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• W3168889555 abstract "Collecting enough samples is difficult in real applications. Several interval-based non-probabilistic reliability methods have been reported. The key of these methods is to estimate system non-probabilistic reliability index. In this paper, a new method is proposed to calculate system non-probabilistic reliability index. Kriging model is used to replace time-consuming simulations, and the efficient global optimization is used to determine the new training samples. A refinement learning function is proposed to determine the best component (or performance function) during the iterative process. The proposed refinement learning function has considered two important factors: (1) the contributions of components to system nonprobabilistic reliability index, and (2) the accuracy of the Kriging model at current iteration. Two stopping criteria are given to terminate the algorithm. The system non-probabilistic index is finally calculated based on the Kriging model and Monte Carlo simulation. Two numerical examples are given to show the applicability of the proposed method." @default.
• W3168889555 created "2021-06-22" @default.
• W3168889555 creator A5028727957 @default.
• W3168889555 creator A5044301848 @default.
• W3168889555 date "2021-09-30" @default.
• W3168889555 modified "2023-10-16" @default.
• W3168889555 title "An efficient method for calculating system non-probabilistic reliability index" @default.
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• W3168889555 doi "https://doi.org/10.17531/ein.2021.3.10" @default.
• W3168889555 hasPublicationYear "2021" @default.
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