期刊
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
卷 60, 期 1, 页码 137-150出版社
SPRINGER
DOI: 10.1007/s00158-019-02205-x
关键词
System reliability analysis; Multiple failure modes; Active learning; Kriging model; Adaptive size of candidate points
资金
- National Natural Science Foundation of China [51705433, 51475386]
- Fundamental Research Funds for the Central Universities [2682017CX028]
- Open Project Program of The State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration [2017ZJKF04, 2017ZJKF02]
- China Scholarship Council
This paper investigates the improvement of system reliability analysis (SRA) methods which combine active learning Kriging (ALK) model with Monte Carlo simulation. In this kind of methods, a number of Monte Carlo samples are treated as the candidate points of the ALK models, and the size (or the number) of candidate points vitally affects the efficiency. However, the existing strategies fail to build the Kriging model with the optimal size of candidate points. Therefore, a certain quantity of training points was wasted. To circumvent this drawback, a strategy with an adaptive size of candidate points (ASCP) is exploited and seamlessly integrated into one of the recently proposed ALK model-based SRA method. In this strategy, the optimal size is iteratively predicted and updated according to the predicted information of component Kriging models. After several iterations, the optimal size can be approximately obtained, and the learning process can be executed with an optimal size of candidate points hereafter. Three numerical examples are investigated to demonstrate the efficiency and accuracy of the proposed method.
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