Bootstrap estimation method of confidence interval for long-life product reliability based on maximum information entropy

被引:0
|
作者
He Y. [1 ]
Wang Y. [1 ]
He L. [1 ]
机构
[1] School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu
关键词
Bayes theory; maximum information entropy; parametric Bootstrap method; reliability assessment; zero-failure data;
D O I
10.12305/j.issn.1001-506X.2023.06.33
中图分类号
学科分类号
摘要
In the reliability assessment method of zero-failure data, the current method has low estimation accuracy and it is difficult to obtain the point estimation and the confidence interval estimation of the parameters at the same time and avoid inconsistent results. In the state of zero-failure data, a point estimation and confidence interval estimation of reliability method is proposed based on maximum information entropy and simulated annealing algorithm which are combined with the parametric Bootstrap method. Firstly, the order of failure probability of Weibull distribution is considered and a hyperparameter optimization model is constructed by using the value range of failure probability and maximizing the information entropy of prior distribution under Bayes theory. Secondly, the simulated annealing algorithm is used to solve the optimization model to avoid falling into the local optimal solution. Thirdly, the weighted least squares method is used to obtain the point estimation of reliability is obtained. Finally, the parametric Bootstrap method is used to re-extract new samples, and then the confidence interval estimation of reliability is obtained. It is verified that the proposed method can not only improve the accuracy of reliability point estimation and interval estimation, but also improve the credibility of evaluation results through simulation examples and actual examples of harmonic reducer with zero-failure data. © 2023 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码:1880 / 1892
页数:12
相关论文
共 37 条
  • [1] LIZ Q, WANG X, CHENY Q, Et al., Research advances and challenges of reliability assessment of complex equipment based on small sample failure data, Aero Weaponry, 28, 3, pp. 83-90, (2021)
  • [2] BOURINETJ M., Rare-event probability estimation with adaptive support vector regression surrogates, Reliability Engineering & System Safety, 150, 6, pp. 210-221, (2016)
  • [3] HE L P, HUANG H Z, DU L, Et al., A review of possibilistic approaches to reliability analysis and optimization in engineering design, Proc. of the 12th International Conference on Human Computer Interaction, pp. 1075-1084, (2007)
  • [4] YANGJ W, WANGJ H, HUANGQ, Et al., Reliability assessment for the solenoid valve of a high-speed train braking system under small sample size, Chinese Journal of Mechanical Engineering, 31, 1, (2018)
  • [5] CHENW H, HEQ C, PANJ, Et al., Reliability test technology of mechanical products-overview and prospect, China Mechanical Engineering, 31, 1, pp. 72-82, (2020)
  • [6] CHEN J D, SUN W L, LI B X., The confidence limits for reliability parameters in the case of failure data, Proc. of the Asian Conference on Statistics, pp. 17-20, (1993)
  • [7] NIUS W, ZHANW, Analysis of confidence lower limits of reliability and hazard rate for electronic stability control systems, Quality and Reliability Engineering International, 29, 5, pp. 621-629, (2013)
  • [8] XUT Q, CHENY P., Two-sided M-Bayesian credible limits of reliability parameters in the case of zero-failure data for exponential distribution, Applied Mathematical Modelling, 38, 9, pp. 2586-2600, (2014)
  • [9] LIH Y, XIEL Y, LIM, Et al., Research on a new reliability assessment method for zero-failure data, Acta Armamentarii, 39, 8, pp. 1622-1631, (2018)
  • [10] JIANGP, LIMJ H, ZUOM J, Et al., Reliability estimation in a Weibull lifetime distribution with zero-failure field data, Quality and Reliability Engineering International, 26, 7, pp. 691-701, (2010)