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AK-PDEMi: A failure-informed enrichment algorithm for improving the AK-PDEM in reliability analysis
Mechanical Systems and Signal Processing ( IF 8.4 ) Pub Date : 2022-06-22 , DOI: 10.1016/j.ymssp.2022.109435
Tong Zhou, Stefano Marelli, Bruno Sudret, Yongbo Peng

A failure-informed enrichment algorithm is devised to improve the performance of the existing adaptive Kriging-probability density evolution method (AK-PDEM) for reliability analysis. This improved method is named the AK-PDEMi. Contrary to empirically prescribing the sample size of representative points in the existing AK-PDEM, the representative point set in the AK-PDEMi is sequentially enriched by new sets of representative points generated by a failure-informed enrichment scheme, which aims to sequentially making fine partitions of the key sub-regions where the representative points make critical contributions to the failure probability. In this regard, a double-loop configuration is devised: the inner loop adaptively refines the accuracy of Kriging model to reduce the Kriging-induced error, and the outer loop involves the failure-informed enrichment process to alleviate the PDEM-associated discretization error. The outer and inner loops are complementary and proceed sequentially until both of their convergence criteria are satisfied. Three numerical examples are studied and comprehensive comparisons are made between the proposed AK-PDEMi and other conventional reliability algorithms. Results show that the AK-PDEMi shows remarkable advantage over the existing AK-PDEM.



中文翻译:

AK-PDEMi:一种用于改进 AK-PDEM 在可靠性分析中的故障通知富集算法

设计了一种故障通知富集算法,以提高现有自适应克里金概率密度演化方法 (AK-PDEM) 的可靠性分析性能。这种改进的方法被命名为 AK-PDEMi。与在现有 AK-PDEM 中根据经验规定代表点的样本大小相反,AK-PDEMi 中的代表点集由故障通知富集方案生成的新代表点集顺序富集,该方案旨在顺序制作精细代表点对失效概率做出关键贡献的关键子区域的分区。在这方面,设计了一个双环配置:内环自适应地改进克里金模型的准确性,以减少克里金引起的误差,外环涉及故障通知富集过程,以减轻与 PDEM 相关的离散化误差。外环和内环是互补的,并按顺序进行,直到满足它们的两个收敛标准。研究了三个数值例子,并对所提出的 AK-PDEMi 和其他常规可靠性算法进行了综合比较。结果表明,AK-PDEMi 与现有的 AK-PDEM 相比具有显着优势。

更新日期:2022-06-22
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