Componentwise decomposition for an efficient reliability computation of systems with repairable components

M. Balakrishnan, Kishor S. Trivedi
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引用次数: 17

Abstract

Fault trees and Markov chains are commonly used for dependability modeling. Markov chains are powerful in that various kinds of dependencies can be easily modeled that fault tree models have difficulty capturing, but the state space grows exponentially in the number of components. Fault tree models are adequate for computing the reliability of nonrepairable systems, but a state space description becomes necessary for repairable systems due to induced dependencies (even when all failure and repair processes are otherwise independent). We demonstrate that a decomposition approach can be used to avoid a full-system Markov reliability model for repairable systems with independent failure and repair processes. For an n-component system, n 3-state sub-models can replace a full-system monolithic model. This is an approximation because the parameters used in the sub-model are approximately derived from the monolithic model.<>
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基于部件分解的可修部件系统可靠性计算方法
故障树和马尔可夫链通常用于可靠性建模。马尔可夫链的强大之处在于,它可以很容易地对故障树模型难以捕获的各种依赖关系进行建模,但状态空间随着组件的数量呈指数增长。故障树模型足以计算不可修复系统的可靠性,但由于诱导依赖性(即使所有故障和修复过程在其他方面是独立的),状态空间描述对于可修复系统是必要的。我们证明了一种分解方法可以用来避免具有独立故障和维修过程的可修系统的全系统马尔可夫可靠性模型。对于一个有n个组件的系统,n个3状态子模型可以代替一个完整的系统整体模型。这是一个近似值,因为子模型中使用的参数是近似地从整体模型中导出的。
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