Reliability Analysis Method Combining Cross-entropy Adaptive Sampling and ALK Model

doi:10.3901/JME.2024.16.073

Abstract

Abstract: When estimating very small failure probability, the traditional methods based on active learning Kriging(ALK) model usually needs too many candidate points. This problem will cause the learning process to be very time-consuming. To address this problem, the improved cross-entropy adaptive important sampling(iCE-AIS) is introduced and a new reliability method fusing ALK model and iCE-AIS is proposed in this paper. The new method is termed as ALK-iCE-AIS. In ALK-iCE-AIS, the iCE-AIS generates important samples according to the prediction of Kriging model and the Kriging model chooses the next best training points from the importance samples of iCE-AIS. After several iterations, the Kriging model will be finely predicting the failure regions and accurate failure probability is obtained by iCE-AIS. Considering the conventional stopping criteria are too conservative, under the framework of iCE-AIS, a new stopping criterion based on failure probability error is proposed to ensure that the Kriging model can automatically stop the learning process after reaching the accuracy requirements. From the investigation of several examples, the ALK-iCE-AIS has excellent accuracy and efficiency：it can accurately estimate very small failure probability with a small number of training samples.

Key words: reliability analysis, active learning, Kriging model, cross-entropy method, adaptive importance sampling

CLC Number:

TG156

YANG Xufeng, CHENG Xin, LIU Zeqing. Reliability Analysis Method Combining Cross-entropy Adaptive Sampling and ALK Model[J]. Journal of Mechanical Engineering, 2024, 60(16): 73-82.

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