基于观测器的线性系统故障检测方法研究
|
摘要
基于观测器的故障检测方法一直是基于解析模型故障诊断方法研究的热点,它充分利用系统的数学模型,具有鲁棒性强、效率高和可靠性好等优点。但大多基于观测器的故障检测系统的设计都采用H∞优化技术,设计方法、算法复杂,为工程实践带来不便。
本论文在介绍基于观测器的故障诊断原理的基础上,从开环和闭环两方面介绍了如何对故障系统建模,并对倒立摆系统、三容水箱系统、汽车横向动态系统三个应用实例详细分析了动态系统的故障建模。
然后,针对线性时不变离散系统,结合观测器理论与等价空间方法,建立评价函数,并确定阈值,把传统的优化问题转化为对观测器和滤波器的优化设计来求解,简化了设计方法,并获得了较为满意的结果。数值仿真验证了方法的有效性。
最后,针对线性时不变连续系统,构建输出观测器生成残差,通过添加后置滤波器来抑制残差中不感兴趣部分而加强其中反应故障的成分,以区分故障与模型不确定性等未知输入的影响,设计方法、算法简单。数值仿真和倒立摆系统、三容水箱系统、汽车横向动态系统三个应用实例仿真说明了有后置滤波器的残差检测效果要明显优于无后置滤波器的效果,验证了方法的有效性。
The observer-based fault detection method, which is robust, strong, high efficiency and good reliability, has been a hot in the model-based fault diagnosis method. The mathematical model of the system was made full use of by the observer-based fault detection method. But the H_∞optimization technology was used in most of the observer-based fault detection systems, which was proved to be complex and inconvenient in engineering practice.
The open-loop and closed-loop fault systems are introduced to explain how to model for the fault systems on the basis of introducing the observer-based fault diagnosis principle. The models of the inverted pendulum control system, the three tank system and the vehicle lateral dynamic system are analyzed minutely.
For LTI discrete systems, combining the observer theory with the parity space, the evaluation function is established, and the threshold is confirmed. The traditional optimization problems are transformed into the design of the observer and filter, which are simplified, so the good results are obtained. Numerical simulation shows the effectiveness of the method.
Finally, for LTI continuous system, the output observer is constructed to generate the residual. Curbing the disagreeable aspect and strengthening the needed aspect of the residual by adding a post-filter, which are aim to distinguish the influence of the fault and the model uncertainty, and the algorithm is simple. The detecting result of using post-filter is proved to be better than the detecting result of non post-filter by numerical simulations and the simulations of the inverted pendulum control system, the three tank system and the vehicle lateral dynamic system. Simulation examples show the effectiveness of the design method and the algorithm.
本论文在介绍基于观测器的故障诊断原理的基础上,从开环和闭环两方面介绍了如何对故障系统建模,并对倒立摆系统、三容水箱系统、汽车横向动态系统三个应用实例详细分析了动态系统的故障建模。
然后,针对线性时不变离散系统,结合观测器理论与等价空间方法,建立评价函数,并确定阈值,把传统的优化问题转化为对观测器和滤波器的优化设计来求解,简化了设计方法,并获得了较为满意的结果。数值仿真验证了方法的有效性。
最后,针对线性时不变连续系统,构建输出观测器生成残差,通过添加后置滤波器来抑制残差中不感兴趣部分而加强其中反应故障的成分,以区分故障与模型不确定性等未知输入的影响,设计方法、算法简单。数值仿真和倒立摆系统、三容水箱系统、汽车横向动态系统三个应用实例仿真说明了有后置滤波器的残差检测效果要明显优于无后置滤波器的效果,验证了方法的有效性。
The observer-based fault detection method, which is robust, strong, high efficiency and good reliability, has been a hot in the model-based fault diagnosis method. The mathematical model of the system was made full use of by the observer-based fault detection method. But the H_∞optimization technology was used in most of the observer-based fault detection systems, which was proved to be complex and inconvenient in engineering practice.
The open-loop and closed-loop fault systems are introduced to explain how to model for the fault systems on the basis of introducing the observer-based fault diagnosis principle. The models of the inverted pendulum control system, the three tank system and the vehicle lateral dynamic system are analyzed minutely.
For LTI discrete systems, combining the observer theory with the parity space, the evaluation function is established, and the threshold is confirmed. The traditional optimization problems are transformed into the design of the observer and filter, which are simplified, so the good results are obtained. Numerical simulation shows the effectiveness of the method.
Finally, for LTI continuous system, the output observer is constructed to generate the residual. Curbing the disagreeable aspect and strengthening the needed aspect of the residual by adding a post-filter, which are aim to distinguish the influence of the fault and the model uncertainty, and the algorithm is simple. The detecting result of using post-filter is proved to be better than the detecting result of non post-filter by numerical simulations and the simulations of the inverted pendulum control system, the three tank system and the vehicle lateral dynamic system. Simulation examples show the effectiveness of the design method and the algorithm.
引文
[1] 张萍,王桂增等.动态系统的故障诊断方法.控制理论与应用, 2000,17(2): 153-158.
[2] 陈玉东,施颂椒等.动态系统的故障诊断方法综述.化工自动化及仪表, 2001,28(3):1-14.
[3] Kinnaert M. Fault diagnosis based on analytical models for linear and nonlinear systems.In Proc. SAFEPROCESS'2003, 2003:37-50.
[4] Frank P M., Ding S X.Current developments in the theory of FDI. In proc.SAFEPROCESS.2000. 2000:16-27.
[5] Frank PM.Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy -a survey and some new results. Automatica, 1990,26(3): 459-474.
[6] Chen J and Patton R J. Robust Model-Based Fault Diagnosis for Dynamic Systems. Kluwer Academic Publishers. Boston, 1999.
[7] Venkat V, Raghunathan R and Kewen Y. A review of process fault detection and diagnosis Part II:Qualitative models and search strategies.Computer and Chemical Engineering, 2003.27(3): 313-326.
[8] Mehra R K and Peshon I. An innovations approach to fault detection and diagnosis in dynamic systems. Autmatica,1971,7:637-640.
[9] Clark R N, Eosth D C and Walton V M.. Detecting instrument malfunctions in control systems. IEEE Trans. On Aerospace&Electronic.1975,11:465-473.
[10] Clark R N. A simplified instrument failure detection scheme. IEEE Trans. Aerospace Electron System,1975,14(4):558-563.
[11] Frank P M.Fault diagnosis on the basis of dynamic process models. Presented at 12th IMACS World Congress on Scientific Computation,Paris,1988:18-22 .
[12] Basseville M. Information criteria for residual generation and fault detection and isolation. Automatica.1997, 33(5):783-803.
[13] Frank P M. Enhancement of robustness in observer-based fault detection. International Journal of Control.1994,59(4):955-981.
[14] Ding X,Guo L,Frank P M. A frequency domain approach to fault detection of uncertain dynamic systems.Proceedings of the 32nd IEEE CDC,1993:1722-1727.
[15] 罗锦,孟晨,苏振中.故障诊断技术的发展和展望.自动化与仪器仪表,2003,(2):1-2.
[16] 赵翔,李著信,萧德云.故障诊断技术的研究现状与发展趋势.机床与液压,2002,(4):3-6.
[17] Beard R V.Failure accommodation in linear systems through self reorganization. Ph DDissertation, Massachusetts Inst Tech, 1971:55-82.
[18] Qui Z, Gertler J. Robust FDI systems and H∞ optimization. Proceedings of the 32nd IEEE CDC, 1993:1710-1715.
[19] 周东华等.故障检测与诊断技术. 控制理论与应用.1991,8(1) :1-10.
[20] Gertler J J. Survey of model-based failure detection and isolation in complex plants. IEEE Control Systems Magazine 1988,3:3-11.
[21] 周 东 华 . 王 庆 林 . 基 于 模 型 的 控 制 系 统 故 障 诊 断 技 术 的 最 新 进 展 . 自 动 化 学报.1995:21(2):244-247.
[22] 闻新,张洪锁,周露.控制系统的故障诊断和容错控制.北京:机械工业出版社,1998.
[23] 周东华,孙优贤.控制系统的故障监测与诊断技术.北京:清华大学出版社.1994.
[24] Magni J F and Mouyon P. On residual generation by observer and parity space approaches. IEEE Transactoins on Automatic Control, 1994, 39(2): 441-447.
[25] Ding S X,Jeinsch T. Application of observer based FDI schemes to the three tank system. Proc Of European Control Conference,Karlsruhe , Germany, 1999.
[26] Gentler J. Fault detection and isolation using parity relations. Control Eng Practice 1997, (5): 653-661.
[27] 周东华,叶银忠.现代故障诊断与容错控制.北京:清华大学出版社 2000.
[28] Venkat V, Raghunathan R and Kewen Y. A review of process fault detection and diagnosis Part I:Qualitative model-based methods.Computer and Chemical Engineering,2003,27(3): 293-311.
[29] 姜仓华,周东华.基于计算智能方法的动态系统故障诊断技术,控制工程.2003,10(5):385-390.
[30] 闻新,周露.神经网络故障诊断技术的可实现性.导弹与航天运载技术,2000, (2) : 17-22.
[31] 汤天浩,罗成汉,李杰仁.层次分类诊断模型的多重结构神经网络与应用. 上海海运学院学报, 1998, 19(4): 1-6.
[32] Koscielny J M. Application of fuzzy logic for fault isolation in a three tank system. In: P roc of 14th IFAC World Congress, Beijing, 1999:73-78.
[33] Heiming B, Lunze J. Parallel process diagnosis based on qualitative models. Int J Control, 2000, 73(11):1061-1077.
[34] 张登峰,王执锉,孙金生.控制系统故障诊断的理论与技术.数据采集.2002,17(3): 293-299.
[35] Rotem Y, Wachs A , Lewin D R. Ethylene compressor monitoring using mode-based PCA. A IChE J ,2000, 46 (9) : 1825-1836.
[36] 叶昊,王桂增,方崇智. 小波变换在故障检测中的应用. 自动化学报,1997,23 (6) :736-741.
[37] Kumamaru K. Robust fault detection using index of Kullback discrimination information. In: Proc of 13th IFAC World Congress, SanFrancisco,1996:205-210.
[38] Mironovski L A. Function diagnosis of linear dynamic systems. Automn Remote Control, 1979,40:1198-1205.
[39] Mironovski L A. Function diagnosis of linear dynamic systems-a survey, Automn Remote Control,1980,41:1122-1143.
[40] Frank P M. On-line fault detection in uncertain nonlinear systems using diagnostic observers: a survey. International Journal Systems Science,1994,25(12):2129-2154.
[41] Isermann R.Process fault detection based on modeling and estimation methods-a survey. Auto-matic,1984,20(44):387-404.
[42] Ray A,Luck R.An introduction to sensor signal validation in redundant measurement systems. IEEE Control Systems Magine, 1991,11(2):44-49.
[43] Kinnaert M.Design of Redundancy Relations for Failure Detection and Isolation by Constrained Optimization. Internatinal Journal of Control ,1996 ,63 (3) :609-622.
[44] Medvedev A. Fault detection and isolation by a continuous parity space method, Automati -ca .1995;31(7):1039-1044.
[45] Medvedev A.Fault detection and isolation by functional continuous deadbeat observers. Int.J.Contro1,1996,54(3):425-439.
[46] Medvedev A.. State estimation and fault detection by a bank of continuous finite-memory filters Int.J.Control,1998,69(4):499-517.
[47] Wang H, Chang D S. A Practical Design for a Robust Fault Detection and Isolation System. International Jouranl of System Science,1997,28(3) :265-275.
[48] GERTLER J, KUNWER M M. Optimal residual decoupling for robust fault diagnosis. International Journal of Control,1995,61(2): 395-421.
[49] GERTLER J, MONAJEMYR. Generating directional residuals with dynamic parity relations. Automatica ,1995,31(4):627-635.
[50] Thomas Hofling and Rolf Isermann. Adaptive parity equations and advanced parameter estimation for fault detection and diagnosis.Proc.of IFAC World Congress,1996:55-60.
[51] Frank P M, Ding X. Survey of robust residual generation and evaluation methods in observer- based fault detection systems. Journal of Process Control 1997,7(6):403-424.
[52] Ding X,Guo L and Frank P M. A frequency domain approach to fault detection of uncertain dynamic systems.Proceedings of the 32nd IEEE CDC, 1993:1722-1727.
[53] Hou M, Patton R J. An LMI approach to H-/H∞ fault detection observers. Proceedings of IEE Conference on Control 96, 1996:305-310.
[54] Qui Z, Gertler J. Robust FDI systems and H∞ optimization. Proceedings of the 32nd IEEE CDC,1993:1710-1715.
[55] Sauter D, Rambeaux F, Hamelin F. Robust fault diagnosis in an H∞ setting. Proceedings of SAFEPROCESS ,1997:879-884.
[56] Patton R J and Hou M. A matrix pencil approach to fault detection and isolation observers.Proc.of IFAC World Congress,1996:229-234.
[57] Patton R J and Hou M. Design of fault detection and isolation observers. Automatica,1998;34(9):1135-1140.
[58] Edwards C,Spurgeon S K,Patton R J.Sliding model observers for fault detection and isolation. Automatica.2000,36(3):541-553.
[59] Zhong M Y, DingS X and Lam J.An LMI approach to design robust fault detection filter for uncertain LTI systems.Automatica, 2003,39(3): 543-550.
[60] Chen R H,Mingori D L, Speyer J L. Optimal Stochastic Fault Detection Filter. Automatica, 2003,39(3): 377-390.
[61] Yannis E. Faitakis and Jeffrey C. Residual generation and fault detection for discrete time systems using an H∞ technique.Int.J.Control,1996,64(1):155-174.
[62] Ding S X, Jeinsch T, Frank P M, Ding E L. A unified approach to the optimization of fault detection systems. International Journal of Adaptive Control and Signal Processing, 2000,14(7): 725-745.
[63] Frank P M. Handling modeling uncertainty in fault detection and isolation system. In: The International Conference IPMU, France, 2002,(3):1-5.
[64] Patton R J, Chen J. A review of parity space approaches to fault diagnosis.In:Proceedings of IFAC Fault Detection, Supervision and Safety for Technical Processes, Baden Baden, Germany, 1991:65-81.
[65] Gertler J. Fault Detection and Diagnosis in Engineering Systems. Marcel Dekker, New York, 1998.
[66] Basseville M, Nikiforov I V .Detection of abrupt changes:theory and application, information and system science. Prentice Hall, New York, 1993.
[67] Patton R J, Frank P M, Clark R. Fault diagnosis in dynamic systems.and Application, Herfordshire: Prentice Hall, 1989.
[68] Frank P M, Marcu T. Diagnosis strategies and systems: principles, fuzzy and neural approaches. In: Intelligent Systems and Interfaces, Kluwer, 2000.
[69] Seliger R,Frank P M.Robust residual evaluation by threshold selection and performance indexfor non-linear observer-based fault diagnosis. In: Proceedings of TOOLDIAG'93, Toulouse.1993:65-88.
[70] Frank P M, Ding S X, Marcu T. Model-Based Fault Diagnosis in Technical Processes. Transactions of the Institute of Measurement and Control, 2000, 22(1):57-101.
[71] Frank P M, Analytical and qualitative model-based fault diagnosis-a survey and some new results. European Journal of Control, 1996,2(1): 6-28.
[72] Chen J, Patton R J. Robust model-based fault diagnosis for dynamic systems.Kluwer AcademicPublishers, 1999.
[73] Gentler J. Diagnosis parametric faults: from parameter estimation to parity relation.In: America Control Conference, Seattle,USA, 1995:1615-1620.
[74] Khan A, Ceglarek D, Ni J. Sensor location optimization for fault diagnosis in multi-fixture assembly systems. ASME J. Manuf. Sci. Eng., 1998, 120(5): 781-792.
[75] Zhou D H, Frank P M. Fault diagnostics and fault tolerant control. IEEE Trans. On Aerospace and Electronic Systems ,1998, 34(2) :420-427.
[76] Hou M, Mueller P C. Fault detection and isolation observers. International Journal of Control, 1995,60(5):827-846.
[77] Chen J, Patton R J, Zhang H Y Design of unknown input observers and robust fault detection filters. Int. J. Control, 1996, 63(1): 85-105.
[78] Ding S X, Guo L. An approach to time domain optimization of observer based fault detection systems. International Journal of Control, 1998, 69(3): 419-442.
[79] Zhou K, Doyle J C, Glover K. Robust and optimal control. Prentice-Hall: Englewood Cliffs, NJ, 1995.
[80] Zhong M Y, Tang B Y, Ding S X. LMI approach to observer-based FD systems designing. Journal of Dong Hua University(Eng.Ed.), 2001,18(4): 41-44.
[2] 陈玉东,施颂椒等.动态系统的故障诊断方法综述.化工自动化及仪表, 2001,28(3):1-14.
[3] Kinnaert M. Fault diagnosis based on analytical models for linear and nonlinear systems.In Proc. SAFEPROCESS'2003, 2003:37-50.
[4] Frank P M., Ding S X.Current developments in the theory of FDI. In proc.SAFEPROCESS.2000. 2000:16-27.
[5] Frank PM.Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy -a survey and some new results. Automatica, 1990,26(3): 459-474.
[6] Chen J and Patton R J. Robust Model-Based Fault Diagnosis for Dynamic Systems. Kluwer Academic Publishers. Boston, 1999.
[7] Venkat V, Raghunathan R and Kewen Y. A review of process fault detection and diagnosis Part II:Qualitative models and search strategies.Computer and Chemical Engineering, 2003.27(3): 313-326.
[8] Mehra R K and Peshon I. An innovations approach to fault detection and diagnosis in dynamic systems. Autmatica,1971,7:637-640.
[9] Clark R N, Eosth D C and Walton V M.. Detecting instrument malfunctions in control systems. IEEE Trans. On Aerospace&Electronic.1975,11:465-473.
[10] Clark R N. A simplified instrument failure detection scheme. IEEE Trans. Aerospace Electron System,1975,14(4):558-563.
[11] Frank P M.Fault diagnosis on the basis of dynamic process models. Presented at 12th IMACS World Congress on Scientific Computation,Paris,1988:18-22 .
[12] Basseville M. Information criteria for residual generation and fault detection and isolation. Automatica.1997, 33(5):783-803.
[13] Frank P M. Enhancement of robustness in observer-based fault detection. International Journal of Control.1994,59(4):955-981.
[14] Ding X,Guo L,Frank P M. A frequency domain approach to fault detection of uncertain dynamic systems.Proceedings of the 32nd IEEE CDC,1993:1722-1727.
[15] 罗锦,孟晨,苏振中.故障诊断技术的发展和展望.自动化与仪器仪表,2003,(2):1-2.
[16] 赵翔,李著信,萧德云.故障诊断技术的研究现状与发展趋势.机床与液压,2002,(4):3-6.
[17] Beard R V.Failure accommodation in linear systems through self reorganization. Ph DDissertation, Massachusetts Inst Tech, 1971:55-82.
[18] Qui Z, Gertler J. Robust FDI systems and H∞ optimization. Proceedings of the 32nd IEEE CDC, 1993:1710-1715.
[19] 周东华等.故障检测与诊断技术. 控制理论与应用.1991,8(1) :1-10.
[20] Gertler J J. Survey of model-based failure detection and isolation in complex plants. IEEE Control Systems Magazine 1988,3:3-11.
[21] 周 东 华 . 王 庆 林 . 基 于 模 型 的 控 制 系 统 故 障 诊 断 技 术 的 最 新 进 展 . 自 动 化 学报.1995:21(2):244-247.
[22] 闻新,张洪锁,周露.控制系统的故障诊断和容错控制.北京:机械工业出版社,1998.
[23] 周东华,孙优贤.控制系统的故障监测与诊断技术.北京:清华大学出版社.1994.
[24] Magni J F and Mouyon P. On residual generation by observer and parity space approaches. IEEE Transactoins on Automatic Control, 1994, 39(2): 441-447.
[25] Ding S X,Jeinsch T. Application of observer based FDI schemes to the three tank system. Proc Of European Control Conference,Karlsruhe , Germany, 1999.
[26] Gentler J. Fault detection and isolation using parity relations. Control Eng Practice 1997, (5): 653-661.
[27] 周东华,叶银忠.现代故障诊断与容错控制.北京:清华大学出版社 2000.
[28] Venkat V, Raghunathan R and Kewen Y. A review of process fault detection and diagnosis Part I:Qualitative model-based methods.Computer and Chemical Engineering,2003,27(3): 293-311.
[29] 姜仓华,周东华.基于计算智能方法的动态系统故障诊断技术,控制工程.2003,10(5):385-390.
[30] 闻新,周露.神经网络故障诊断技术的可实现性.导弹与航天运载技术,2000, (2) : 17-22.
[31] 汤天浩,罗成汉,李杰仁.层次分类诊断模型的多重结构神经网络与应用. 上海海运学院学报, 1998, 19(4): 1-6.
[32] Koscielny J M. Application of fuzzy logic for fault isolation in a three tank system. In: P roc of 14th IFAC World Congress, Beijing, 1999:73-78.
[33] Heiming B, Lunze J. Parallel process diagnosis based on qualitative models. Int J Control, 2000, 73(11):1061-1077.
[34] 张登峰,王执锉,孙金生.控制系统故障诊断的理论与技术.数据采集.2002,17(3): 293-299.
[35] Rotem Y, Wachs A , Lewin D R. Ethylene compressor monitoring using mode-based PCA. A IChE J ,2000, 46 (9) : 1825-1836.
[36] 叶昊,王桂增,方崇智. 小波变换在故障检测中的应用. 自动化学报,1997,23 (6) :736-741.
[37] Kumamaru K. Robust fault detection using index of Kullback discrimination information. In: Proc of 13th IFAC World Congress, SanFrancisco,1996:205-210.
[38] Mironovski L A. Function diagnosis of linear dynamic systems. Automn Remote Control, 1979,40:1198-1205.
[39] Mironovski L A. Function diagnosis of linear dynamic systems-a survey, Automn Remote Control,1980,41:1122-1143.
[40] Frank P M. On-line fault detection in uncertain nonlinear systems using diagnostic observers: a survey. International Journal Systems Science,1994,25(12):2129-2154.
[41] Isermann R.Process fault detection based on modeling and estimation methods-a survey. Auto-matic,1984,20(44):387-404.
[42] Ray A,Luck R.An introduction to sensor signal validation in redundant measurement systems. IEEE Control Systems Magine, 1991,11(2):44-49.
[43] Kinnaert M.Design of Redundancy Relations for Failure Detection and Isolation by Constrained Optimization. Internatinal Journal of Control ,1996 ,63 (3) :609-622.
[44] Medvedev A. Fault detection and isolation by a continuous parity space method, Automati -ca .1995;31(7):1039-1044.
[45] Medvedev A.Fault detection and isolation by functional continuous deadbeat observers. Int.J.Contro1,1996,54(3):425-439.
[46] Medvedev A.. State estimation and fault detection by a bank of continuous finite-memory filters Int.J.Control,1998,69(4):499-517.
[47] Wang H, Chang D S. A Practical Design for a Robust Fault Detection and Isolation System. International Jouranl of System Science,1997,28(3) :265-275.
[48] GERTLER J, KUNWER M M. Optimal residual decoupling for robust fault diagnosis. International Journal of Control,1995,61(2): 395-421.
[49] GERTLER J, MONAJEMYR. Generating directional residuals with dynamic parity relations. Automatica ,1995,31(4):627-635.
[50] Thomas Hofling and Rolf Isermann. Adaptive parity equations and advanced parameter estimation for fault detection and diagnosis.Proc.of IFAC World Congress,1996:55-60.
[51] Frank P M, Ding X. Survey of robust residual generation and evaluation methods in observer- based fault detection systems. Journal of Process Control 1997,7(6):403-424.
[52] Ding X,Guo L and Frank P M. A frequency domain approach to fault detection of uncertain dynamic systems.Proceedings of the 32nd IEEE CDC, 1993:1722-1727.
[53] Hou M, Patton R J. An LMI approach to H-/H∞ fault detection observers. Proceedings of IEE Conference on Control 96, 1996:305-310.
[54] Qui Z, Gertler J. Robust FDI systems and H∞ optimization. Proceedings of the 32nd IEEE CDC,1993:1710-1715.
[55] Sauter D, Rambeaux F, Hamelin F. Robust fault diagnosis in an H∞ setting. Proceedings of SAFEPROCESS ,1997:879-884.
[56] Patton R J and Hou M. A matrix pencil approach to fault detection and isolation observers.Proc.of IFAC World Congress,1996:229-234.
[57] Patton R J and Hou M. Design of fault detection and isolation observers. Automatica,1998;34(9):1135-1140.
[58] Edwards C,Spurgeon S K,Patton R J.Sliding model observers for fault detection and isolation. Automatica.2000,36(3):541-553.
[59] Zhong M Y, DingS X and Lam J.An LMI approach to design robust fault detection filter for uncertain LTI systems.Automatica, 2003,39(3): 543-550.
[60] Chen R H,Mingori D L, Speyer J L. Optimal Stochastic Fault Detection Filter. Automatica, 2003,39(3): 377-390.
[61] Yannis E. Faitakis and Jeffrey C. Residual generation and fault detection for discrete time systems using an H∞ technique.Int.J.Control,1996,64(1):155-174.
[62] Ding S X, Jeinsch T, Frank P M, Ding E L. A unified approach to the optimization of fault detection systems. International Journal of Adaptive Control and Signal Processing, 2000,14(7): 725-745.
[63] Frank P M. Handling modeling uncertainty in fault detection and isolation system. In: The International Conference IPMU, France, 2002,(3):1-5.
[64] Patton R J, Chen J. A review of parity space approaches to fault diagnosis.In:Proceedings of IFAC Fault Detection, Supervision and Safety for Technical Processes, Baden Baden, Germany, 1991:65-81.
[65] Gertler J. Fault Detection and Diagnosis in Engineering Systems. Marcel Dekker, New York, 1998.
[66] Basseville M, Nikiforov I V .Detection of abrupt changes:theory and application, information and system science. Prentice Hall, New York, 1993.
[67] Patton R J, Frank P M, Clark R. Fault diagnosis in dynamic systems.and Application, Herfordshire: Prentice Hall, 1989.
[68] Frank P M, Marcu T. Diagnosis strategies and systems: principles, fuzzy and neural approaches. In: Intelligent Systems and Interfaces, Kluwer, 2000.
[69] Seliger R,Frank P M.Robust residual evaluation by threshold selection and performance indexfor non-linear observer-based fault diagnosis. In: Proceedings of TOOLDIAG'93, Toulouse.1993:65-88.
[70] Frank P M, Ding S X, Marcu T. Model-Based Fault Diagnosis in Technical Processes. Transactions of the Institute of Measurement and Control, 2000, 22(1):57-101.
[71] Frank P M, Analytical and qualitative model-based fault diagnosis-a survey and some new results. European Journal of Control, 1996,2(1): 6-28.
[72] Chen J, Patton R J. Robust model-based fault diagnosis for dynamic systems.Kluwer AcademicPublishers, 1999.
[73] Gentler J. Diagnosis parametric faults: from parameter estimation to parity relation.In: America Control Conference, Seattle,USA, 1995:1615-1620.
[74] Khan A, Ceglarek D, Ni J. Sensor location optimization for fault diagnosis in multi-fixture assembly systems. ASME J. Manuf. Sci. Eng., 1998, 120(5): 781-792.
[75] Zhou D H, Frank P M. Fault diagnostics and fault tolerant control. IEEE Trans. On Aerospace and Electronic Systems ,1998, 34(2) :420-427.
[76] Hou M, Mueller P C. Fault detection and isolation observers. International Journal of Control, 1995,60(5):827-846.
[77] Chen J, Patton R J, Zhang H Y Design of unknown input observers and robust fault detection filters. Int. J. Control, 1996, 63(1): 85-105.
[78] Ding S X, Guo L. An approach to time domain optimization of observer based fault detection systems. International Journal of Control, 1998, 69(3): 419-442.
[79] Zhou K, Doyle J C, Glover K. Robust and optimal control. Prentice-Hall: Englewood Cliffs, NJ, 1995.
[80] Zhong M Y, Tang B Y, Ding S X. LMI approach to observer-based FD systems designing. Journal of Dong Hua University(Eng.Ed.), 2001,18(4): 41-44.