A system active learning Kriging method for system reliability-based design optimization with a multiple response model

被引:116
作者
Xiao, Mi [1 ]
Zhang, Jinhao [1 ]
Gao, Liang [1 ]
机构
[1] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China
基金
中国国家自然科学基金;
关键词
System reliability-based design optimization (SRBDO); System failure probability; Kriging; System active learning function; Multiple response model; SMALL FAILURE PROBABILITIES; SURROGATE MODELS; INTERVAL; SIMULATION; FRAMEWORK; METAMODEL; NETWORKS; REGION;
D O I
10.1016/j.ress.2020.106935
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper proposes a system active learning Kriging (SALK) method to handle system reliability-based design optimization (SRBDO) problems, where responses of all constraints at an input can be obtained simultaneously by running a multiple response model. In SALK, to select update points around the limit-state surfaces, three new system active learning functions are respectively defined for parallel, series and combined systems. The confidence interval of estimation of system failure probability at intermediate SRBDO solutions is considered in the stopping condition of Kriging update to reduce unnecessary update points used for refining the region far from the final SRBDO solution. Based on updated Kriging models, system failure probability is estimated by Monte Carlo simulation (MCS), and its partial derivative with respect to random variables is calculated by stochastic sensitivity analysis. The efficiency of the proposed SALK method for SRBDO is validated by four examples, including a power harvester design. The results indicate that SALK can locally approximate the limit-state surfaces around the final SRBDO solution and efficiently reduce the computational cost on the refinement of the region far from the final SRBDO solution.
引用
收藏
页数:11
相关论文
共 68 条
[1]   Reliability-Based Optimal Design and Tolerancing for Multibody Systems Using Explicit Design Space Decomposition [J].
Arenbeck, Henry ;
Missoum, Samy ;
Basudhar, Anirban ;
Nikravesh, Parviz .
JOURNAL OF MECHANICAL DESIGN, 2010, 132 (02) :0210101-02101011
[2]   Adaptive explicit decision functions for probabilistic design and optimization using support vector machines [J].
Basudhar, Anirban ;
Missoum, Samy .
COMPUTERS & STRUCTURES, 2008, 86 (19-20) :1904-1917
[3]   Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions [J].
Bichon, B. J. ;
Eldred, M. S. ;
Swiler, L. P. ;
Mahadevan, S. ;
McFarland, J. M. .
AIAA JOURNAL, 2008, 46 (10) :2459-2468
[4]   Efficient surrogate models for reliability analysis of systems with multiple failure modes [J].
Bichon, Barron J. ;
McFarland, John M. ;
Mahadevan, Sankaran .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2011, 96 (10) :1386-1395
[5]   Uncertainty analyses in fault trees and Bayesian networks using FORM SORM metlnods [J].
Castillo, E ;
Sarabia, JM ;
Solares, C ;
Gómez, P .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 1999, 65 (01) :29-40
[6]   A hybrid Kriging-based reliability method for small failure probabilities [J].
Chen Weidong ;
Xu Chunlong ;
Shi Yaqin ;
Ma Jingxin ;
Lu Shengzhuo .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2019, 189 :31-41
[7]   An important boundary sampling method for reliability-based design optimization using kriging model [J].
Chen, Zhenzhong ;
Peng, Siping ;
Li, Xiaoke ;
Qiu, Haobo ;
Xiong, Huadi ;
Gao, Liang ;
Li, Peigen .
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2015, 52 (01) :55-70
[8]   An adaptive decoupling approach for reliability-based design optimization [J].
Chen, Zhenzhong ;
Qiu, Haobo ;
Gao, Liang ;
Su, Liu ;
Li, Peigen .
COMPUTERS & STRUCTURES, 2013, 117 :58-66
[9]   NARROW RELIABILITY BOUNDS FOR STRUCTURAL SYSTEMS [J].
DITLEVSEN, O .
JOURNAL OF STRUCTURAL MECHANICS, 1979, 7 (04) :453-472
[10]   Application of adaptive surrogate models in time-variant fatigue reliability assessment of welded joints with surface cracks [J].
Dong, Y. ;
Teixeira, A. P. ;
Soares, C. Guedes .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2020, 195