructure systems.
This work offers original contributions by developing a unified analytical and simulation-based framework for the systematic comparison of maintenance policies in multi-component systems subject to stochastic degradation and visit-level economic dependencies. In contrast to traditional approaches centered on independent component-level decisions, the proposed methodology explicitly incorporates condition information and system-level coordination, including opportunistic maintenance mechanisms. The originality of the study resides in the formal definition and consistent evaluation of maintenance strategies with progressively increasing coordination levels. Its value lies in quantitatively demonstrating the economic and operational benefits of coordinated condition-based maintenance, thereby providing a rigorous foundation for the design of efficient maintenance policies in complex industrial environments.
