Article
Engineering, Aerospace
Yingshi Hu, Zhenzhou Lu, Ning Wei, Xia Jiang, Changcong Zhou
Summary: This paper proposes a Kriging surrogate model based method to estimate the safety lifetime of Multi-mode Time-Dependent Structural System (MTDSS). The method utilizes extremum learning function, Advanced First Failure Instant Learning Function (AFFILF), and Candidate Sample Pool (CSP) reduction strategy to accurately and efficiently search for the safety lifetime.
CHINESE JOURNAL OF AERONAUTICS
(2022)
Article
Engineering, Industrial
Yingshi Hu, Zhenzhou Lu, Xia Jiang, Ning Wei, Changcong Zhou
Summary: This paper proposes an improved reliability analysis model for time-dependent structural systems, transforming the performance function into a single-mode time-dependent function and estimating failure probability using Kriging surrogate model and Monte Carlo simulation method. The theoretical analysis supports the rationality of the proposed model, and the efficiency is illustrated through numerical examples.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Computer Science, Interdisciplinary Applications
Hengchao Li, Zhenzhou Lu, Kaixuan Feng
Summary: In the Kriging model-based time-dependent reliability analysis algorithm, the existing double-loop (DLK) is inefficient and may experience unstable convergence. To address this, an importance sampling (IS)-based DLK (IS-DLK) is proposed, which improves algorithm stability and reduces the required random input size for convergent time-dependent reliability. By utilizing stable minimum estimation in the inner loop and IS variance reduction and derived convergence criterion in the outer loop, IS-DLK is more efficient than existing DLK, as demonstrated by examples.
ENGINEERING WITH COMPUTERS
(2023)
Article
Engineering, Civil
Zongrui Tian, Pengpeng Zhi, Yi Guan, Jiabin Feng, Yadong Zhao
Summary: In this paper, an effective single loop Kriging surrogate method combining stratified sampling for structural time-dependent reliability analysis is developed to improve the computational efficiency. The method divides the candidate samples into four regions and performs sequential sampling in each region, improving the efficiency of reliability analysis. A new learning function is proposed to select candidate samples that contribute much to the reliability analysis. A combined convergence criterion is also proposed to accelerate the convergence of the algorithm. The method's accuracy and effectiveness are demonstrated through four examples.
Article
Computer Science, Interdisciplinary Applications
Zhenliang Jiang, Jiawei Wu, Fu Huang, Yifan Lv, Liangqi Wan
Summary: The paper proposes an adaptive Kriging method for TRBRDO, which has shown superior computing efficiency and accuracy compared to existing methods.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Industrial
Runan Cao, Zhili Sun, Jian Wang, Fanyi Guo
Summary: This paper proposes an efficient time-dependent reliability method based on the Kriging model and the importance sampling method. The new method can obtain the failure probability varying with time and improves the accuracy of failure probability estimation for complex reliability problems.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Mechanical
Weifei Hu, Jiquan Yan, Feng Zhao, Chen Jiang, Hongwei Liu, Hyunkyoo Cho, Ikjin Lee
Summary: This article proposes a new surrogate-based time-dependent reliability analysis method for a digital twin (DT), which overcomes the challenges of conducting time-dependent reliability analysis instantly and accurately. The method dynamically selects discrete time nodes, selects multiple sensitive subdomains simultaneously for adaptive sampling, and introduces an improved weighted expected feasibility function to enhance sampling efficiency. The effectiveness of the proposed method is demonstrated through realistic DT applications.
JOURNAL OF MECHANICAL DESIGN
(2023)
Article
Computer Science, Information Systems
Shui Yu, Yun Li
Summary: This paper introduces an active learning Kriging technique to enhance the computational efficiency of time-dependent reliability analysis. By utilizing a Kriging model as a response surface to fit extreme value responses and developing an adaptive iterative algorithm for updating the model with sampling points, the method aims to improve accuracy and effectiveness in engineering problems. Multiple case studies are conducted to validate the proposed approach.
Article
Computer Science, Artificial Intelligence
Jiawei Wu, Zhenliang Jiang, Huaming Song, Liangqi Wan, Fu Huang
Summary: This study proposed a parallel efficient global optimization method integrated with the adaptive Kriging-Monte Carlo simulation for time-dependent reliability analysis (TRA) problems, which showed superior computing efficiency and high accuracy in solving high-dimensional TRA problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Huan Liu, Xindang He, Pan Wang, Zhenzhou Lu, Zhufeng Yue
Summary: Based on the idea of adaptive radial-based important sampling method, this paper proposes a new method for solving time-dependent reliability problems. This method combines ARBIS with time-dependent AK model to improve the estimation efficiency and accuracy of structural reliability analysis.
ENGINEERING WITH COMPUTERS
(2023)
Article
Engineering, Multidisciplinary
Changcong Zhou, Shiju Gao, Qi Chang, Haodong Zhao
Summary: A new time-dependent reliability analysis model with a mixture of random variables and interval variables is proposed in this paper. Samples of random variables are generated based on their probability density functions, and then substituted into the performance function to obtain interval processes. The reliability index of each interval process is computed to obtain the upper and lower bounds of failure probability. The proposed model is compared to the conventional hybrid reliability model, showing that it is more convenient to implement and accurately evaluates the reliability of the structure, providing assistance and guidance for hybrid time-dependent reliability analysis of structures.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING
(2023)
Article
Engineering, Industrial
Dapeng Wang, Haobo Qiu, Liang Gao, Chen Jiang
Summary: This paper proposes a single-loop Kriging coupled with subset simulation (SLK-co-SS) method to address the challenges in conventional TRA for high-reliability products. By introducing time-dependent intermediate failure events and an improved adaptive sampling strategy, the proposed method demonstrates good capability and applicability in four comparison examples.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Industrial
Zhao Zhao, Zhao-Hui Lu, Xuan-Yi Zhang, Yan-Gang Zhao
Summary: This paper proposes a nested single-loop Kriging model coupled with subset simulation method, named NSLK-co-SS, for time-dependent system reliability assessment. The NSLK method is developed to estimate each intermediate failure probability by reformulating the time-dependent system reliability problem as a nested system reliability one. A SYSU learning function is proposed to update the models.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Computer Science, Interdisciplinary Applications
Dapeng Wang, Haobo Qiu, Liang Gao, Danyang Xu, Chen Jiang
Summary: In this paper, a new single-loop active learning Kriging method with probability of rejecting classification is proposed for solving time-dependent system reliability analysis problems. The method makes full use of the response information of all potential failure time instants or failure modes to improve the sampling efficiency and algorithm interpretability. An effective active learning strategy is developed to identify the new training sample and the target Kriging model to be updated corresponding to a certain failure mode. The proposed method demonstrates excellent efficiency and computational accuracy in three examples.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Qiangqiang Zhao, Jinyan Duan, Tengfei Wu, Jun Hong
Summary: This paper proposes a computational method for time-dependent reliability analysis under random and interval uncertainties. The method constructs a Kriging surrogating model and utilizes expansion optimal linear estimation, sparse-grid Gaussian quadrature, and saddlepoint approximation methods to efficiently compute the time-dependent failure probability bounds.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
