A system reliability analysis method combining active learning Kriging model with adaptive size of candidate points-Reference-Cited by-同舟云学术

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A system reliability analysis method combining active learning Kriging model with adaptive size of candidate points

Published:2019-02-01 Issue:1 Volume:60 Page:137-150
ISSN:1615-147X
Container-title:Structural and Multidisciplinary Optimization
language:en
Short-container-title:Struct Multidisc Optim

Author:

Yang Xufeng,Mi Caiying,Deng Dingyuan,Liu Yongshou

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

The State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration

Publisher

Springer Science and Business Media LLC

Subject

Control and Optimization,Computer Graphics and Computer-Aided Design,Computer Science Applications,Control and Systems Engineering,Software

Link

http://link.springer.com/article/10.1007/s00158-019-02205-x/fulltext.html

Reference44 articles.

1. Bichon BJ, Eldred MS, Swiler LP, Mahadevan S, McFarland JM (2008) Efficient global reliability analysis for nonlinear implicit performance functions. AIAA J 46:2459–2468

2. Bichon BJ, McFarland JM, Mahadevan S (2011) Efficient surrogate models for reliability analysis of systems with multiple failure modes. Reliab Eng Syst Saf 96:1386–1395

3. Bourinet J-M, Deheeger F, Lemaire M (2011) Assessing small failure probabilities by combined subset simulation and support vector machines. Struct Saf 33:343–353

4. Cadini F, Santos F, Zio E (2014) An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability. Reliab Eng Syst Saf 131:109–117

5. Castillo E, Sarabia JM, Solares C, Gómez P (1999) Uncertainty analyses in fault trees and Bayesian networks using FORM/SORM methods. Reliab Eng Syst Saf 65:29–40

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