Hybrid reliability analysis method for systems with random and non-parameterized p-boxes based on weight coefficients

• Autorzy: Ye Yan , Xiong Cenbo , Ma Biao , Zhou Qinghua , Yu Liang

Identyfikatory

DOI

10.17531/ein/191458

• Języki publikacji: EN

Abstrakty

EN

This paper establishes a hybrid variable system failure probability optimization model based on sampling methods and weighting coefficients. By introducing auxiliary input variables, important sampling functions, and p-box, failure samples are mapped from the random variable space to the p-box variable space. The new weight coefficients are constructed, including important sampling weights and interval weights. Combining discretization methods and Monte Carlo simulation (MCS), the interval weights are transformed into variables, and constraints conforming to the p-box variable distribution are constructed. After calculating the weighting coefficients for all failure samples, the new failure probability optimization model is built. This model is independent of the performance functions and does not involve cyclic optimization, with computational complexity only related to the dimensions. Six cases are used for method comparison, validating that the new method exhibits higher efficiency and accuracy.

Słowa kluczowe

EN

hybrid reliability analysis; Non-Parameterized p-boxes; weight coefficients; optimizing equation

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2024
• Tom: Vol. 26, no. 4
• Strony: art. no. 191458
• Opis fizyczny: Bibliogr. 49 poz., rys., tab., wykr.

Twórcy

autor

Ye Yan

• School of Mechanical Engineering, Beijing Institute of Technology, China

• https://orcid.org/0009-0003-2228-7786

autor

Xiong Cenbo

• School of Mechanical Engineering, Beijing Institute of Technology, China

autor

Ma Biao

• School of Mechanical Engineering, Beijing Institute of Technology, China

autor

Zhou Qinghua

• School of Aeronautics and Astronautics, Sichuan University, China

autor

Yu Liang

• School of Mechanical Engineering, Beijing Institute of Technology, China

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