Time-dependent global reliability sensitivity can quantify the effect of input variables in their whole distribution ranges on the time-dependent failure probability. To efficiently estimate it to help researchers control the time-dependent failure probability, a novel method is proposed. The proposed method transforms the estimation of unconditional-conditional time-dependent failure probabilities into that of the unconditional-conditional probability density functions (PDFs) of the minimum of time-dependent performance function. Firstly, the minimum of time-dependent performance function is evaluated by adaptive Kriging, and its unconditional-conditional fractional moments are estimated by multiplicative dimensional reduction method (M-DRM). Then, the maximum entropy (MaxEnt) constrained by these fractional moments is used to estimate the unconditional-conditional PDFs, on which the unconditional-conditional time-dependent failure probabilities can be obtained. Finally, the one-dimensional Gaussian quadrature is applied to estimate the time-dependent global reliability sensitivity indices. Due to the high efficiency of adaptive Kriging for estimating the minimum of time-dependent performance function, the avoidance of dimensional curse by M-DRM, and the high efficiency of MaxEnt constrained by fractional moments for estimating PDF, the proposed method can reduce the computational cost dramatically.

时间相关的全局可靠性敏感性可以量化输入变量在其整个分布范围内对时间相关的故障概率的影响。为了有效地估计它，以帮助研究人员控制与时间有关的失效概率，提出了一种新的方法。所提出的方法将无条件的时间相关的故障概率的估计转换为时间相关的性能函数的最小值的无条件条件的概率密度函数（PDF）的估计。首先，通过自适应克里格法评估与时间相关的性能函数的最小值，并通过乘维降阶方法（M-DRM）估计其无条件的分数矩。然后，由这些分数矩约束的最大熵（MaxEnt）用于估计无条件的PDF，在该PDF上可以获得无条件的时间相关的故障概率。最后，将一维高斯正交应用于估计与时间有关的全局可靠性灵敏度指标。由于自适应Kriging能够高效地估计与时间相关的性能函数的最小值，M-DRM避免了维数诅咒，并且因分数矩而限制了MaxEnt的PDF估计效率很高，因此该方法可以减少计算量成本急剧上升。利用一维高斯积分来估计与时间有关的全局可靠性灵敏度指标。由于自适应Kriging能够高效地估计与时间相关的性能函数的最小值，M-DRM避免了维数诅咒，并且因分数矩而限制了MaxEnt的PDF估计效率很高，因此该方法可以减少计算量成本急剧上升。利用一维高斯积分来估计与时间有关的全局可靠性灵敏度指标。由于自适应Kriging能够高效地估计与时间相关的性能函数的最小值，M-DRM避免了维数诅咒，并且因分数矩而限制了MaxEnt的PDF估计效率很高，因此该方法可以减少计算量成本急剧上升。