Combining imperfect coverage with digraph models

S. A. Doyle, J. Dugan, M. Boyd
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引用次数: 1

Abstract

We present a prototype implementation of a program to compute the unreliability of a system based on the digraph model of the system and coverage models for individual components. The C program we have written takes as input a system description describing failure modes in terms of a digraph model. This, as well as coverage probability information are used to produce a quantitative unreliability result. The problem being addressed is an important one. The goal is not only to improve the validity of the model being used, but to keep the framework simple, usable and adaptable. A more complete model allows for more realistic analysis. It is essential that life critical systems meet their required level of accuracy. Excluding any of the factors discussed here could result in serious miscalculations. One benefit of performing a quantitative analysis is the digraph models could be used to help analyze the dependability of the system being designed so as to facilitate tradeoff analysis when alternative designs are considered. Another attractive feature of the proposed approach is that it could be used in conjunction with pre-existing tools to enhance the diagnosis process that already exists without significantly affecting the time, money or effort involved. Within the concept of fault diagnosis, a quantitative analysis could allow a prioritization of lists of possible failure causes based on the probabilities associated with those events. In other words, the paths of the digraphs would be weighted so that most likely causes could be considered first.
将不完全覆盖与有向图模型相结合
我们提出了一个基于系统的有向图模型和单个组件的覆盖模型来计算系统不可靠性的程序的原型实现。我们编写的C程序以有向图模型的形式将描述故障模式的系统描述作为输入。这一点,以及覆盖概率信息被用来产生定量的不可靠性结果。正在处理的问题是一个重要的问题。目标不仅是提高所使用模型的有效性,而且要保持框架的简单、可用和适应性。一个更完整的模型可以进行更现实的分析。至关重要的是,生命关键系统满足其所需的精度水平。排除这里讨论的任何因素都可能导致严重的误判。执行定量分析的一个好处是,可以使用有向图模型来帮助分析正在设计的系统的可靠性,以便在考虑备选设计时促进权衡分析。该方法的另一个吸引人的特点是,它可以与已有的工具结合使用,以增强现有的诊断过程,而不会显著影响所涉及的时间、金钱或精力。在故障诊断的概念中,定量分析可以根据与这些事件相关的概率对可能的故障原因列表进行优先级排序。换句话说,将对有向图的路径进行加权,以便首先考虑最可能的原因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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