AK-SYSi: an improved adaptive Kriging model for system reliability analysis with multiple failure modes by a refined U learning function

作者：Yun, Wanying; Lu, Zhenzhou^*; Zhou, Yicheng; Jiang, Xian

来源：Structural and Multidisciplinary Optimization, 2019, 59(1): 263-278.

DOI：10.1007/s00158-018-2067-3

• System reliability analysis
• Refined U learning function
• Easily identifiable failure mode
• Independency of the initial Kriging meta-model

Due to multiple implicit limit state functions needed to be surrogated, adaptive Kriging model for system reliability analysis with multiple failure modes meets a big challenge in accuracy and efficiency. In order to improve the accuracy of adaptive Kriging meta-model in system reliability analysis, this paper mainly proposes an improved AK-SYS by using a refined U learning function. The improved AK-SYS updates the Kriging meta-model from the most easily identifiable failure mode among the multiple failure modes, and this strategy can avoid identifying the minimum mode or the maximum mode by the initial and the in-process Kriging meta-models and eliminate the corresponding inaccuracy propagating to the final result. By analyzing three case studies, the effectiveness and the accuracy of the proposed refined U learning function are verified.

• 出版日期2019-1
• 单位西北工业大学