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基于随机相关的电子部件二元加速退化可靠性评估

盖炳良 滕克难 王浩伟 王文双 陈健 宦婧

盖炳良, 滕克难, 王浩伟, 等 . 基于随机相关的电子部件二元加速退化可靠性评估[J]. 北京航空航天大学学报, 2019, 45(11): 2237-2246. doi: 10.13700/j.bh.1001-5965.2019.0130
引用本文: 盖炳良, 滕克难, 王浩伟, 等 . 基于随机相关的电子部件二元加速退化可靠性评估[J]. 北京航空航天大学学报, 2019, 45(11): 2237-2246. doi: 10.13700/j.bh.1001-5965.2019.0130
GAI Bingliang, TENG Kenan, WANG Haowei, et al. Reliability assessment for electronic components with bivariate accelerated degradation based on random correlation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2237-2246. doi: 10.13700/j.bh.1001-5965.2019.0130(in Chinese)
Citation: GAI Bingliang, TENG Kenan, WANG Haowei, et al. Reliability assessment for electronic components with bivariate accelerated degradation based on random correlation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2237-2246. doi: 10.13700/j.bh.1001-5965.2019.0130(in Chinese)

基于随机相关的电子部件二元加速退化可靠性评估

doi: 10.13700/j.bh.1001-5965.2019.0130
基金项目: 

国家自然科学基金 51605487

详细信息
    作者简介:

    盖炳良  男, 博士研究生。主要研究方向:装备可靠性评估、加速试验技术等

    滕克难  男, 博士, 教授, 博士生导师。主要研究方向:装备延寿理论与技术

    通讯作者:

    滕克难.E-mail:Tengkn@sina.com

  • 中图分类号: TB114.3

Reliability assessment for electronic components with bivariate accelerated degradation based on random correlation

Funds: 

National Natural Science Foundation of China 51605487

More Information
  • 摘要:

    针对加速应力下电子部件二元相关退化可靠性分析难题,提出一种基于随机相关的可靠性分析方法。采用考虑个体差异的Wiener过程模型建立边缘退化过程模型,并基于加速因子不变原则建立了模型参数与加速应力的关系;构建了基于Copula函数的随机相关模型,采用两阶段贝叶斯参数估计方法进行参数估计,综合运用散点图、偏差信息准则(DIC)值以及Kendall τ的非参数估计值等方法进行随机相关模型选择,并采用蒙特卡罗仿真方法进行可靠度计算。最后采用实例验证了所提方法有效性,为考虑个体差异的贮存可靠性评估提供了技术支撑。

     

  • 图 1  二元相关退化建模框架

    Figure 1.  Framework of bivariate correlation degradation modeling

    图 2  X1X2的随机参数箱线图

    Figure 2.  Boxplots of random parameters of X1 and X2

    图 3  3个加速应力下的边缘退化增量累积分布函数取值的散点图

    Figure 3.  Scatter plots of CDFs of degradation increments under three accelerated stresses

    图 4  边缘退化增量累积分布函数取值的散点图

    Figure 4.  Scatter plot of CDFs of degradation increments

    图 5  随机相关性模型参数的箱线图

    Figure 5.  Boxplots of parameters of random correlation model

    图 6  可靠度曲线

    Figure 6.  Reliability curves

    表  1  Copula函数

    Table  1.   Copula function

    Copula函数 分布函数C(u, v; θ) θ τ
    Frank
    Gaussian
    Gumbel
    Clayton
    下载: 导出CSV

    表  2  边缘分布参数估计值

    Table  2.   Parameter estimations of marginal distribution

    寿命表征参数 参数 均值 置信区间(置信水平为0.95) 先验
    X1 RDV(1) 1.338 [0.111 9,2.778] U(0, 100)
    RDV(2) 906.2 [689.1,997.3] U(0, 1 000)
    RDV(3) 0.530 3 [0.016 53,1.678] U(0, 100)
    RDV(4) 0.444 5 [0.018 11,1.206] U(0, 100)
    0.259 6 [0.158 9,0.359 6] U(0, 10)
    4.448 [1.288,9.462] U(0, 10)
    X2 RDV(1) 2.901 [1.23,4.303] U(0, 100)
    RDV(2) 823.4 [465.8,994.4] U(0, 1 000)
    RDV(3) 0.651 5 [0.021 36, 1.983] U(0, 100)
    RDV(4) 1.058 [0.084 9, 2.11] U(0, 100)
    0.217 [0.126 9, 0.309 5] U(0, 10)
    6.027 [1.729, 9.793] U(0, 10)
    下载: 导出CSV

    表  3  Copula函数参数估计值

    Table  3.   Parameter estimations of Copula function

    模型 参数 均值 先验 DIC值 τ
    Gaussian模型A θ 0.158 1 U(-1, 1) 179 0.101 1
    Frank模型A θ 2.897 U(0, 100) -14.07 0.298 1
    Gumbel模型A θ 1.302 U(1, 100) -11.46 0.232 0
    Clayton模型A θ 0.558 U(0, 100) -12.18 0.218 2
    下载: 导出CSV

    表  4  随机相关模型参数估计值

    Table  4.   Parameter estimations of random correlation models

    模型 参数 均值 置信区间(置信水平为0.95) 先验 DIC值
    A θ 2.897 [1.493,4.288] (0, 100) -14.07
    B γB(1) 1.433 [0.544 7,2.195] (0, 100) -13.64
    γB(2) 190.4 [8.855,388.4] (0, 400)
    C aθ 3.677 [1.615,6.332] (0, 100) -16.45
    bθ 2.357 [0.228 2,5.605] (0, 100)
    D γD(1) 14.2 [0.904 6,43.15] (0, 100)-13.61
    γD(2) 9 280 [154.6,19 590] (0, 20 000)
    bθ 4.29 [0.727 5,9.157] (0, 100)
    E γE(1) 2.706 [0.159 9, 5.845] (0, 100) -15.18
    γE(2) 1 139 [90.83,1 963] (0, 2 000)
    aθ 3.416 [1.628,5.748] (0, 100)
    F γF(1) 3.161 [0.905,6.113] (0, 100) -13.66
    γF(2) 813.9 [29.4,1 909] (0, 2 000)
    γF(3) 2.193 [0.138 2,4.711] (0, 100)
    γF(4) 830 [58.14,1 471] (0, 1 500)
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-03-27
  • 录用日期:  2019-05-28
  • 网络出版日期:  2019-11-20

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