Maintenance planning and execution are challenging tasks for every system with complex structure. Interdependent nature of the components that builds up the system may have significant effect on system integrity. While preventive maintenance actions can be carried out in a more planned fashion, corrective actions are more time sensitive as they directly affect the availability of the system. This study proposes a cost-effective dynamic Bayesian network modeling scheme to be used in the planning of corrective maintenance actions on systems having hidden components which have stochastic and structural dependencies. In such context, the regenerative air heater system which is a key element of a power plant is taken into consideration. The proposed maintenance framework offers several methods, each aiming to balance the cost with the probability effect using a normalization procedure. The methodologies are extensively simulated for sensitivity analysis under various downtime cost values. Fault effect methods with worst state probability efficiency measures give the least total cost for all downtime cost values and their distinction becomes significant as this value increases. Further statistical analysis concludes that considerable gains on maintenance costs can be achieved by the proposed approach.

维护计划和执行对于具有复杂结构的每个系统都是具有挑战性的任务。构建系统的组件的相互依赖性质可能对系统完整性产生重大影响。尽管可以以更有计划的方式执行预防性维护措施，但纠正措施对时间更敏感，因为它们直接影响系统的可用性。这项研究提出了一种具有成本效益的动态贝叶斯网络建模方案，该方案可用于对具有隐藏组件（具有随机和结构依赖性）的系统进行纠正性维护措施的计划。在这种情况下，考虑了作为电厂关键要素的蓄热式空气加热器系统。建议的维护框架提供了几种方法，每个目标都是使用标准化程序来平衡成本和概率效应。在各种停机时间成本值下，对方法进行了广泛的仿真，以进行灵敏度分析。具有最坏状态概率效率度量的故障效应方法对于所有停机成本值给出的总成本最低，并且随着该值的增加，它们的区别变得很明显。进一步的统计分析得出的结论是，通过所提出的方法可以实现可观的维护成本收益。