北京航空航天大学学报 ›› 2014, Vol. 40 ›› Issue (9): 1312-1316.doi: 10.13700/j.bh.1001-5965.2013.0599

• 论文 • 上一篇    下一篇

成败型系统可靠性增长的Bayes评估

袁昆, 李晓钢   

  1. 北京航空航天大学 可靠性与系统工程学院, 北京 100191
  • 收稿日期:2013-10-22 出版日期:2014-09-20 发布日期:2014-10-10
  • 作者简介:袁昆(1991-),男,安徽安庆人,硕士生,yuankun@dse.buaa.edu.cn.
  • 基金资助:

    航空科学基金资助项目(2012ZD51055)

Bayesian evaluation method for binomial system reliability growth

Yuan Kun, Li Xiaogang   

  1. School of Reliability and System Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
  • Received:2013-10-22 Online:2014-09-20 Published:2014-10-10

摘要:

针对成败型系统在研制阶段的动态增长的可靠性评估问题,依据前期可靠性增长试验中多阶段的试验信息和专家经验,提出了基于新Dirichlet先验分布的可靠性增长的Bayes评估方法。该方法根据已有的不同阶段试验信息,利用离散AMSAA(Army Material System Analysis Activity)可靠性增长模型描述可靠性增长试验中不同阶段可靠性的增长趋势,并对各个阶段的可靠性进行评估;针对下一阶段建立基于新Dirichlet分布的可靠性先验分布,并根据下一阶段可靠度的估计值采用最大熵模型给出先验分布参数估计;在获得现场试验数据的条件下,给出下一阶段的可靠度后验估计,并讨论不同区间范围对后验可靠性的影响。最后通过实例分析,表明相较于直接利用离散AMSAA模型和Beta先验分布,该方法在合适的专家经验的指导下能够给出更为准确的可靠性评估。

关键词: 可靠性增长, Bayes估计, 新Dirichlet先验分布, 离散AMSAA模型, 最大熵模型

Abstract:

Concerning the multi-stages field information during the process of product development, a Bayes evaluation method based on the new Dirichlet prior distribution was proposed. This method made use of discrete army material system analysis activity (AMSAA) model to describe the dynamic change of reliability at different stage and predicted the reliability at next stage according to the multi-stages field data during the reliability growth test (GRT). Then the new Dirichlet prior distribution was introduced and the parameter estimations were obtained by maximum entropy method. Under the conditions of gaining the field test data, the Bayesian evaluation of product reliability was obtained by the proposed method. Finally, numerical example demonstrates the accuracy of the proposed method with appropriate expert experience compared to the discrete AMSAA model and Beta prior distribution.

Key words: reliability growth, Bayesian estimation, new Dirichlet prior distribution, discrete AMSAA model, maximum entropy method

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