TY - JOUR

T1 - A single-loop time-variant reliability evaluation via a decoupling strategy and probability distribution reconstruction

AU - Zhang, Yang

AU - Xu, Jun

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.

PY - 2023/4

Y1 - 2023/4

N2 - 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.

AB - 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.

KW - Box–Cox transformation

KW - Decoupling strategy

KW - Fractional exponential moments

KW - Maximum entropy method

KW - Time-variant reliability

KW - Voronoi cells

UR - http://www.scopus.com/inward/record.url?scp=85145661206&partnerID=8YFLogxK

U2 - 10.1016/j.ress.2022.109031

DO - 10.1016/j.ress.2022.109031

M3 - Article

AN - SCOPUS:85145661206

VL - 232

JO - Reliability Engineering and System Safety

JF - Reliability Engineering and System Safety

SN - 0951-8320

M1 - 109031

ER -