Reliability calculation method for mechanical structures with generalized triangular fuzzy number

被引：0
|
作者：
Tang Z. [1 ]
Li W. [1 ]
Li Y. [1 ]
机构：
[1] School of Manufacturing Science & Engineering, Sichuan University, Chengdu
来源：
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2018年 / 38卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Evidence theory; Generalized density method; Generalized triangular fuzzy number; Improved entropy equivalent method; Reliability; Uncertainty;
D O I：
10.12011/1000-6788(2018)08-2155-13
中图分类号：
学科分类号：
摘要：
Aiming at the problem that the uncertainty of generalized triangular fuzzy numbers (GTFN) exists in the mechanical structures for the reliability measurement, a discretization reliability calculation method based on evidence theory is proposed. Firstly, to properly construct the basic probability assignment(BPA) for uncertain variables, based on the discrete property of the BPA of the evidence variables,the deficiency of entropy equivalent method (EEM) is improved when the generalized triangular fuzzy number is defuzzified. On the basis of the improved entropy equivalent method (IEEM), a more simple defuzzification method, generalized density method (GDM), is proposed. Secondly, the evidence structure characterization of the random variables and GTFN are realized by the discretization method, the discrete continuous focal element sequences (or subintervals) are used as their evidence bodies, and then the BPA is constructed. Finally, the fusion rule of evidence theory is used to fuse the evidence bodies, in order to achieve the numerical calculation of the belief and plausibility. Combined with the Monte Carlo simulation(MCS) method, the feasibility of proposed approach is verified by taking the reliability calculation of crank-slider mechanism for example. © 2018, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.
引用
收藏
页码：2155 / 2167
页数：12
相关论文
共 32 条
  • [1] Yang X.F., Liu Y.S., Ma P.K., Structural reliability analysis under evidence theory using the active learning Kriging model, Engineering Optimization, 49, 11, pp. 1922-1938, (2017)
  • [2] Hoffman F.O., Hammonds J.S., Propagation of uncertainty in risk assessments: The need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability, Risk Analysis, 14, 5, pp. 707-712, (1994)
  • [3] Bae H.R., Grandhi R.V., Canfield R.A., Epistemic uncertainty quantification techniques including evidence theory for large-scale structures, Computers & Structures, 82, 13-14, pp. 1101-1112, (2004)
  • [4] Helton J.C., Johnson J.D., Oberkampfw L., Et al., A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory, Computer Methods in Applied Mechanics and Engineering, 196, 37-40, pp. 3980-3998, (2007)
  • [5] Shafer G., A mathematical theory of evidence, (1976)
  • [6] Li X.H., Wang F.Q., Chen X.H., Intuitionistic trapezoidal fuzzy IOWA operator based on Dempster-Shafer theory and its application, Systems Engineering - Theory & Practice, 36, 11, pp. 2915-2923, (2016)
  • [7] Guo H.X., Liu D.S., Hu G.Y., Et al., Method of reliability design optimization using evidence theory and interval analysis, Chinese Journal of Mechanical Engineering, 44, 12, pp. 35-41, (2008)
  • [8] Zhang Z., Jiang C., Han X., Et al., A response surface approach for structural reliability analysis using evidence theory, Advances in Engineering Software, 69, pp. 37-45, (2014)
  • [9] Mourelatos Z.P., Zhou J., A design optimization method using evidence theory, Journal of Mechanical Design, 128, 4, pp. 901-908, (2006)
  • [10] Li L.L., Wu M., Li Z.G., Uncertain information processing method in the reliability measurement, Journal of Mechanical Engineering, 48, 8, pp. 153-158, (2012)
1234