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航空发动机传感器与执行机构信息重构算法

孙浩 郭迎清 赵万里

孙浩, 郭迎清, 赵万里等 . 航空发动机传感器与执行机构信息重构算法[J]. 北京航空航天大学学报, 2020, 46(2): 331-339. doi: 10.13700/j.bh.1001-5965.2019.0240
引用本文: 孙浩, 郭迎清, 赵万里等 . 航空发动机传感器与执行机构信息重构算法[J]. 北京航空航天大学学报, 2020, 46(2): 331-339. doi: 10.13700/j.bh.1001-5965.2019.0240
SUN Hao, GUO Yingqing, ZHAO Wanliet al. Information reconstruction algorithm of aero-engine sensors and actuators[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(2): 331-339. doi: 10.13700/j.bh.1001-5965.2019.0240(in Chinese)
Citation: SUN Hao, GUO Yingqing, ZHAO Wanliet al. Information reconstruction algorithm of aero-engine sensors and actuators[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(2): 331-339. doi: 10.13700/j.bh.1001-5965.2019.0240(in Chinese)

航空发动机传感器与执行机构信息重构算法

doi: 10.13700/j.bh.1001-5965.2019.0240
详细信息
    作者简介:

    孙浩   男, 博士研究生。主要研究方向:航空发动机故障诊断与容错控制

    郭迎清  男, 博士, 教授, 博士生导师。主要研究方向:航空发动机先进控制与健康管理技术

    通讯作者:

    郭迎清. E-mail: yqguo@nwpu.edu.cn

  • 中图分类号: V233.7

Information reconstruction algorithm of aero-engine sensors and actuators

More Information
  • 摘要:

    为实现航空发动机传感器与执行结构在故障情形下的故障幅值估计及信息重构,缓解故障对发动机性能的影响,在已有故障检测和故障隔离算法的基础上,提出一种基于修正的广义似然比(GLR)方法的信息重构算法。针对某型民用涡扇发动机的传感器与执行机构发生恒偏差与漂移故障的情形下进行了仿真验证。结果表明:基于修正的GLR方法对传感器和执行机构恒偏差和漂移故障的故障幅值估计具有较高的精度,两种故障情形下故障幅值的估计值的均方根误差均不超过0.005,故障部件信息重构后故障对系统性能的影响得到有效缓解。

     

  • 图 1  航空发动机传感器和执行机构信息重构系统结构

    Figure 1.  Information reconstruction system structure of aero-engine sensors and actuators

    图 2  恒偏差故障下的故障幅值估计

    Figure 2.  Fault magnitude estimation under constant deviation fault

    图 3  漂移故障下的故障幅值估计

    Figure 3.  Fault magnitude estimation under drift fault

    图 4  Nl传感器漂移故障下,故障信息重构前后,主燃油流量和7个可测参数变化趋势

    Figure 4.  Variation of main fuel flow and seven measurable parameters before and after fault information reconstruction under Nl sensor drift fault

    图 5  Nl传感器漂移故障下,故障信息重构前后,4个不可测参数变化趋势

    Figure 5.  Variation of four unmeasurable parameters before and after fault information reconstruction under Nl sensor drift fault

    图 6  VBV执行机构漂移故障下,故障信息重构前后,主燃油流量和7个可测参数变化趋势

    Figure 6.  Variation of main fuel flow and seven measurable parameters before and after fault information reconstruction under VBV actuator drift fault

    图 7  VBV执行机构漂移故障下,故障信息重构前后,4个不可测参数变化趋势

    Figure 7.  Variation of four unmeasurable parameters before and after fault information reconstruction under VBV actuator drift fault

    表  1  恒偏差故障下的故障幅值估计RSME计算结果

    Table  1.   Calculation results of RSME of constant deviation fault magnitude estimation

    传感器参数 RSME 执行机构参数 RSME
    Nl 0.001 3 Wfm 0.003 8
    Nh 0.001 5 VBV 0.004 7
    P25 0.002 1 VSV 0.003 6
    Ps3 0.001 6
    T25 0.002 3
    T3 0.001 7
    T45 0.002 4
    下载: 导出CSV

    表  2  漂移故障下的故障幅值估计RSME计算结果

    Table  2.   Calculation results of RSME of drift fault magnitude estimation

    传感器参数 RSME 执行机构参数 RSME
    Nl 0.001 4 Wfm 0.004 2
    Nh 0.001 8 VBV 0.004 8
    P25 0.002 8 VSV 0.004 3
    Ps3 0.002 1
    T25 0.002 5
    T3 0.002 1
    T45 0.002 7
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-05-18
  • 录用日期:  2019-07-05
  • 刊出日期:  2020-02-20

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