Failure-probability-based global sensitivity (FP-GS) analysis can measure the effect of the input uncertainty on the failure probability. The state-of-the-art for estimating the FP-GS are less efficient for the rare failure event and the implicit performance function case. Thus, an adaptive Kriging nested Importance Sampling (AK-IS) method is proposed in this work to efficiently estimate the FP-GS. For eliminating the dimensionality dependence in the calculation, an equivalent form of the FP-GS transformed by the Bayes’ formula is employed by the proposed method. Then the AK model is nested into IS for recognizing the failure samples. After all the failure samples are correctly identified from the IS sample pool, the failure samples are transformed into those subjected to the original conditional probability density function (PDF) on the failure domain by the Metropolis–Hastings algorithm, on which the conditional PDF of the input on the failure domain can be estimated for the FP-GS finally. The proposed method highly improves the efficiency of estimating the FP-GS comparing with the state-of-the-art, which is illustrated by the results of several examples in this paper.

基于故障概率的全局灵敏度（FP-GS）分析可以测量输入不确定性对故障概率的影响。对于罕见故障事件和隐式性能函数情况，用于估计FP-GS的最新技术效率较低。因此，在这项工作中提出了一种自适应克里格嵌套重要抽样（AK-IS）方法，以有效地估计FP-GS。为了消除计算中的尺寸依赖性，所提出的方法采用了由贝叶斯公式转换后的FP-GS的等效形式。然后将AK模型嵌套到IS中以识别故障样本。从IS样本池中正确识别出所有故障样本后，通过Metropolis-Hastings算法将故障样本转换为在故障域上经受原始条件概率密度函数（PDF）的样本，最终可以为FP-GS估计故障域上输入的条件PDF 。与现有技术相比，该方法大大提高了FP-GS的估计效率。