A new active learning method for system reliability analysis with multiple failure modes

Chunlong Xu, Ya Yang, Huajun Wu and Jianping Zhou

Reliability Engineering and System Safety, 2023, vol. 240, issue C

Abstract: For practical system problems, all response values of the component performance function can be obtained by running an expensive computational model once. In current adaptive Kriging-based methods for system reliability analysis, only one or all the component Kriging models are updated in each iteration. The former may waste computational resources, whereas the latter has the problem of overfitting. To improve the efficiency and stability of these problems, this study proposes a new active learning method for system reliability analysis, where a specific number of component Kriging models are refined in each iteration, rather than updating only one or all the component kriging models, as in the existing methods. First, a new learning function based on an approximate estimation of the error probability of the system is proposed. Subsequently, two strategies are proposed to stabilize the adaptive Kriging-based algorithms. Finally, five examples are used to compare the proposed approach with the other existing active Kriging-based methods. Practical validations show that the proposed method outperforms the other methods in terms of accuracy, efficiency, and stability.

Keywords: Kriging model; System reliability analysis; Active learning methods; Learning function; Stabilization strategy (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:240:y:2023:i:c:s0951832023005288

DOI: 10.1016/j.ress.2023.109614

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