A Coupled Adaptive Kriging Model and Generalized Subset Simulation Hybrid Reliability Analysis Method for Rare Failure Events

• Published in: IEEE access 2024, Vol.12, p.163621-163637
• Main Authors: Ling, Yunhan, Peng, Huajun, Sun, Yong, Yuan, Chao, Su, Zining, Tian, Xiaoxiao, Nie, Peng, Yang, Hengfei, Yang, Shiyuan
• Format: Article
• Language: English
• Subjects: Accuracy; Adaptation models; Adaptive sampling; Analytical models; Failure; Generalized subset simulation; hybrid reliability analysis; Hydraulic turbines; kriging model; Learning; Limit states; Predictive models; Probability density function; Random variables; rare failure events; Reliability; Reliability analysis; Reliability engineering; Uncertainty; Wings (aircraft)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3483567

This research proposes a novel hybrid reliability analysis method for rare failure events, which integrates the coupled Adaptive Kriging model and Generalized Subset Simulation (AK-GSS). In the proposed method, the adaptive Kriging model is applied to approximate the actual Performance Function (PF) to reduce the number of PF calls. A newly updated strategy is proposed to look for samples on the limit state surface to achieve active learning of the Kriging model. This updated strategy avoids the limitations of most current learning functions based on the prediction variance of Kriging models. The advantages of AK-GSS are illustrated through five examples, including two engineering applications of aircraft wings and hydraulic turbine rotor brackets. The results show that the proposed method is more efficient and accurate for rare failure events.