A hybrid algorithm for reliability analysis combining Kriging and subset simulation importance sampling

To solve the problem of large computation when failure probability with time-consuming numerical model is calculated, we propose an improved active learning reliability method called AK-SSIS based on AK-IS algorithm [1]. First, an improved iterative stopping criterion in active learning is presented so that iterations decrease dramatically. Second, the proposed method introduces Subset simulation importance sampling (SSIS) into the active learning reliability calculation, and then a learning function suitable for SSIS is proposed. Finally, the efficiency of AK-SSIS is proved by two academic examples from the literature. The results show that AK-SSIS requires fewer calls to the performance function than AK-IS, and the failure probability obtained from AK-SSIS is very robust and accurate. Then this method is applied on a spur gear pair for tooth contact fatigue reliability analysis.

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