Global non-probabilistic reliability sensitivity analysis based on surrogate model

• Autorzy: Liu Hui , Xiao Ning-Cong

Identyfikatory

DOI

10.17531/ein.2022.4.2

• Języki publikacji: EN

Abstrakty

EN

Sensitivity analysis is used to find the key variables which have significant effect on system reliability. For a product in early design stage, it is impossible to collect sufficient samples. Thus, the probabilistic-based reliability sensitivity analysis methods are difficult to use due to the requirement of probability distribution. As an alternative, interval can be used because it only requires few samples. In this study, an effective global non-probabilistic sensitivity analysis based on adaptive Kriging model is proposed. The global accuracy Kriging model is constructed to reduce overall computational cost. Subsequently, the global non-probabilistic sensitivity analysis method is developed. Compared to existing non-probabilistic sensitivity analysis methods, the proposed method is a global non-probabilistic reliability sensitivity analysis method. The proposed method is easy to use and does not require probability distribution of the input variables. The applicability of proposed method is demonstrated via two examples.

Słowa kluczowe

EN

structural reliability; non-probabilistic reliability sensitivity; adaptive Kriging; surrogates; models; interval variables

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2022
• Tom: Vol. 24, no. 4
• Strony: 612--616
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Liu Hui

• Chengdu University of Traditional Chinese Medicine, School of intelligent medicine, No. 1166, Liutai Avenue, Wenjiang District, Chengdu 611137, China

autor

Xiao Ning-Cong

• University of Electronic Science and Technology of China, School of Mechanical and Electrical Engineering, No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, China

• https://orcid.org/0000-0003-0493-1251

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).