AK-SYSi: an improved adaptive Kriging model for system reliability analysis with multiple failure modes by a refined U learning function
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.
KeywordsSystem reliability analysis Refined U learning function Easily identifiable failure mode Independency of the initial Kriging meta-model
This work was supported by the National Natural Science Foundation of China (Grant 51775439, 11602197) and the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University (Grant CX201708).
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