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模糊贝叶斯网络在启动系统可靠性中的应用

钱存华 张洋洋

钱存华,张洋洋. 模糊贝叶斯网络在启动系统可靠性中的应用[J]. 机械科学与技术,2023,42(5):814-820 doi: 10.13433/j.cnki.1003-8728.20220027
引用本文: 钱存华,张洋洋. 模糊贝叶斯网络在启动系统可靠性中的应用[J]. 机械科学与技术,2023,42(5):814-820 doi: 10.13433/j.cnki.1003-8728.20220027
QIAN Cunhua, ZHANG Yangyang. Application of Fuzzy Bayesian Network in Reliability Assessment of Turbofan Engine Startup System[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(5): 814-820. doi: 10.13433/j.cnki.1003-8728.20220027
Citation: QIAN Cunhua, ZHANG Yangyang. Application of Fuzzy Bayesian Network in Reliability Assessment of Turbofan Engine Startup System[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(5): 814-820. doi: 10.13433/j.cnki.1003-8728.20220027

模糊贝叶斯网络在启动系统可靠性中的应用

doi: 10.13433/j.cnki.1003-8728.20220027
基金项目: 国家自然科学基金项目(71371097)
详细信息
    作者简介:

    钱存华(1964−),教授,博士生导师,博士,研究方向为系统可靠性、系统优化和决策理论及应用等,qch64317@njtech.edu.cn

  • 中图分类号: V23

Application of Fuzzy Bayesian Network in Reliability Assessment of Turbofan Engine Startup System

  • 摘要: 飞机涡轮风扇发动机的组成结构极其精密复杂,且启动系统的健康状态对发动机的影响尤为重大,因此对发动机启动系统的可靠性评估不管是对航空工程的安全性和稳定性,都有着举足轻重的作用。为提高系统可靠性分析的准确性,将原始故障数据进行梯形模糊处理,并结合专家经验进行两种重要度分析和后验概率计算,完成对涡扇发动机启动系统可靠性评估的建模。通过实例验证,对某型航空涡扇发动机的启动系统进行可靠性评估,计算系统中各组件的不同状态对启动系统产生的影响及其重要程度,找出系统易出故障的薄弱环节,为提高整个发动机系统的安全性和可靠性寻找思路与方法。
  • 图  1  BN模型

    图  2  T-S模糊故障树模型

    图  3  梯形模糊隶属函数

    图  4  启动系统故障诊断原理

    图  5  启动系统的T-S模糊故障树

    图  6  启动系统的贝叶斯网络

    表  1  BN各节点及名称

    节点名称节点名称
    x1 电机轴承故障 x8 转换器失灵
    x2 电机线路烧蚀 x9 转换器烧蚀
    x3 电机线路断路 y1 启动电机故障
    x4 调压器烧蚀 y2 调压器故障
    x5 调压器失灵 y3 电源转换器故障
    x6 调压器线路断路 T 启动系统故障
    x7 电源电量故障
    下载: 导出CSV

    表  2  根节点故障概率及模糊子集

    根节点xj故障率梯形模糊数
    x10.004762(0.000333,0.000429,0.000524,0.000619)
    x20.232558(0.162791,0.209302,0.255814,0.302325)
    x30.125000(0.087500,0.112500,0.137500,0.162500)
    x40.004500(0.003150,0.004050,0.004950,0.005850)
    x50.006135(0.004295,0.005522,0.006749,0.007976)
    x60.008321(0.005825,0.007489,0.009153,0.010817)
    x70.005545(0.003882,0.004991,0.006100,0.007209)
    x80.001523(0.001066,0.0013719,0.001675,0.001980)
    x90.027734(0.019414,0.024961,0.030507,0.036054)
    下载: 导出CSV

    表  3  中间节点${y_1}$的条件概率表

    编号x1x2x3y1
    00.51
    1000100
    2000.50.40.40.2
    3001001
    400.500.40.50.1
    500.50.50.20.20.6
    600.51001
    7010001
    8010.5001
    9011001
    100.5000.40.40.2
    110.500.50.20.40.4
    120.501001
    130.50.500.10.30.6
    140.50.50.50.10.10.8
    150.50.51001
    16100001
    17111001
    下载: 导出CSV

    表  4  根节点状态重要度

    根节点状态重要度
    $I_{0.5}^{De}({x_j})$$I_1^{De}({x_j})$
    x100.035723
    x20.0206300
    x300
    x400.166587
    x500.030000
    x600
    x700
    x800
    x900
    下载: 导出CSV

    表  5  根节点模糊重要度

    根节点模糊重要度
    $I_{0.5}^{Fu}({x_j})$$I_1^{Fu}({x_j})$
    x10.0168450.134435
    x20.0353700.155440
    x30.0216350.150990
    x40.0206550.142245
    x50.0284450.116225
    x60.0274600.138510
    x70.0313000.123485
    x80.0357700.126825
    x90.0272150.130855
    下载: 导出CSV

    表  6  根节点后验概率

    根节点后验概率
    $P({x_j} = 1|T = 1)$
    根节点后验概率
    $P({x_j} = 1|T = 1)$
    x1 0.00049 x6 0.00858
    x2 0.23138 x7 0.00572
    x3 0.12836 x8 0.00157
    x4 0.00400 x9 0.02857
    x5 0.00632
    下载: 导出CSV
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  • 收稿日期:  2021-05-12
  • 刊出日期:  2023-05-25

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