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A Kriging-Based Active Learning Algorithm for Mechanical Reliability Analysis with Time-Consuming and Nonlinear Response
Tong C(佟操)1,2; Wang, Jian3; Liu JG(刘金国)2
Department空间自动化技术研究室
Corresponding AuthorTong, Cao(tongcao19@163.com)
Source PublicationMathematical Problems in Engineering
ISSN1024-123X
2019
Volume2019Pages:1-14
Indexed BySCI ; EI
EI Accession number20193707428635
WOS IDWOS:000484746800001
Contribution Rank1
Funding OrganizationYoung Doctor Scientific Research Foundation of College (Grant no.19YB27) ; State Key Laboratory of Robotics (Grant no. 2017-Z18) ; Liaoning Provincial Natural Science Foundation (Grant no. 2018010334-301)
AbstractWhen the reliability analysis of the mechanical products with high nonlinearity and time-consuming response is carried out, there will be the problems of low precision and huge computation using the traditional reliability methods. To solve these issues, the active learning reliability methods have been paid much attention in recent years. It is the key to choose an efficient learning function (such as U, EFF, and ERF). The aim of this study is to further decrease the computation and improve the accuracy of the reliability analysis. Inspired from these learning functions, a new point-selected learning function (called HPF) is proposed to update DOE, and a new point is sequentially added step by step to the DOE. The proposed learning function can consider the features like the sampling density, the probability to be wrongly predicted, and the local and global uncertainty close to the limit state. Based on the stochastic property of the Kriging model, the analytic expression of HPF is deduced by averaging a hybrid indicator throughout the real space. The efficiency of the proposed method is validated by two explicit examples. Finally, the proposed method is applied to the mechanical reliability analysis (involving time-consuming and nonlinear response). By comparing with traditional mechanical reliability methods, the results show that the proposed method can solve the problems of large computation and low precision.
Language英语
WOS SubjectEngineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications
WOS KeywordSURROGATE MODELS ; ACCURACY ; EFFICIENCY ; REGIONS
WOS Research AreaEngineering ; Mathematics
Funding ProjectYoung Doctor Scientific Research Foundation of College[19YB27] ; State Key Laboratory of Robotics[2017-Z18] ; Liaoning Provincial Natural Science Foundation[2018010334-301]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25624
Collection空间自动化技术研究室
Corresponding AuthorTong C(佟操)
Affiliation1.School of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Recommended Citation
GB/T 7714
Tong C,Wang, Jian,Liu JG. A Kriging-Based Active Learning Algorithm for Mechanical Reliability Analysis with Time-Consuming and Nonlinear Response[J]. Mathematical Problems in Engineering,2019,2019:1-14.
APA Tong C,Wang, Jian,&Liu JG.(2019).A Kriging-Based Active Learning Algorithm for Mechanical Reliability Analysis with Time-Consuming and Nonlinear Response.Mathematical Problems in Engineering,2019,1-14.
MLA Tong C,et al."A Kriging-Based Active Learning Algorithm for Mechanical Reliability Analysis with Time-Consuming and Nonlinear Response".Mathematical Problems in Engineering 2019(2019):1-14.
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