2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

复杂动态系统的实际非完全失效故障的可诊断性评估

符方舟 王大轶 李文博

符方舟, 王大轶, 李文博. 复杂动态系统的实际非完全失效故障的可诊断性评估. 自动化学报, 2017, 43(11): 1941-1949. doi: 10.16383/j.aas.2017.c160393
引用本文: 符方舟, 王大轶, 李文博. 复杂动态系统的实际非完全失效故障的可诊断性评估. 自动化学报, 2017, 43(11): 1941-1949. doi: 10.16383/j.aas.2017.c160393
FU Fang-Zhou, WANG Da-Yi, LI Wen-Bo. Quantitative Evaluation of Actual LOE Fault Diagnosability for Dynamic Systems. ACTA AUTOMATICA SINICA, 2017, 43(11): 1941-1949. doi: 10.16383/j.aas.2017.c160393
Citation: FU Fang-Zhou, WANG Da-Yi, LI Wen-Bo. Quantitative Evaluation of Actual LOE Fault Diagnosability for Dynamic Systems. ACTA AUTOMATICA SINICA, 2017, 43(11): 1941-1949. doi: 10.16383/j.aas.2017.c160393

复杂动态系统的实际非完全失效故障的可诊断性评估

doi: 10.16383/j.aas.2017.c160393
基金项目: 

国家杰出青年科学基金项目 61525301

国家自然科学基金项目 61640304

国家自然科学基金项目 61690215

详细信息
    作者简介:

    符方舟  北京控制工程研究所博士生.2015年获得哈尔滨工业大学深圳研究生院硕士学位.主要研究方向为控制系统的故障诊断, 可诊断性评价.E-mail:fizssg@163.com

    李文博  北京控制工程研究所高级工程师.2012年获得哈尔滨工业大学博士学位.主要研究方向为故障诊断与容错控制, 卫星控制系统的可诊断性评价与设计.E-mail:liwenbo_bice@163.com

    通讯作者:

    王大轶  北京空间飞行器总体设计部研究员.主要研究方向为航天器的自主制导、导航与控制, 故障诊断与容错控制.本文通信作者.E-mail:dayiwang@163.com

Quantitative Evaluation of Actual LOE Fault Diagnosability for Dynamic Systems

Funds: 

National Science Funds for Distinguished Young Scholar of China 61525301

National Natural Science Foundation of China 61640304

National Natural Science Foundation of China 61690215

More Information
    Author Bio:

     Ph.D. Candidate at Beijing Institute of Control Engineering. He received his master degree from Harbin Institute of Technology Shenzhen Graduate School in 2015. His research interest covers fault diagnosis and fault diagnosability evaluation.

     Senior engineer at Beijing Institute of Control Engineering. He received his Ph. D. degree from Harbin Institute of Technology in 2012. His research interest covers fault diagnosis and tolerant control, fault diagnosability evaluation and design for satellite control systems.

    Corresponding author: WANG Da-Yi   Professor at Beijing Institute of Spacecraft System Engineering. His research interest covers autonomous guidance, navigation and control, fault diagnosis, and tolerant control for spacecrafts. Corresponding author of this paper
  • 摘要: 针对缺乏有效的非完全失效故障(Loss of effect,LOE)可诊断性量化分析方法的现状,本文提出了一种基于距离相似度的系统非完全失效故障的实际可诊断性评价方法.通过将状态空间描述的动态系统转换为时间堆栈动态模型,使故障的可诊断性评估分析问题转化为多元分布的相似度问题.给出系统非完全失效故障可检测性与可隔离性的相关定义,并对故障的可诊断性进行量化.通过求取最小二乘解计算最小巴氏距离,增大了算法适用范围.最后,通过仿真实例验证评价方法的有效性,并通过所提出的可诊断性评估算法求取非完全失效故障的最大可诊断效能系数.
    1)  本文责任编委 胡昌华
  • 图  1  不同时间窗口长度对系统(16)故障可诊断性评价结果的影响

    Fig.  1  Curves comparing computed distinguishability of dynamic systems (16) with different window length $s$

    图  2  不同效能系数$\varepsilon$对系统(16)故障可检测性评价结果的影响

    Fig.  2  Curves comparing computed detectability of dynamic systems (16) with different effectiveness coefficient $\varepsilon$

    图  3  不同效能系数$\varepsilon$对系统(16)故障可检测性评价结果的影响

    Fig.  3  Curves comparing computed isolability of dynamic systems (16) with different effectiveness coefficient $\varepsilon$

    表  1  系统(16)在时间序列$\theta $的输入下的可诊断评价结果($\varepsilon = 0.8$, $s = 5$)

    Table  1  Computed distinguishability of dynamic systems (16) with the given fault time profile $\theta $ ($\varepsilon = 0.8$, $s = 5$)

    $F{D_\theta }/F{I_\theta }$ ${\rm NF}$ $f_1$ $f_2$ $f_3$
    $f_1$155.022 0 84.110 3.0614
    $f_2$274.632 131.22 0 111.20
    $f_3$830.902 6.7403 126.05 0
    下载: 导出CSV

    表  2  系统(16)在时间序列$\theta $的输入下的可诊断评价结果($s = 3$)

    Table  2  Computed distinguishability of dynamic systems (16) with the given fault time profile $\theta $ ($s = 3$)

    $F{D_\theta }/F{I_\theta }$ ${\rm NF}$ $f_1$ $f_2$ $f_3$
    $f_1$130.6592 0 0 0
    $f_2$61.6940 0 0 0
    $f_3$346.1748 0 0 0}
    下载: 导出CSV

    表  3  系统(16)在时间序列$\theta $的输入下的可诊断评价结果($s = 6$)~($ \times {10^3}$)

    Table  3  Computed distinguishability of dynamic systems (16) with the given fault time profile $\theta $ $(s = 6)~( \times {10^3})$

    $F{D_\theta }/F{I_\theta }$ ${\rm NF}$ $f_1$ $f_2$ $f_3$
    $f_1$0.3585 0 0.0853 0.0040
    $f_2$0.5403 0.2581 0 0.1489
    $f_3$1.3274 0.0225 0.2751 0}
    下载: 导出CSV

    表  4  系统(16)在时间序列$\theta $的输入下的可诊断评价结果$(\varepsilon = 0.5)$ $( \times {10^3})$

    Table  4  Computed distinguishability of dynamic systems (16) with the given fault time profile $\theta $ $(\varepsilon = 0.5)$ $( \times {10^3})$

    $F{D_\theta }/F{I_\theta }$ ${\rm NF}$ $f_1$ $f_2$ $f_3$
    $f_1$0.9689 0 0.5257 0.0191
    $f_2$1.7164 0.8202 0 0.6950
    $f_3$5.1931 0.0421 0.7878 0}
    下载: 导出CSV

    表  5  系统(16)在时间序列$\theta $的输入下的最大可诊断效能系数$({p_i} = 0.3,{p_{i,j}} = 0.4)$

    Table  5  Maximum effectiveness coefficient of dynamic systems (16) with the given fault time profile $\theta $ $({p_i} = 0.3,{p_{i,j}} = 0.4)$

    $\varepsilon $ ${\rm NF}$ $f_1$ $f_2$ $f_3$
    $f_1$ 0.45 0 0.36 0.36
    $f_2$ 0.45 0.36 0 0.36
    $f_3$ 0.45 0.36 0.36 0
    下载: 导出CSV
  • [1] Wang Y L, Ma G F, Ding S X, Li C J. Subspace aided data-driven design of robust fault detection and isolation systems. Automatica, 2011, 47(11):2474-2480 doi: 10.1016/j.automatica.2011.05.028
    [2] Michail K, Deliparaschos K M, Tzafestas S G, Zolotas A C. AI-based actuator/sensor fault detection with low computational cost for industrial applications. IEEE Transactions on Control Systems Technology, 2016, 24(1):293-301 doi: 10.1109/TCST.2015.2422794
    [3] Castillo I, Edgar T F, Dunia R. Nonlinear detection and isolation of multiple faults using residuals modeling. Industrial & Engineering Chemistry Research, 2013, 53(13):5217-5233
    [4] Sadeh J, Bakhshizadeh E, Kazemzadeh R. A new fault location algorithm for radial distribution systems using modal analysis. International Journal of Electrical Power & Energy Systems, 2013, 45(1):271-278
    [5] Ding S. Model-based Fault Diagnosis Techniques:Design Schemes, Algorithms, and Tools. Berlin Heidelberg:Springer-Verlag, 2008. 69-83 http://d.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ023040606/
    [6] Del Gobbo D, Napolitano M R. Issues in fault detectability for dynamic systems. In:Proceedings of the 2000 American Control Conference. Chicago, IL, USA:IEEE, 2000. 3203-3207
    [7] Pucel X, Travé-Massuyès L, Pencolé Y. Another point of view on diagnosability. In:Proceedings of the 2008 conference on STAIRS 2008:Proceedings of the Fourth Starting AI Researchers' Symposium. Amsterdam, The Netherlands:IOS Press, 2008. 151-162
    [8] Leal R, Aguilar J, Travé-Massuyès L, Camargo E, Rios A. An approach for diagnosability analysis and sensor placement for continuous processes based on evolutionary algorithms and analytical redundancy. Applied Mathematical Sciences, 2015, 9(43):2125-2146
    [9] Chi G Y, Wang D W, Zhu S Q. An integrated approach for sensor placement in linear dynamic systems. Journal of the Franklin Institute, 2015, 352(3):1056-1079 doi: 10.1016/j.jfranklin.2014.11.013
    [10] Jung D, Khorasgani H, Frisk E, Krysander M, Biswas G. Analysis of fault isolation assumptions when comparing model-based design approaches of diagnosis systems. IFAC-PapersOnLine, 2015, 48(21):1289-1296 doi: 10.1016/j.ifacol.2015.09.703
    [11] Nyberg M. Criterions for detectability and strong detectability of faults in linear systems. International Journal of Control, 2002, 75(7):490-501 doi: 10.1080/00207170110121303
    [12] Roberts C, Dassanayake H P B, Lehrasab N, Goodman C J. Distributed quantitative and qualitative fault diagnosis:railway junction case study. Control Engineering Practice, 2002, 10(4):419-429 doi: 10.1016/S0967-0661(01)00159-9
    [13] Travé-Massuyès L, Escobet T, Olive X. Diagnosability analysis based on component-supported analytical redundancy relations. IEEE Transactions on Systems, Man, and Cybernetics, Part A:Systems and Humans, 2006, 36(6):1146-1160 doi: 10.1109/TSMCA.2006.878984
    [14] Ding S X. Application of factorization and gap metric techniques to fault detection and isolation Part Ⅱ:gap metric technique aided FDI performance analysis. IFAC-PapersOnLine, 2015, 48(21):119-124 doi: 10.1016/j.ifacol.2015.09.514
    [15] Sharifi R, Langari R. Isolability of faults in sensor fault diagnosis. Mechanical Systems and Signal Processing, 2011, 25(7):2733-2744 doi: 10.1016/j.ymssp.2011.02.015
    [16] Khorasgani H, Eriksson D, Biswas G, Frisk E, Krysander M. Off-line robust residual selection using sensitivity analysis. In:Proceedings of the 25th International Workshop on Principles of Diagnosis (DX-14). Graz, Austria, 2014.
    [17] Eriksson D, Frisk E, Krysander M. A method for quantitative fault diagnosability analysis of stochastic linear descriptor models. Automatica, 2013, 49(6):1591-1600 doi: 10.1016/j.automatica.2013.02.045
    [18] 李文博, 王大轶, 刘成瑞.动态系统实际故障可诊断性的量化评价研究.自动化学报, 2015, 41(3):497-507 http://www.aas.net.cn/CN/abstract/abstract18628.shtml

    Li Wen-Bo, Wang Da-Yi, Liu Cheng-Rui. Quantitative evaluation of actual fault diagnosability for dynamic systems. Acta Automatica Sinica, 2015, 41(3):497-507 http://www.aas.net.cn/CN/abstract/abstract18628.shtml
    [19] 李文博, 王大轶, 刘成瑞.卫星姿态确定系统的故障可诊断性分析方法.航天控制, 2014, 32(6):50-56 http://d.wanfangdata.com.cn/Periodical/htkz201406011

    Li Wen-Bo, Wang Da-Yi, Liu Cheng-Rui. An approach to fault diagnosability analysis of satellite attitude determination systems. Aerospace Control, 2014, 32(6):50-56 http://d.wanfangdata.com.cn/Periodical/htkz201406011
    [20] Jung D, Frisk E, Krysander M. Quantitative isolability analysis of different fault modes. IFAC-PapersOnLine, 2015, 48(21):1275-1282 doi: 10.1016/j.ifacol.2015.09.701
    [21] 宣国荣, 柴佩琪.基于巴氏距离的特征选择.模式识别与人工智能, 1996, 9(4):324-329 http://d.wanfangdata.com.cn/Periodical/jsjgcyyy200436028

    Xuan Guo-Rong, Chai Pei-Qi. Feature selection based on Bhattacharyya distance. PR & AI, 1996, 9(4):324-329 http://d.wanfangdata.com.cn/Periodical/jsjgcyyy200436028
  • 加载中
图(3) / 表(5)
计量
  • 文章访问数:  2181
  • HTML全文浏览量:  289
  • PDF下载量:  671
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-05-11
  • 录用日期:  2016-08-15
  • 刊出日期:  2017-11-20

目录

    /

    返回文章
    返回