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故障样本量确定与分配一体化设计方案

王康 史贤俊 韩旭 龙玉峰

王康, 史贤俊, 韩旭, 等 . 故障样本量确定与分配一体化设计方案[J]. 北京航空航天大学学报, 2020, 46(1): 103-114. doi: 10.13700/j.bh.1001-5965.2019.0184
引用本文: 王康, 史贤俊, 韩旭, 等 . 故障样本量确定与分配一体化设计方案[J]. 北京航空航天大学学报, 2020, 46(1): 103-114. doi: 10.13700/j.bh.1001-5965.2019.0184
WANG Kang, SHI Xianjun, HAN Xu, et al. Integrated design scheme for fault sample size determination and allocation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(1): 103-114. doi: 10.13700/j.bh.1001-5965.2019.0184(in Chinese)
Citation: WANG Kang, SHI Xianjun, HAN Xu, et al. Integrated design scheme for fault sample size determination and allocation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(1): 103-114. doi: 10.13700/j.bh.1001-5965.2019.0184(in Chinese)

故障样本量确定与分配一体化设计方案

doi: 10.13700/j.bh.1001-5965.2019.0184
详细信息
    作者简介:

    王康  男, 博士研究生。主要研究方向:装备测试性设计、验证与评估

    史贤俊  男, 博士, 教授, 博士生导师。主要研究方向:导弹自动测试技术, 装备测试性设计、验证与评估

    通讯作者:

    史贤俊,E-mail:sxjaa@sina.com

  • 中图分类号: V240.2;TJ06

Integrated design scheme for fault sample size determination and allocation

More Information
  • 摘要:

    针对当前测试性验证领域未能考虑故障样本量确定和样本分配2个环节的相互联系,以及现有样本分配方案对影响因子的选择没有统一的框架,导致确定的故障样本量和样本分配不合理的问题,提出了一种故障样本量确定与分配一体化设计方案。首先,以层次Bayes网络模型为框架,融合各节点测试性指标先验信息得到顶层测试性指标的融合分布,并建立故障样本量确定流程;其次,引入结构重要度作为样本分配影响因子,同时结合故障模式影响及危害性分析(FMECA)信息确定节点和故障模式的样本分配影响因子,提出基于节点和故障模式的二次分配框架实施样本分配;最后,通过实际案例进行对比分析。结果表明:相比其他样本分配方案,所提方案能充分考虑系统结构及其先验信息,进而实现了故障样本量确定和分配一体化方案的设计,保证了所确定的故障样本量和分配的合理性,具备更好的工程适用性。

     

  • 图 1  系统结构模型向一体化设计模型的转化示意图

    Figure 1.  Schematic diagram of transformation from system structure model to integrated design model

    图 2  二次电源系统一体化设计模型

    Figure 2.  Integrated design model of secondary power system

    图 3  节点N(1, 1)不同先验分布概率密度曲线

    Figure 3.  Probability density curves of different prior distribution at node N(1, 1)

    表  1  一体化设计模型的CPT

    Table  1.   CPT of integrated design model

    N(2, 1) 0 1
    N(2, 2) 0 1 0 1
    N(2, 3) 0 1 0 1 0 1 0 1
    N(2, 4) 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1
    N(1, 1) 0
    1
    下载: 导出CSV

    表  2  节点先验信息

    Table  2.   Prior information of each node

    节点 继承先验超参数 专家信息融合 成败型数据 自先验超参数
    a* b* 估计类型 转化形式 a b
    N(1, 1) 点估计型
    =0.921
    Beta分布
    at=11.009
    bt=0.945
    (10, 1) 20.009 1.945
    N(2, 1) 点估计型
    =0.913
    Beta分布
    at=9.910
    bt=0.944
    (20, 2) 27.910 2.944
    N(2, 2) 点估计型
    =0.927
    Beta分布
    at=12.030
    bt=0.947
    (24, 0) 36.030 0.947
    N(2, 3) 31.2 3.6 区间估计型[0.861, 0.932] 成败型数据(211, 21) (28, 3) 246.2 27.6
    N(2, 4) 点估计型
    =0.932
    Beta分布
    at=13.037
    bt=0.951
    (30, 2) 41.037 2.951
    下载: 导出CSV

    表  3  二次电源系统FMECA信息

    Table  3.   FMECA information of secondary power system

    节点 故障率λp/(10-6h-1) 故障模式M 故障模式频数比αj/% 故障模式影响概率βj 故障模式危害度Dji 故障影响(扩散度)Ij 被检测难度评分等级Ej
    标识码 模式名称
    N(2, 1) 3.042 M1N(2, 1)短路 20 1 4.259 4 2
    M2N(2, 1)退化 80 0.1 1.460 4 5
    N(2, 2) 0.139 M1N(2, 2)开路 92 1 0.384 3 2
    M2N(2, 2)参漂 8 0.5 0.017 3 4
    N(2, 3) 10.516 M1N(2, 3)退化 35 0.1 2.576 5 5
    M2N(2, 3)低温不启动 10 0.1 0.736 5 1
    M3N(2, 3)漏气 15 0.1 0.946 5 3
    M4N(2, 3)开路 15 1 11.042 5 2
    M5N(2, 3) 短路 25 1 18.403 5 1
    N(2, 4) 0.366 M1N(2, 4)接触不良 80 0.8 1.640 1 2
    M2N(2, 4)开路 20 1 0.512 1 2
    注:①指故障模式危害度, 通过文献[13]求解;②指本文以扩散度表征故障影响,可通过文献[27]求解。
    下载: 导出CSV

    表  4  不同样本分配方式结果对比

    Table  4.   Result comparison of different sample allocation methods

    节点 故障模式M 基于故障率的样本分配[12] 基于危害度的样本分配[13] 基于故障属性的样本分配[27] 基于多因子的样本分配[15] 一体化设计方案
    节点 故障模式 节点 故障模式 节点 故障模式 节点 故障模式 节点 故障模式
    N(2, 1) M1N(2, 1) 10 2 6 4 8 4 1(2) 0(1) 6 3
    M2N(2, 1) 8 2 4 1 3
    N(2, 2) M1N(2, 2) 1(2) 1 0(2) 0(1) 1(2) 1 0(2) 0(1) 0(2) 0(1)
    M2N(2, 2) 0(1) 0(1) 0(1) 0(1) 0(1)
    N(2, 3) M1N(2, 3) 12 3 8 16 9
    M2N(2, 3) 4 1 1 5 1
    M3N(2, 3) 35 5 38 1 37 2 46 7 39 2
    M4N(2, 3) 5 12 14 7 15
    M5N(2, 3) 9 21 12 11 12
    N(2, 4) M1N(2, 4) 1(2) 1 3 2 1(2) 1 0(2) 0(1) 2(3) 2
    M2N(2, 4) 0(1) 1 0(1) 0(1) 0(1)
    注:(·)内数字为基于式(29)校正后的样本量。
    下载: 导出CSV
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  • 收稿日期:  2019-04-23
  • 录用日期:  2019-07-19
  • 刊出日期:  2020-01-20

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