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基于遗传算法的飞行管理系统余度配置优化方法

霍琳 费思邈

霍琳, 费思邈. 基于遗传算法的飞行管理系统余度配置优化方法[J]. 北京航空航天大学学报, 2017, 43(7): 1306-1312. doi: 10.13700/j.bh.1001-5965.2016.0512
引用本文: 霍琳, 费思邈. 基于遗传算法的飞行管理系统余度配置优化方法[J]. 北京航空航天大学学报, 2017, 43(7): 1306-1312. doi: 10.13700/j.bh.1001-5965.2016.0512
HUO Lin, FEI Simiao. Flight management system redundancy optimization method based on genetic algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(7): 1306-1312. doi: 10.13700/j.bh.1001-5965.2016.0512(in Chinese)
Citation: HUO Lin, FEI Simiao. Flight management system redundancy optimization method based on genetic algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(7): 1306-1312. doi: 10.13700/j.bh.1001-5965.2016.0512(in Chinese)

基于遗传算法的飞行管理系统余度配置优化方法

doi: 10.13700/j.bh.1001-5965.2016.0512
详细信息
    作者简介:

    霍琳 女, 博士, 讲师。主要研究方向:可靠性工程、故障预测与健康管理

    费思邈 男, 博士, 工程师。主要研究方向:可靠性工程、人工智能

    通讯作者:

    霍琳, E-mail:helen0404@icloud.com

  • 中图分类号: V240.2;X913.4

Flight management system redundancy optimization method based on genetic algorithm

More Information
  • 摘要:

    余度设计是飞行管理系统研制过程中提升其安全性的主要手段之一。针对余度设计受制造预算(经济性)与使用阶段维修保障资源消耗(可靠性)的制约问题,基于飞行管理系统的安全性、基本可靠性及经济性数学模型,利用改进后适用于整数优化的遗传算法,提出了一种以安全性为目标、基本可靠性与经济性为约束的余度配置优化方法,并以实例说明其适用于较为复杂系统的余度配置优化。经过敏感性分析发现,安全性指标最优值随着基本可靠性约束下限的提高而降低,随着经济性约束上限的增加而增加。2种约束条件对优化目标竞争约束,在同一时刻只有1种约束条件起主要约束作用。

     

  • 图 1  飞管系统组成

    Figure 1.  Composition of flight management system

    图 2  遗传算法流程图

    Figure 2.  Flowchart of genetic algorithm

    图 3  余度优化结果

    Figure 3.  Redundancy optimization results

    图 4  基本可靠性和经济性约束下敏感性分析

    Figure 4.  Sensitivity analysis of basic reliability and economy constraints

    表  1  飞管系统相关参数

    Table  1.   Related parameters of flight management system

    设备名称MTBF/(106h)MTBCF/(106h)单价/万元余度范围
    大气数据传感器3.03.831~4
    速率陀螺组1.22.951~4
    加速度计组2.77.621~4
    激光惯导3.38.781~4
    无线电高度表0.64.611~4
    飞管计算机0.95.7981
    模拟接口设备12.972.70.81
    数字接口设备8.956.60.91
    作动器远程控制终端1.222.83.21~4
    舵机12.312.41.21~4
    作动筒10.86.30.51~4
    舵机22.312.41.21~4
    作动筒20.86.30.51~4
    下载: 导出CSV

    表  2  最优余度配置

    Table  2.   Optimal redundancy allocation

    设备名称余度
    本文遗传算法编码改进遗传算法[8]贪婪搜索规划算法[22]
    大气数据传感器333
    速率陀螺组222
    加速度计组222
    激光惯导111
    无线电高度表222
    作动器远程控制终端211
    舵机1333
    作动筒1333
    舵机2333
    作动筒2333
    安全性最优值0.999 9360.999 9270.999 936
    时间消耗6 min3 min53 h
    下载: 导出CSV

    表  3  不同基本可靠性约束下限对应余度优化结果

    Table  3.   Equipment redundancy optimization results with different basic reliabilities lower bounds

    基本可靠性约束下限余度
    大气数据传感器速率陀螺组加速度计组激光惯导无线电高度表作动器远程控制终端舵机1作动筒1舵机2作动筒2
    0.990 02222212322
    0.996 02222212322
    0.996 12222213222
    0.996 52232212222
    0.996 62232212222
    0.996 72232212222
    0.996 82222212222
    0.997 03222112232
    0.997 32222122122
    0.997 52221112122
    0.997 73222111132
    0.997 92212111122
    0.998 02121111121
    0.998 11111111111
    下载: 导出CSV

    表  4  不同经济性约束上限对应余度优化结果

    Table  4.   Equipment redundancy optimization results with different economy upper bounds

    经济性约束上限/万元余度
    大气数据传感器速率陀螺组加速度计组激光惯导无线电高度表作动器远程控制终端舵机1作动筒1舵机2作动筒2
    1402221212222
    1503221223333
    1603322322222
    1703332433333
    1803333334444
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-06-14
  • 录用日期:  2016-06-20
  • 网络出版日期:  2017-07-20

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