In the time-variant systems, random variables, stochastic processes, and time parameter are regarded as the inputs of time-variant computational model. This results in an even more computationally expensive model what makes the time-variant reliability analysis a challenging task. This paper addresses the problem by presenting an active learning strategy using polynomial chaos expansion (PCE) in an augmented reliability space. We first propose a new algorithm that determines the sparse representation applying statistical threshold to determine the significant terms of the PCE model. This adaptive decision test is integrated into the variational Bayesian method, improving its accuracy and reducing convergence time. The proposed method uses a composite criterion to identify the most significant time instants and the associated training points to enrich the experimental design. By simulations, we compare the performance of the proposed method with respect to other existing time-variant reliability analysis methods.

在时变系统中，随机变量、随机过程和时间参数被视为时变计算模型的输入。这导致计算成本更高的模型，这使得时变可靠性分析成为一项具有挑战性的任务。本文通过在增强可靠性空间中使用多项式混沌展开 (PCE) 提出一种主动学习策略来解决该问题。我们首先提出了一种新算法，该算法确定应用统计阈值来确定 PCE 模型的重要项的稀疏表示。这种自适应决策测试被集成到变分贝叶斯方法中，提高了其准确性并减少了收敛时间。所提出的方法使用复合标准来识别最重要的时刻和相关的训练点，以丰富实验设计。通过仿真，我们比较了所提出方法与其他现有时变可靠性分析方法的性能。