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考虑执行器性能退化的控制系统剩余寿命预测方法

施权 胡昌华 司小胜 扈晓翔 张正新

施权, 胡昌华, 司小胜, 扈晓翔, 张正新. 考虑执行器性能退化的控制系统剩余寿命预测方法. 自动化学报, 2019, 45(5): 941-952. doi: 10.16383/j.aas.2018.c170027
引用本文: 施权, 胡昌华, 司小胜, 扈晓翔, 张正新. 考虑执行器性能退化的控制系统剩余寿命预测方法. 自动化学报, 2019, 45(5): 941-952. doi: 10.16383/j.aas.2018.c170027
SHI Quan, HU Chang-Hua, SI Xiao-Sheng, HU Xiao-Xiang, ZHANG Zheng-Xin. Remaining Useful Lifetime Prediction Method of Controlled Systems Considering Performance Degradation of Actuator. ACTA AUTOMATICA SINICA, 2019, 45(5): 941-952. doi: 10.16383/j.aas.2018.c170027
Citation: SHI Quan, HU Chang-Hua, SI Xiao-Sheng, HU Xiao-Xiang, ZHANG Zheng-Xin. Remaining Useful Lifetime Prediction Method of Controlled Systems Considering Performance Degradation of Actuator. ACTA AUTOMATICA SINICA, 2019, 45(5): 941-952. doi: 10.16383/j.aas.2018.c170027

考虑执行器性能退化的控制系统剩余寿命预测方法

doi: 10.16383/j.aas.2018.c170027
基金项目: 

国家自然科学基金 61773386

国家自然科学基金 61473094

国家自然科学基金 61573365

国家自然科学基金 61673311

国家自然科学基金 61374126

国家自然科学基金 61573366

中国科协青年人才托举工程 2016QNRC001

详细信息
    作者简介:

    施权  火箭军工程大学控制工程系硕士研究生.主要研究方向为剩余寿命预测和延寿控制.E-mail:shiquan_93@163.com

    司小胜  火箭军工程大学控制工程系副教授.2014年获得火箭军工程大学博士学位.主要研究方向为预测与健康管理, 剩余寿命预测, 可靠性与预测维护.E-mail:sxs09@mails.tsinghua.edu.cn

    扈晓翔  火箭军工程大学控制工程系讲师.2012年获得火箭军工程大学博士学位.主要研究方向为非线性控制.E-mail:sxs09@mails.tsinghua.edu.cn

    张正新  火箭军工程大学控制工程系博士.2017年获得火箭军工程大学博士学位.主要研究方向为预测与健康管理, 预测维护和寿命预测.E-mail:zhangzhengxin13@gmail.com

    通讯作者:

    胡昌华  火箭军工程大学控制工程系教授.1996年获得西北工业大学博士学位.主要研究方向为故障诊断与预测, 可靠性工程, 寿命预测和容错控制.本文通信作者.E-mail:hch6603@263.net

Remaining Useful Lifetime Prediction Method of Controlled Systems Considering Performance Degradation of Actuator

Funds: 

National Natural Science Foundation of China 61773386

National Natural Science Foundation of China 61473094

National Natural Science Foundation of China 61573365

National Natural Science Foundation of China 61673311

National Natural Science Foundation of China 61374126

National Natural Science Foundation of China 61573366

Young Elite Scientists Sponsorship Program of China Association for Science and Technology 2016QNRC001

More Information
    Author Bio:

     Master student in the Department of Control Engineering, Rocket Force University of Engineering. His research interest covers remaining useful life prediction and lifetime extending maintenance

     Associate professor in the Department of Control Engineering, Rocket Force University of Engineering. He received his Ph. D. degree from Rocket Force University of Engineering in 2014. His research interest covers prognostics and health management, remaining useful life prediction, reliability, and predictive maintenance

     Lecturer in the Department of Control Engineering, Rocket Force University of Engineering. He received his Ph. D. degree from Rocket Force University of Engineering in 2012. His research interest covers nonlinear control

     Ph. D. in the Department of Control Engineering, Rocket Force University of Engineering. He received his Ph. D. degree from Rocket Force University of Engineering in 2017. His research interest covers prognostics and health management, predictive maintenance, and lifetime prediction

    Corresponding author: HU Chang-Hua  Professor in the Department of Control Engineering, Rocket Force University of Engineering. He received his Ph. D. degree from North Western Polytechnic University in 1996. His research interest covers fault diagnosis and prediction, reliability engineering, life prognosis, and fault tolerant control. Corresponding author of this paper
  • 摘要: 工程控制系统在运行过程中,由于内外部应力的综合作用以及外部环境等的影响,其部件性能将逐渐退化,最终会导致控制系统失效.然而,由于控制系统中闭环反馈的作用,系统的输出残差可能仍在较小范围内变动,使得早期性能退化这种微小故障难以被检测到,呈现隐含退化的特点.现有文献中,针对此类在闭环反馈控制作用下部件存在隐含退化过程的控制系统剩余寿命(Remaining useful lifetime,RUL)预测问题,鲜有研究.为此,本文针对一类仅考虑执行器性能退化的确定闭环控制系统,提出一种基于解析模型的剩余寿命预测方法.该方法首先基于权值优选粒子滤波算法,利用系统的监测数据在线估计出执行器的隐含退化量,然后在每一个预测时刻通过蒙特卡洛(Monte Carlo,MC)仿真计算得到合理的失效阈值,建立基于该失效阈值的系统失效判断准则,最后将隐含退化量的估计值代入退化模型中外推出剩余寿命分布.惯性平台稳定回路控制系统的仿真实验结果验证了该方法的可行性、有效性.
    1)  本文责任编委 钟麦英
  • 图  1  考虑执行器性能退化的闭环控制系统失效过程

    Fig.  1  Failure process of closed-loop controlled system considering performance degradation of an actuator

    图  3  预测建模原理图

    Fig.  3  Illustration of the prediction modelling principle

    图  2  变失效阈值和固定失效阈值的对比

    Fig.  2  Comparison of variable failure threshold and fixed failure threshold

    图  4  惯性平台稳定回路控制系统

    Fig.  4  Stabilization loop controlled system in inertial platform

    图  5  惯性平台稳定回路控制系统仿真

    Fig.  5  Simulation of stabilization loop controlled system for inertial platform

    图  6  系统的剩余寿命分布

    Fig.  6  Remaining reliability life distribution of the system

    图  7  不同预测时刻的可靠度

    Fig.  7  Reliability calculated at different predicting moments

    表  1  惯性平台稳定回路模型

    Table  1  Stabilization loop model in inertial platform

    系统参数
    $ J=0.83\, {\rm kg}\cdot {\rm m}^{2} $ $ L_{{\rm m}} =2.7\, {\rm mH} $ $ R_{{\rm m}} =3.6\, \Omega $
    $ K_{{\rm m}} =0.407\, {{\rm N}\cdot {\rm m}}/{{\rm A}} $ $ K_{{\rm e}} =0.478\, {{\rm V}}/({\rm rad}\cdot {\rm s}) $ $ \left| u \right|_{\max } =80\, {\rm V} $
    控制器参数
    $ K_{{\rm P}} =10.54 $ $ T_{{\rm I}} =15.58 $ $ T_{{\rm D}} =15.37 $
    初始状态量
    $ x_{1} (0)=0 $ $ x_{2} (0)=0 $ $ x_{3} (0)=0 $
    $ x_{4} (0)=0.407 $ $ y(0)=0 $
    过程噪声参数
    $ q_{1} =0.2 $ $ q_{2} ={\rm 0.00008} $ $ q_{3} ={\rm 0.0000000003} $
    退化过程参数
    $ \lambda =-0.000257 $ $ \sigma_{{\rm B}} =0.0000002 $
    下载: 导出CSV

    表  2  基于固定阈值不同时刻预测结果对比

    Table  2  Comparison of prediction results based on fixed threshold at different times

    预测时刻 10 s 20 s 30 s 40 s
    失效阈值 0.12205 0.12205 0.12205 0.12205
    真实剩余寿命 36.2 26.2 16.2 6.2
    剩余阈值寿命 36.1 26.1 16.1 6.1
    $ MSE_{K_{{\rm m}} } $ 5.6135 10.9825 7.6921 3.1061
    $ MSE_{e} $ 5.5925 10.5087 7.2746 2.8566
    下载: 导出CSV

    表  3  基于变阈值不同时刻预测结果对比

    Table  3  Comparison of prediction results based on variable threshold at different times

    预测时刻 10 s 20 s 30 s 40 s
    失效阈值 0.16321 0.17461 0.13787 0.12205
    真实剩余寿命 36.2 26.2 16.2 6.2
    剩余阈值寿命 33.4 22.9 15.5 6.1
    $ MSE_{K_{{\rm m}} } $ 6.7076 6.2822 5.9862 3.1061
    $ MSE_{e} $ 8.1668 7.8860 4.0403 2.8566
    下载: 导出CSV
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  • 收稿日期:  2017-01-12
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