In many contexts, it is of interest to assess the impact of selected parameters on the failure probability of a physical system. To this end, one can perform conditional reliability analysis, in which the probability of failure becomes a function of these parameters. Computing conditional reliability requires recomputing failure probabilities for a sample sequence of the parameters, which strongly increases the already high computational cost of conventional reliability analysis. We alleviate these costs by reusing information from previous reliability computations in each subsequent reliability analysis of the sequence. The method is designed using two variants of importance sampling and performs information transfer by reusing importance densities from previous reliability analyses in the current one. We put forward a criterion for selecting the most informative importance densities, which is robust with respect to the input space dimension, and use a recently proposed density mixture model for constructing effective importance densities in high dimensions. The method controls the estimator coefficient of variation to achieve a prescribed accuracy. We demonstrate its performance by means of two engineering examples featuring a number of pitfall features such as strong non-linearity, high dimensionality and small failure probabilities ($10^{-5}\text{to}10^{-9}$).

在许多情况下，评估所选参数对物理系统故障概率的影响是很重要的。为此，可以执行条件可靠性分析，其中故障概率成为这些参数的函数。计算条件可靠性需要针对参数的样本序列重新计算故障概率，这极大地增加了常规可靠性分析已经很高的计算成本。我们通过在序列的每个后续可靠性分析中重用先前可靠性计算中的信息来减轻这些成本。该方法使用重要性采样的两种变体进行设计，并通过在当前的可靠性分析中重用先前的可靠性分析中的重要性密度来执行信息传递。我们提出了一个选择信息量最大的重要性密度的标准，该标准相对于输入空间维数是鲁棒的，并使用最近提出的密度混合模型来构建高维的有效重要性密度。该方法控制估计器的变化系数以达到规定的精度。我们通过两个工程示例来展示其性能，这些示例具有许多陷阱特征，例如强非线性，高维度和小故障概率（$\mathrm{1个}{0}^{-5}\text{至}\mathrm{1个}{0}^{-9}$）。