A single-loop time-variant reliability evaluation via a decoupling strategy and probability distribution reconstruction

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

Externe Organisationen

  • Hunan University
  • The University of Liverpool
  • Tongji University
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Details

OriginalspracheEnglisch
Aufsatznummer109031
FachzeitschriftReliability Engineering and System Safety
Jahrgang232
Frühes Online-Datum16 Dez. 2022
PublikationsstatusVeröffentlicht - Apr. 2023

Abstract

In this paper, a single-loop approach for time-variant reliability evaluation is proposed based on a decoupling strategy and probability distribution reconstruction. The most attractive feature of the proposed method is that the reliability at a specified time instant can be captured by performing time-invariant reliability analysis only once. In this method, the expansion optimal linear estimation is first employed to discretize the loading stochastic process. Then, a decoupling strategy that decouples the loading stochastic process and degradation processes is developed to formulate a single-loop method for time-variant reliability analysis, where an equivalent extreme value limit state function (EEV-LSF) is obtained. To improve the accuracy and robustness, the Box–Cox transformation is applied to get a transformed EEV-LSF. The maximum entropy method with fractional exponential moments is employed to robustly derive the probability distribution of transformed EEV-LSF. Once the probability distribution is captured, the time-variant failure probability can be readily computed. To handle a large number of random variables, a weighted sampling method is applied for moment assessment to ensure an efficient solution. Numerical examples including a complex real-world case are studied to validate the proposed method, where pertinent Monte Carlo simulations and PHI2 method are conducted for comparisons.

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A single-loop time-variant reliability evaluation via a decoupling strategy and probability distribution reconstruction. / Zhang, Yang; Xu, Jun; Beer, Michael.
in: Reliability Engineering and System Safety, Jahrgang 232, 109031, 04.2023.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

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abstract = "In this paper, a single-loop approach for time-variant reliability evaluation is proposed based on a decoupling strategy and probability distribution reconstruction. The most attractive feature of the proposed method is that the reliability at a specified time instant can be captured by performing time-invariant reliability analysis only once. In this method, the expansion optimal linear estimation is first employed to discretize the loading stochastic process. Then, a decoupling strategy that decouples the loading stochastic process and degradation processes is developed to formulate a single-loop method for time-variant reliability analysis, where an equivalent extreme value limit state function (EEV-LSF) is obtained. To improve the accuracy and robustness, the Box–Cox transformation is applied to get a transformed EEV-LSF. The maximum entropy method with fractional exponential moments is employed to robustly derive the probability distribution of transformed EEV-LSF. Once the probability distribution is captured, the time-variant failure probability can be readily computed. To handle a large number of random variables, a weighted sampling method is applied for moment assessment to ensure an efficient solution. Numerical examples including a complex real-world case are studied to validate the proposed method, where pertinent Monte Carlo simulations and PHI2 method are conducted for comparisons.",
keywords = "Box–Cox transformation, Decoupling strategy, Fractional exponential moments, Maximum entropy method, Time-variant reliability, Voronoi cells",
author = "Yang Zhang and Jun Xu and Michael Beer",
note = "Funding Information: The Natural Science Foundation of Hunan Province, China (No. 2022JJ20012 ), The Science and Technology Innovation Program of Hunan Province (No. 2022RC1176 ), National Natural Science Foundation of China (No. 51978253 , 52278178 ), and the Fundamental Research Funds for the Central Universities, China (No. 531107040024 ) are gratefully appreciated for the financial support of this research. ",
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AU - Beer, Michael

N1 - Funding Information: The Natural Science Foundation of Hunan Province, China (No. 2022JJ20012 ), The Science and Technology Innovation Program of Hunan Province (No. 2022RC1176 ), National Natural Science Foundation of China (No. 51978253 , 52278178 ), and the Fundamental Research Funds for the Central Universities, China (No. 531107040024 ) are gratefully appreciated for the financial support of this research.

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