Adaptive Approximation of the First-crossing PDF for Time-variant Reliability Analysis

被引:0
作者
Yu S. [1 ,2 ]
Wu X. [1 ,2 ]
Guo P. [1 ,2 ]
Wang Z. [3 ]
机构
[1] School of Mechanical Engineering, Southwest Jiaotong University, Chengdu
[2] Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province, Chengdu
[3] Chengdu Aircraft Industrial (Group) Co. Ltd, Chengdu
来源
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2024年 / 60卷 / 05期
关键词
adaptive surrogate model; first-crossing time point; kernel density function; time-variant reliability; whole life cycle;
D O I
10.3901/JME.2024.05.264
中图分类号
学科分类号
摘要
An adaptive probability distribution model of first-crossing time point is proposed for the time-varying reliability of mechanical products over their whole life cycle, which can obtain the evolution of reliability during the life cycle and provide a tool for reliability analysis and design of mechanical products over their whole life cycle. To address the difficulty of estimating the first crossing rate model in the traditional first-crossing time-variant reliability method. Firstly, an adaptive surrogate model is proposed for the first-crossing time point based on support vector regression. Secondly, Latinized partially stratified sampling (LPSS) is employed to estimate the fourth origin moments of first-crossing time point surrogate model. The adaptive learning function is constructed by combining the uniform design with the nearest point to the first-order moment of the surrogate model as the center. Then, the maximum error of each order moment of two adjacent iterations is used as the convergence condition to build the optimal surrogate model for the first-crossing time point. Finally, based on the optimal surrogate model, the probability distribution function of the first-crossing time point is solved using the kernel density function to obtain the time-variant reliability trend during the product life cycle. The effectiveness of the proposed method is verified by three examples. © 2024 Chinese Mechanical Engineering Society. All rights reserved.
引用
收藏
页码:264 / 275
页数:11
相关论文
共 31 条
[1]  
ZHANG Yimin, Connotation and development of mechanical reliability-based design, Journal of mechanical engineering, 46, 14, pp. 167-180, (2010)
[2]  
JIANG Chen, QIU Haobo, GAO Liang, Research progresses in reliability-based design optimization under aleatory uncertainties, China Mechanical Engineering, 31, 2, pp. 190-205, (2020)
[3]  
ANDRIEU-RENAUD C, SUDRET B, LEMAIRE M., The PHI2 method:a way to compute time-variant reliability, Reliability Engineering & System Safety, 84, 1, pp. 75-86, (2004)
[4]  
YU Shui, ZHANG Yanwei, LI Yun, Et al., Time-variant reliability analysis via approximation of the first-crossing PDF[J], Structural and Multidisciplinary Optimization, 62, 5, pp. 2653-2667, (2020)
[5]  
MENG Zeng, ZHAO Jingyu, JIANG Chen, An efficient semi-analytical extreme value method for time-variant reliability analysis[J], Structural and Multidisciplinary Optimization, 64, 3, pp. 1469-1480, (2021)
[6]  
WANG Jian, CAO Runan, Zhili SUN, Importance sampling for time-variant reliability analysis[J], IEEE Access, 9, pp. 20933-20941, (2021)
[7]  
YUAN Xiukai, LIU Shaolong, FAES M, Et al., An efficient importance sampling approach for reliability analysis of time-variant structures subject to time-dependent stochastic load[J], Mechanical Systems and Signal Processing, 159, (2021)
[8]  
JIANG Chen, WANG Dapeng, QIU Haobo, Et al., An active failure-pursuing Kriging modeling method for time-dependent reliability analysis[J], Mechanical Systems and Signal Processing, 129, pp. 112-129, (2019)
[9]  
SHI Yan, Zhenzhou LU, XU Liyang, Et al., An adaptive multiple-Kriging-surrogate method for time-dependent reliability analysis[J], Applied Mathematical Modelling, 70, pp. 545-571, (2019)
[10]  
RICE S O., Mathematical analysis of random noise[J], The Bell System Technical Journal, 24, 1, pp. 146-156, (1945)