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On the use of uncertainty importance measures in reliability and risk analysis

机译：关于不确定性重要性度量在可靠性和风险分析中的使用

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This paper discusses the use of uncertainty importance measures in reliability and risk analysis. Such measures are used to rank the importance of components (activities) of complex systems. The measures reflect to what degree the uncertainties on the component level influence the uncertainties on the system level. An example of such a measure is the change in the variance of the reliability of the system when ignoring the uncertainties in the component reliability. The measures are traditionally based on a Bayesian perspective as knowledge-based (subjective) probabilities express the epistemic uncertainties about the reliability and risk parameters introduced. In this paper we carry out a rethinking of the rationale for such measures. What information do they provide compared to the traditional importance measures such as the improvement potential and the Birnbaum measure? To discuss these issues we distinguish between two situations: (A) the key quantities of interest are observable quantities such as the occurrence of a system failure and the number of failures and (B) the key quantities of interest are fictional parameters constructed to reflect the aleatory uncertainties. A new type of combined sets of measures are introduced based on an integration of a traditional measure and a related uncertainty importance measure. A simple reliability example is used to illustrate the analysis and findings.
机译：本文讨论了不确定性重要性度量在可靠性和风险分析中的使用。此类措施用于对复杂系统的组件（活动）的重要性进行排名。这些措施反映了组件级别的不确定性在多大程度上影响了系统级别的不确定性。这种措施的一个例子是当忽略组件可靠性的不确定性时系统可靠性方差的变化。传统上，这些度量基于贝叶斯观点，因为基于知识的（主观）概率表示关于引入的可靠性和风险参数的认知不确定性。在本文中，我们对这种措施的理由进行了重新思考。与传统的重要性衡量标准（例如改进潜力和Birnbaum衡量指标）相比，它们提供了哪些信息？为了讨论这些问题，我们区分了两种情况：（A）关键的关注数量是可观察到的数量，例如系统故障的发生和故障的数量，以及（B）关键的关注数量是为了反映实际情况而构造的虚构参数。不确定性。在传统度量和相关不确定性重要性度量的集成的基础上，引入了一种新的组合度量集。一个简单的可靠性示例用于说明分析和发现。

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