Abstract

The modeling of phased-mission systems is difficult and the solving process is complex because of the relevance of the phase tasks and the sharing of components existing in different phases or between phases. To solve the problem, based on the cumulative exposure model, the path sets combination method of phased-mission systems is proposed. Aiming at the problem of the cross-stage correlation of components and its different failure rate in each phase, the cumulative exposure model considering the historical damage of components is used to solve by obtaining the cumulative damage distribution of each component in each phase. Firstly, a phased-mission systems reliability model is build by mapping phased-mission system fault trees into a Bayesian network. By traversing the Bayesian network, the minimal path sets of each phase are obtained. Secondly, the disjoint formulas introduced by variable elimination method are used to do the disjoint operation of the minimal path sets of each phase and the conditional probability relations of the common components are used to reduce the minimal path sets scale. Finally, the minimum disjoint path sets of each phase are combined and summed according to the component conditional probability relation. The path sets combination method of phased-mission systems avoids the large conditional probability table, large storage and large computation problems caused by the excessive discrete states in the traditional Bayesian method and the problem that the PMS-BDD method has strict requirements for variable ordering and is difficult to solve the system reliability with multiple failure distribution types of components. In the end, a phased-mission systems reliability modeling and solving is carried out for a geosynchronous orbit satellite, and compared with the PMS-BDD method, which verifies the correctness of the method.

Highlights

  • 利用阶段代数与向后 BDD 阶段合并规则[4] 将 3 个阶段的 BDD 合并为系统 BDD,即先将阶段 1 的 BDD 与阶段 2 的 BDD 合并再与阶段 3 的合并,如图 6 所示。

  • 1) 本文针对多阶段系统因阶段任务相关、元件 共用造成的系统可靠性难以建模与求解问题,提出 了基于累积损伤模型的多阶段系统路集组合方法, 该方法直接对各阶段不交化后的路集进行组合求 和,没有 PMS⁃BDD 方法对变量排序的严格限制以 及传统 BN 方法因阶段状态离散过多造成的条件概 率表规模大、计算量大问题。 且该方法不限制元件 的寿命分布类型,有更广的适用性; 2) 针对多阶段共用元件在各阶段工作时长、失 效率不同,元件各阶段可靠度难以求取问题,本文考 虑元件历史损伤作用,利用元件累积损伤模型,获得 元件各阶段条件寿命分布,解决了共用元件在各阶 段的可靠度求解问题; 3) 针对路集相关,利用由 BN 变量消元法推导 出的不交化公式实现了路集去相关性。 针对路集规 模过大问题,利用共用元件的条件概率关系,缩减了 路集规模,减小了计算量; 4) 算例分析表明本文方法与 PMS⁃BDD 方法计 算结果一致,验证了本文方法的正确性。

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Summary

Introduction

多阶段系统( phased⁃mission systems,PMS) 广泛 存在于航空航天、汽车等大型复杂设备中。 大型复 杂设备功能的完成往往由一系列阶段任务组成,如 地球同步轨道卫星在转移轨道阶段需经历多次变轨 阶段,如太阳捕获阶段、地球捕获阶段、地球指示阶 段等。 PMS 各阶段之间属于串联关系,任意阶段任 务失败整个系统任务将失败。 PMS 各阶段任务不 同,因此各阶段系统构成、元件配置、任务持续时间 也不尽相同,加之阶段内以及阶段间通常存在元件 共用即同一个元件可能在阶段内多个位置使用或者 在多个阶段被使用,这使得 PMS 各阶段不是彼此独 立存在即存在相关性。 PMS 各个阶段处于的物理 环境通常不同,因此各阶段元件受到的应力水平也 不尽相同,即同一元件在各阶段的失效率可能不同, 如何求解共用元件在各阶段的可靠度以及如何对 现有 PMS 可靠性分析方法可分为 3 类:静态模 型法、动态模型法以及蒙特卡罗仿真方法。 静态模 型法[1⁃3] 主要有可靠性框图方法、故障树方法、贝叶 斯网络 方 法 ( Bayesian networks, BN) 、 二元决策图法、多元决策图法等。 静态模型方法假设元件失效 具有独立性,但实际系统在阶段内以及阶段间可能 存在元件共用,这使得静态分析法受到很大限制。 结合阶段代数与二元决策图 ( binary decision diagram,BDD) 的 PMS⁃BDD 方法[4] 很好地解决了阶 段间的相关性以及元件共用问题,但 BDD 对变量排 序有严格要求,不同的变量排序产生的节点规模相 差极大,以及 PMS⁃BDD 方法难以求取含有多种失 效分布类型元件的系统可靠度;贝叶斯网络方法利

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