Maintenance activities often require an identical preparatory work. Therefore, a joint execution of such maintenance activities may save a substantial cost. In this work, we consider the problem of optimizing the total maintenance cost of a multi-component repairable system by grouping of components and carrying out maintenance activities on group(s) of components of a complex system. More specifically, we propose a maintenance grouping cost optimization model based on the stochastic dependency as well as economic dependency among components in a system. The stochastic dependency modeling is done using Bayesian network by considering the failure probability of components as a measure of failure interactions among components. Penalty functions are formulated due to the shift of individual optimal maintenance time of components to find the optimum joint maintenance interval and associated cost benefits. Finally, a case study on a diesel engine of a diesel power plant involving 10 components (components of diesel engine, air intake system, and turbocharger) is presented to illustrate the proposed approach.

维护活动通常需要相同的准备工作。因此，联合执行此类维护活动可以节省大量成本。在这项工作中，我们考虑了通过对组件进行分组并对复杂系统的组件组进行维护活动来优化多组件可修复系统的总维护成本的问题。更具体地说，我们基于系统组件之间的随机依赖关系和经济依赖关系，提出了维护分组成本优化模型。随机依赖性建模是使用贝叶斯网络完成的，方法是将组件的故障概率视为组件之间故障交互的一种度量。惩罚功能的制定是由于组件各自的最佳维护时间的变化，以找到最佳的联合维护间隔和相关的成本效益。最后，以一个包含10个组件（柴油机，进气系统和涡轮增压器的组件）的柴油发电厂的柴油机为例，以说明该方法。