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一类存在参数摄动的线性随机系统的鲁棒间歇故障诊断方法

鄢镕易 何潇 周东华

鄢镕易, 何潇, 周东华. 一类存在参数摄动的线性随机系统的鲁棒间歇故障诊断方法. 自动化学报, 2016, 42(7): 1004-1013. doi: 10.16383/j.aas.2016.c150756
引用本文: 鄢镕易, 何潇, 周东华. 一类存在参数摄动的线性随机系统的鲁棒间歇故障诊断方法. 自动化学报, 2016, 42(7): 1004-1013. doi: 10.16383/j.aas.2016.c150756
YAN Rong-Yi, HE Xiao, ZHOU Dong-Hua. Robust Diagnosis of Intermittent Faults for Linear Stochastic Systems Subject to Time-varying Perturbations. ACTA AUTOMATICA SINICA, 2016, 42(7): 1004-1013. doi: 10.16383/j.aas.2016.c150756
Citation: YAN Rong-Yi, HE Xiao, ZHOU Dong-Hua. Robust Diagnosis of Intermittent Faults for Linear Stochastic Systems Subject to Time-varying Perturbations. ACTA AUTOMATICA SINICA, 2016, 42(7): 1004-1013. doi: 10.16383/j.aas.2016.c150756

一类存在参数摄动的线性随机系统的鲁棒间歇故障诊断方法

doi: 10.16383/j.aas.2016.c150756
基金项目: 

国家自然科学基金 61290324

国家自然科学基金 61490701

山东省泰山学者优势特色学科人才团队支持计划 [2015]73

国家自然科学基金 61522309

国家自然科学基金 61473163

清华大学自主科研项目 025-CMY-Z09

详细信息
    作者简介:

    鄢镕易 清华大学自动化系博士研究生.主要研究方向为间歇故障诊断与容错控制, 高速列车故障诊断与容错控制.E-mail:yry10@mails.tsinghua.edu.cn

    何潇  清华大学自动化系副教授.主要研究方向为网络化系统的鲁棒滤波、故障诊断与容错控制, 无人机(群)智能自主控制中的安全性问题, 高速列车信息控制系统的故障诊断.E-mail:hexiao@tsinghua.edu.cn

    通讯作者:

    周东华 山东科技大学电气与自动化工程学院教授, 清华大学自动化系教授.主要研究方向为动态系统的故障诊断与容错控制, 故障预测与智能维护技术.本文通信作者.E-mail:zdh@mail.tsinghua.edu.cn

Robust Diagnosis of Intermittent Faults for Linear Stochastic Systems Subject to Time-varying Perturbations

Funds: 

Supported by National Natural Science Foundation of China 61290324

Supported by National Natural Science Foundation of China 61490701

Research Fund for the Taishan Scholar Project of Shandong Province [2015]73

Supported by National Natural Science Foundation of China 61522309

Supported by National Natural Science Foundation of China 61473163

Tsinghua University Initiative Scientific Research Program 025-CMY-Z09

More Information
    Author Bio:

    Ph. D. candidate in the Department of Automation, Tsinghua University. His research interest covers fault diagnosis and tolerance control of intermittent faults, fault diagnosis for the information control system of high-speed trains

    Associate professor in the Department of Automation, Tsinghua University. His research interest covers robust estimation, fault diagnosis and tolerant control of networked systems, safety problems in intelligent autonomous control of unmanned aerial vehicles, fault diagnosis for the information control system of high-speed trains

    Corresponding author: ZHOU Dong-Hua Professor at the College of Electrical Engineering and Automation, Shandong University of Science and Technology, and the Department of Automation, Tsinghua University. His research interest covers fault diagnosis and tolerant control, fault prediction and intelligent maintenance. Corresponding author of this paper
  • 摘要: 间歇故障(Intermittent faults, IFs)具有随机性,其检测要求在本次间歇故障消失之前检测出间歇故障的发生,在下一次间歇故障发生之前检测出间歇故障的消失.本文针对一类存在未知时变参数摄动的离散线性随机动态系统,研究了其鲁棒间歇故障检测与分离问题.基于降维未知输入观测器,通过引入滑动时间窗口,本文设计了一组与未知时变摄动解耦的结构化截断残差,并提出其存在的一个充分条件.与传统残差相比,截断残差信号更为显著地反映了间歇故障的发生和消失.为满足间歇故障的检测要求,本文提出两个假设检验分别用于检测间歇故障的发生时刻和消失时刻,并给出了一个详细算法.最后,在沿参考轨道运行的卫星模型上对所述方法进行了仿真实验,结果表明该方法能够有效检测出间歇故障的所有发生时刻和消失时刻,并准确实现故障分离.
  • 图  1  间歇故障与滑动时间窗口的相对位置关系

    Fig.  1  Relative positions between the intermittent fault and the sliding-time windo

    图  2  正常运行时的系统输出

    Fig.  2  Normal output of the satellite system (20)

    图  3  k=500时发生间歇m3(k)的系统输出

    Fig.  3  Output of system (20) subject to the IF m3(k)

    图  4  初始残差信号 $r_1(k)$ 和新残差信号 $r_1(k, \Delta k_1)$

    Fig.  4  Comparing $r_1(k, \Delta k_1)$ with $r_1(k)$

    图  5  初始残差信号 $r_2(k)$ 和新残差信号 $r_2(k, \Delta k_2) $

    Fig.  5  Comparing $r_2(k, \Delta k_2)$ with $r_2(k)$

    图  6  初始残差信号 $r_3(k)$ 和新残差信号 $r_3(k, \Delta k_3) $

    Fig.  6  Comparing $r_3(k, \Delta k_3)$ with $r_3(k)$

    图  7  间歇故障检测结果

    Fig.  7  by using the proposed method

    图  8  基于Kalman滤波方法的残差信号

    Fig.  8  The Kalman filter based residual

    表  1  间歇故障发生(消失)时刻及其实际检测值

    Table  1  The detection result of $m_3(k)$ by using the proposed method

    q $\mu_{3, q}$ $\mu_{3, q}^{\text{dec}}$ $\nu_{3, q}$ $\nu_{3, q}^{\text{dec}}$
    1 5.00 5.03 5.57 5.62
    2 6.02 6.03 6.59 6.67
    3 7.14 7.15 7.75 7.77
    4 8.32 8.34 8.83 8.87
    5 9.26 9.28 9.76 9.87
    下载: 导出CSV
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  • 收稿日期:  2015-11-11
  • 录用日期:  2016-03-20
  • 刊出日期:  2016-07-01

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