Aiming at accurately and efficiently estimating the time-dependent failure probability, a novel time-dependent reliability analysis method based on active learning Kriging model is proposed. Although active surrogate model methods have been used to estimate the time-dependent failure probability, efficiently estimating the time-dependent failure probability by a fewer computational time remains an issue because screening all the candidate samples iteratively by the active surrogate model is time-consuming. This article is intended to address this issue by establishing an optimization strategy to search the new training samples for updating the surrogate model. The optimization strategy is performed in the adaptive sampling region which is first proposed. The adaptive sampling region is adjustable by the current surrogate model in order to provide a proper candidate samples region of the input variables. The proposed method employs the optimization strategy to select the optimal sample to be the new training sample point in each iteration, and it does not need to predict the values of all the candidate samples at every time instant in each iterative step. Several examples are introduced to illustrate the accuracy and efficiency of the proposed method for estimating the time-dependent failure probability by simultaneously considering the computational cost and precision.

为了准确有效地估计时间相关的失效概率，提出了一种基于主动学习克里格模型的时间相关的可靠性分析方法。尽管已经使用主动代理模型方法来估计与时间有关的失败概率，但是通过较少的计算时间来有效估计与时间有关的失败概率仍然是一个问题，因为由主动代理模型迭代地筛选所有候选样本非常耗时。本文旨在通过建立优化策略来搜索新的训练样本以更新代理模型来解决此问题。在首先提出的自适应采样区域中执行优化策略。自适应采样区域可由当前代理模型调整，以提供输入变量的适当候选样本区域。所提出的方法采用优化策略来选择最佳样本作为每次迭代中的新训练样本点，并且不需要在每个迭代步骤中的每个时刻都预测所有候选样本的值。引入几个例子来说明通过同时考虑计算成本和精度来估计时间相关故障概率的方法的准确性和效率。并且不需要在每个迭代步骤中的每个时刻都预测所有候选样本的值。引入几个例子来说明通过同时考虑计算成本和精度来估计时间相关故障概率的方法的准确性和效率。并且不需要在每个迭代步骤中的每个时刻都预测所有候选样本的值。引入几个例子来说明通过同时考虑计算成本和精度来估计时间相关故障概率的方法的准确性和效率。