In this paper, we consider a single-unit system which is subject to shocks and deterioration over time. We assume that the shocks occur based on a counting process. Each incoming shock affects the system in two ways; the fatal shock causes the system failure whereas the effective shock weakens the system by increasing its age. The system degradation is modeled based on a general degradation path (GDP) model which depends on the impact of shocks received during system operation and the system virtual age. Both soft and hard failures are considered and a new degradation-threshold-shock (DTS) model is proposed. The main focus of this paper is to provide a dynamic maintenance policy based on the current conditions of the system while both environmental and operational conditions of the system are uncertain. In this regard, a condition-based maintenance (CBM) policy with periodic inspection is developed. Both corrective and preventive replacements are taken into account and preventive replacement threshold is updated at each inspection time. The update is done in the light of new information on the number and magnitude of shocks using a rolling horizon approach within the Bayesian framework. A simulation study has been conducted to show the applicability and efficiency of the proposed method and the effect of prior information on the maintenance decisions has been studied.

在本文中，我们考虑一个单单元系统，该系统会随着时间的流逝而受到冲击和退化。我们假设冲击是基于计数过程发生的。每次传入的冲击都会以两种方式影响系统。致命的电击会导致系统故障，而有效的电击会通过增加使用寿命来削弱系统。基于一般退化路径（GDP）模型对系统退化进行建模，该模型取决于系统运行期间收到的冲击的影响以及系统虚拟寿命。同时考虑了软故障和硬故障，并提出了一种新的退化阈值休克（DTS）模型。本文的主要重点是在系统的环境和运行状况都不确定的情况下，根据系统的当前状况提供动态维护策略。在这方面，制定了带有定期检查的基于状态的维护（CBM）策略。纠正性更换和预防性更换都考虑在内，并且预防性更换阈值在每个检查时间都会更新。更新是根据贝叶斯框架内的滚动水平方法，根据有关冲击的数量和程度的新信息进行的。仿真研究表明该方法的适用性和有效性，并且研究了先验信息对维护决策的影响。更新是根据贝叶斯框架内的滚动水平方法，根据有关冲击的数量和程度的新信息进行的。仿真研究表明该方法的适用性和有效性，并且研究了先验信息对维护决策的影响。更新是根据贝叶斯框架内的滚动水平方法，根据有关冲击的数量和程度的新信息进行的。仿真研究表明该方法的适用性和有效性，并且研究了先验信息对维护决策的影响。