Most of the existing methods for estimating time-dependent failure credibility (TDFC) are based on optimization algorithms, which may result in a heavy burden on the computational cost and accuracy issue related to the local optimization. In order to achieve the best compromise between computational accuracy and cost, an efficient method is proposed in this work by embedding a single-loop adaptive Kriging model (S-AK) into the dichotomy searching algorithm (S-AK-DSA). The proposed S-AK-DSA can be regarded as a double-loop procedure. In the inner loop, the Kriging model of the real time-dependent performance function (TD-PF) is updated iteratively to accurately predict the signs of the upper/lower boundary of the TD-PF minimum with respect to the time variable at the given membership level. Based on the inner loop, the outer loop searches TDFC by continuously dichotomizing the searching interval of the TDFC. The advantages of S-AK-DSA are mainly manifested in two aspects. Firstly, S-AK-DSA converts the problem of optimizing the exact value of the upper/lower boundary into the problem of accurately identifying their signs, which can avoid the use of optimization algorithms. Secondly, at different membership levels, the S-AK-DSA method chooses the candidate sample pool and continuously updates the current Kriging model of TD-PF, and the adaptive learning function as well as appropriate stopping criterion can effectively reduce the cost of predicting the signs of the upper/lower boundary and improve the computational accuracy. Four case studies are introduced to demonstrate the feasibility and superiority of the proposed S-AK-DSA approach.

现有的大多数估计时间相关故障可信度（TDFC）的方法都是基于优化算法的，这可能会给与本地优化有关的计算成本和准确性问题造成沉重负担。为了在计算精度和成本之间取得最佳折衷，在这项工作中提出了一种有效的方法，即将单环自适应Kriging模型（S-AK）嵌入二分搜索算法（S-AK-DSA）中。提出的S-AK-DSA可以看作是一个双循环过程。在内部循环中，实时更新实时相关性能函数（TD-PF）的Kriging模型，以相对于给定时间变量准确地预测TD-PF最小值的上/下边界的符号会员级别。基于内循环，外循环通过连续二分TDFC的搜索间隔来搜索TDFC。S-AK-DSA的优势主要体现在两个方面。首先，S-AK-DSA将优化上下边界的精确值的问题转换为准确识别其符号的问题，这可以避免使用优化算法。其次，在不同的成员资格级别下，S-AK-DSA方法选择候选样本池并不断更新当前的TD-PF克里格模型，自适应学习功能以及适当的停止准则可以有效地降低预测的成本。上下边界的符号，并提高了计算精度。介绍了四个案例研究，以证明所提出的S-AK-DSA方法的可行性和优越性。