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System reliability under prescribed marginals and correlations: Are we correct about the effect of correlations?

机译：在规定的边际和相关性条件下的系统可靠性：我们对相关性的影响是否正确？

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摘要

Many reliability problems involve correlated random variables. However, the probabilistic specification of random variables is commonly given in terms of marginals and correlations, which is actually incomplete because the data dependency needed for distribution modeling is not characterized. The implicitly assumed Gaussian dependence structure is not necessarily true and may bias the reliability result. To investigate the effect of correlations on system reliability under non-Gaussian dependence structures, a general approach to the probability distribution model construction based on the pair-copula decomposition is proposed. Numerical examples have highlighted the importance of dependence modeling in system reliability since large deviation in failure probabilities under different dependencies is observed. The method for identifying the best fit data dependency from data is later provided and illustrated with a retaining wall. It is demonstrated that the reliability result can be accurately estimated if the qualitative dependence structure is complemented to the available quantitative statistical information. (C) 2018 Elsevier Ltd. All rights reserved.
机译：许多可靠性问题涉及相关的随机变量。但是，随机变量的概率性规范通常是根据边际和相关性给出的，实际上这是不完整的，因为没有特征化分布建模所需的数据依赖性。隐式假定的高斯依赖结构不一定是正确的，并且可能会使可靠性结果产生偏差。为了研究相关性对非高斯依赖结构下系统可靠性的影响，提出了一种基于对-关联分解的概率分布模型构建的通用方法。数值示例强调了依赖关系建模在系统可靠性中的重要性，因为观察到在不同依赖关系下故障概率的较大偏差。稍后提供用于从数据中识别最佳拟合数据依赖性的方法，并用挡土墙进行说明。结果表明，如果定性依赖结构与可用的定量统计信息互补，则可以准确地估计可靠性结果。 （C）2018 Elsevier Ltd.保留所有权利。

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