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Probability and Possibility-Based Representations of Uncertainty in Fault Tree Analysis

机译:故障树分析中基于概率和可能性的不确定性表示

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摘要

Expert knowledge is an important source of input to risk analysis. In practice, experts might be reluctant to characterize their knowledge and the related (epistemic) uncertainty using precise probabilities. The theory of possibility allows for imprecision in probability assignments. The associated possibilistic representation of epistemic uncertainty can be combined with, and transformed into, a probabilistic representation; in this article, we show this with reference to a simple fault tree analysis. We apply an integrated (hybrid) probabilistic-possibilistic computational framework for the joint propagation of the epistemic uncertainty on the values of the (limiting relative frequency) probabilities of the basic events of the fault tree, and we use possibility-probability (probability-possibility) transformations for propagating the epistemic uncertainty within purely probabilistic and possibilistic settings. The results of the different approaches (hybrid, probabilistic, and possibilistic) are compared with respect to the representation of uncertainty about the top event (limiting relative frequency) probability. Both the rationale underpinning the approaches and the computational efforts they require are critically examined. We conclude that the approaches relevant in a given setting depend on the purpose of the risk analysis, and that further research is required to make the possibilistic approaches operational in a risk analysis context.
机译:专家知识是风险分析投入的重要来源。在实践中,专家可能不愿意使用精确的概率来描述其知识和相关的(流行性)不确定性。可能性理论允许概率分配不精确。认知不确定性的相关可能表示可以与概率表示结合并转化为概率表示;在本文中,我们将参考简单的故障树分析来说明这一点。我们为故障树的基本事件的(极限相对频率)概率值的认知不确定性的联合传播应用了一个综合的(混合)概率-可能性计算框架,并且我们使用了可能性-概率(probability-possibility ),用于在纯概率和概率环境中传播认知不确定性的转换。比较了关于顶部事件(限制相对频率)概率的不确定性表示的不同方法(混合,概率和可能性)的结果。这些方法的基本原理和所需的计算工作都经过严格审查。我们得出的结论是,在给定背景下相关的方法取决于风险分析的目的,并且需要进一步的研究以使可能的方法在风险分析环境中可操作。

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