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An active learning reliability method combining Kriging constructed with exploration and exploitation of failure region and subset simulation

机译：一种主动学习可靠性方法，与失败区域的探索和利用构成克里格和子集模拟

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摘要

Subset simulation (SS) is a powerful reliability analysis method by transforming a rare failure event into a sequence of multiple intermediate failure events with larger probabilities. Recently, the metamodel-assisted SS method has attracted great attention to improve the efficiency of reliability analysis for time-consuming performance functions. However, in cases with highly nonlinear performance functions and small failure probabilities, it is still difficult to balance the estimation accuracy of failure probability and the computational cost on the construction of metamodels. To address this challenge, an active learning reliability method combining Kriging constructed with exploration and exploitation of failure region and subset simulation (AKEE-SS) is proposed in this paper. The exploration and exploitation of failure region benefit from the samples in the first and last levels of SS, respectively. To control the influence of metamodel error on the estimation of failure probability, two error measure functions are developed to quantify the influence and be considered in the termination conditions of metamodel update. Five numerical examples are used to test the performance of AKEESS. Results indicate that AKEE-SS is an accurate and efficient reliability analysis method for problems with highly nonlinear performance functions and small failure probabilities.
机译：子集仿真（SS）是一种强大的可靠性分析方法，通过将稀有故障事件转换为具有更大概率的多个中间故障事件序列。最近，Metomodel辅助的SS方法引起了极大的关注，提高了耗时性能功能的可靠性分析效率。然而，在具有高度非线性性能功能和小故障概率的情况下，仍然难以平衡故障概率的估计准确性和计算成本对元拱起的构造。为了解决这一挑战，在本文中提出了一种主动学习可靠性方法，与失败区域的勘探和开发和子集模拟（AKEE-SS）建立了构建的克里格。失败区域的勘探和开发分别从SS的第一个和最后一级的样本中受益。为了控制元模型误差对故障概率估计的影响，开发了两个错误测量功能以量化影响并考虑在元模型更新的终止条件下。五个数值例子用于测试Akeess的性能。结果表明，AKEE-SS是一种准确高效的可靠性分析方法，用于高度非线性性能功能和小故障概率。

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