System reliability analysis under uncertain information

被引：0
|
作者：
Ding J. [1 ,2 ]
Yuan Q. [1 ,2 ]
Ren D. [1 ,2 ]
Jia L. [1 ,2 ]
You J. [1 ,2 ]
机构：
[1] Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming
[2] Artificial Intelligence Key Laboratory of Yunnan Province, Kunming
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2019年 / 40卷 / 04期
关键词：
Evidence theory; Monte-Carlo; Probability-box; System stability; Uncertainty;
D O I：
10.19650/j.cnki.cjsi.J1804508
中图分类号：
学科分类号：
摘要：
In system stability analysis, a correct expression of the uncertain parameters is a prerequisite for stability evaluation. However, the parameter distribution that affects system reliability often lacks strict regularity in engineering. Even the parameters generally obey a certain distribution, they always drift. Information-loss is another concern when traditional methods are used to deal with such uncertainties. Therefore, a new method to conduct system reliability analysis under uncertain information is proposed by introducing probability-box theory. Firstly, the probability-box is used to model uncertain parameters. Secondly, the probability-box model of system reliability is obtained by discretizing each parameter into equally confidence levels and calculating Cartesian product with the system reliability equation. Finally, the risk zone and the stable zone are divided with zero as boundary, and the system reliability is quantitatively analyzed by integral calculating the area of probability-box. The cantilever beam system is analyzed in the experiments. Experimental results demonstrate that the proposed method is effective, and can also improve the accuracy compared with other related approaches. © 2019, Science Press. All right reserved.
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页码：153 / 162
页数：9
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