Modeling of stochastic dependency among components in a repairable system is still a challenging task when dealing with the maintenance of multicomponent systems. With the help of stochastic dependency information, failure of a component brings attention to the components having strong interactions with the failed component. With this information, one can plan the maintenance of components in a better way. Since a change in failure probability of a component (due to deterioration or failure of a component in a given time interval) influences the failure probabilities of other components in the system, therefore, in this article, we consider probability of failure to represent the state of the component to model the stochastic dependency among components. We apply the Bayesian belief network to model such scenario of dependency among the components and present two case studies to compute various probabilities. In the first study, expert elicitation is being used, whereas the time between failure of the components is used in the second case to calculate failure probabilities. To illustrate the applicability of the proposed approach, one case study for each is presented. The first case study takes the case of an army truck through expert elicitation approach whereas the second case study deals with a rolling mill gearbox whose time between failure of components was available.

当处理多组件系统的维护时，对可修复系统中组件之间的随机依赖性进行建模仍然是一项艰巨的任务。借助随机依赖性信息，组件的故障会引起人们对与故障组件之间具有强大交互作用的组件的关注。有了这些信息，就可以更好地计划组件的维护。由于组件故障概率的改变（由于给定时间间隔内组件的退化或故障）会影响系统中其他组件的故障概率，因此，在本文中，我们考虑将故障概率表示为状态对组件之间的随机性进行建模。我们应用贝叶斯信念网络对组件之间的依赖关系进行建模，并提出两个案例研究以计算各种概率。在第一项研究中，使用专家启发，而在第二种情况下，使用组件失效之间的时间来计算失效概率。为了说明所提出方法的适用性，每个方法都进行了一个案例研究。第一个案例研究是通过专家启发方法对一辆军用卡车进行案例研究，而第二个案例研究是对一个轧机齿轮箱进行了研究，该齿轮箱的零部件之间的间隔时间是可用的。而在第二种情况下，使用组件之间的间隔时间来计算故障概率。为了说明所提出方法的适用性，每个方法都进行了一个案例研究。第一个案例研究是通过专家启发方法对一辆军用卡车进行案例研究，而第二个案例研究是对一个轧机齿轮箱进行了研究，该齿轮箱的零部件之间的间隔时间是可用的。在第二种情况下，使用组件之间的间隔时间来计算故障概率。为了说明所提出方法的适用性，每个方法都进行了一个案例研究。第一个案例研究是通过专家启发方法对一辆军用卡车进行案例研究，而第二个案例研究是对一个轧机齿轮箱进行了研究，该齿轮箱的零部件之间的间隔时间是可用的。