时滞系统的故障诊断方法研究
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摘要
针对应用中常见的时滞系统以及系统中最常见的一类传感器和执行器故障本文研究含测量时滞系统的故障诊断问题。首先综述了故障诊断和控制的国内外研究现状,介绍了时滞系统的故障诊断制成果。然后利用最优理论、对偶原理、线性矩阵不等式及状态观测等技术,提出了在系统中含有不时滞情况下的故障诊断和容错控制方法。本文的研究内容概括如下。
1.对带有故障的线性系统模型进行了描述,并对本文所研究的一类已知动态特性未知初始状态和初始时刻的故障信号用“外系统”的概念和状态空间模型进行了建模。将故障视为一个增广系统的状态,可以将故障诊断问题转换为状态观测问题。然后根据系统执行器故障和传感器故障特征值的不同情况,以及故障模型的两种不同情况,利用可观性的PBH特征向量判据给出了不同情况下相应的故障可诊断的判别条件。
2.针对含测量时滞的线性系统,通过线性变换将时滞系统转化为无时滞系统,然后将故障状态转化为增广系统的状态,构造了基于观测器的增广系统故障诊断器,利用Lyapunov稳定性理论和线性矩阵不等式技术,实现了系统的实时在线故障诊断。
3.研究了线性时滞系统的故障诊断方法。给出并证明了线性时滞系统发生此类故障时故障可诊断性的充分条件。提出了一种无时滞转换方法,将时滞系统转化为形式上不含时滞的系统,从而解决测量时滞系统的故障诊断问题。然后通过最优控制理论和对偶原理,提出了一种新的的最优故障诊断器的设计方法,使之能够满足一定的二次型性能指标。从而在实现在线故障诊断的同时使所设计的故障诊断器实现最优。
This dissertation studies the problems on fault diagnosis for systems with delayed mesurement to solve a class of usual actuator and/or sensor faults. At firstly, the history of the development on the fault diagnosis is reviewed. The latest research tendency and the main methods are summarized in the fault diagnosis domain, and the fault diagnosis approaches are introduced for time-delay systems. Then, by using optimal control theory, duality principle, linear matrix inequation, and state observer and so on technology, fault diagnosis approaches are proposed for systems with delayed mesurement. The major results of this dissertation are summarized as follows.
1. On a class of linear system with the fault model is described, and the model is established with the concept of "exosystem" and the state space expression for this class of faults with known dynamic characteristics and unknown initial time and initial state. We regard the fault states as the sub-states of an augmented system, the fault diagnosis problem can be converted to state observation. Then according to the different eigenvalues of system actuator failures and sensor failures, as well as two different situations fault model, according to the PBH eigenvector criteria of observability, we obtain the fault can be diagnosed criterion appropriate in different situations.
2. The problems of fault diagnosis for systems with delayed measurements are considered. A functional transformation is first presented, which transforms the system with delayed measurements into a system without delay formally. Then the fault states are transformed into the states of an augmented systerm, and design of the observer-based diagnoser is proposed. Finally, by using the Lyapunov stability theory and liner matrix inequality(LMI) technology, the real-time fault diagnosis is realized on-line.
3. Studies the problem of fault diagnosis for systems with delayed measurements when there exist sensor and actuator faults. The main contribution consists in the delay-free transformation approach for measurement delays and in the design of a new optimal fault diagnoser. Firstly, we regard the fault states as the sub-states of an augmented system, and a delay-free transformation is proposed to transform the system with delayed measurement vector into a delay-free one. Then a sufficient condition of the observability is proved for the transformed system. By using duality principle, the design problem of the optimal fault diagnoser is transformed into an equivalent optimal control problem for the duality system of the fault diagnosis error equation. Finally, the real-time fault diagnosis is realized by constructing a new reduced-order optimal fault diagnoser.
4. The conclusions are made.And the direction for the future study is indicated.
1.对带有故障的线性系统模型进行了描述,并对本文所研究的一类已知动态特性未知初始状态和初始时刻的故障信号用“外系统”的概念和状态空间模型进行了建模。将故障视为一个增广系统的状态,可以将故障诊断问题转换为状态观测问题。然后根据系统执行器故障和传感器故障特征值的不同情况,以及故障模型的两种不同情况,利用可观性的PBH特征向量判据给出了不同情况下相应的故障可诊断的判别条件。
2.针对含测量时滞的线性系统,通过线性变换将时滞系统转化为无时滞系统,然后将故障状态转化为增广系统的状态,构造了基于观测器的增广系统故障诊断器,利用Lyapunov稳定性理论和线性矩阵不等式技术,实现了系统的实时在线故障诊断。
3.研究了线性时滞系统的故障诊断方法。给出并证明了线性时滞系统发生此类故障时故障可诊断性的充分条件。提出了一种无时滞转换方法,将时滞系统转化为形式上不含时滞的系统,从而解决测量时滞系统的故障诊断问题。然后通过最优控制理论和对偶原理,提出了一种新的的最优故障诊断器的设计方法,使之能够满足一定的二次型性能指标。从而在实现在线故障诊断的同时使所设计的故障诊断器实现最优。
This dissertation studies the problems on fault diagnosis for systems with delayed mesurement to solve a class of usual actuator and/or sensor faults. At firstly, the history of the development on the fault diagnosis is reviewed. The latest research tendency and the main methods are summarized in the fault diagnosis domain, and the fault diagnosis approaches are introduced for time-delay systems. Then, by using optimal control theory, duality principle, linear matrix inequation, and state observer and so on technology, fault diagnosis approaches are proposed for systems with delayed mesurement. The major results of this dissertation are summarized as follows.
1. On a class of linear system with the fault model is described, and the model is established with the concept of "exosystem" and the state space expression for this class of faults with known dynamic characteristics and unknown initial time and initial state. We regard the fault states as the sub-states of an augmented system, the fault diagnosis problem can be converted to state observation. Then according to the different eigenvalues of system actuator failures and sensor failures, as well as two different situations fault model, according to the PBH eigenvector criteria of observability, we obtain the fault can be diagnosed criterion appropriate in different situations.
2. The problems of fault diagnosis for systems with delayed measurements are considered. A functional transformation is first presented, which transforms the system with delayed measurements into a system without delay formally. Then the fault states are transformed into the states of an augmented systerm, and design of the observer-based diagnoser is proposed. Finally, by using the Lyapunov stability theory and liner matrix inequality(LMI) technology, the real-time fault diagnosis is realized on-line.
3. Studies the problem of fault diagnosis for systems with delayed measurements when there exist sensor and actuator faults. The main contribution consists in the delay-free transformation approach for measurement delays and in the design of a new optimal fault diagnoser. Firstly, we regard the fault states as the sub-states of an augmented system, and a delay-free transformation is proposed to transform the system with delayed measurement vector into a delay-free one. Then a sufficient condition of the observability is proved for the transformed system. By using duality principle, the design problem of the optimal fault diagnoser is transformed into an equivalent optimal control problem for the duality system of the fault diagnosis error equation. Finally, the real-time fault diagnosis is realized by constructing a new reduced-order optimal fault diagnoser.
4. The conclusions are made.And the direction for the future study is indicated.
引文
[1]孟凡芹,煤矿胶带输送机运输线计算机监控系统关键技术研究.[博士学位论文].徐州：中国矿业大学,2002.
[2]N. Sawalhi, R. B. Randall, and H. Endo, The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis, Mechanical Systems and Signal Processing,2007 21:2616-2633.
[3]Randall R. B., Detection and diagnosis of incipient bearing failure in helicopter gearboxes, Engineering Failure Analysis,2004,11:177-190.
[4]Daviu J. Antonino, Riera M.Guasp, Roger J. Folch, Martinez F. Gimenez, and Peris A., Application and optimization of the discrete wavelet transform for the detection of broken rotor bars in induction machines, Applied and Computational Harmonic Analysis,2006, 21:268-279.
[5]Calis H. and Cakir A., Rotor bar fault diagnosis in three phase induction motors by monitoring fluctuations of motor current zero crossing instants, Electric Power Systems Research,2007,77:385-392.
[6]Isermann R. and Balle P., Trends in the application of model-based fault detection and diagnosis of technical processes, Control Engineering Practice,1997,5:709-719.
[7]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,2000.
[8]王红茹,动态系统的鲁棒故障检测与分离方法研究.[博士学位论文],哈尔滨：哈尔滨工业大学,2006.
[9]Beard R. V., Failure accommodation in linear systems through self-reorganization. Report MVT-71-1, Man Vehicle Lab, MIT, Cambridge, Massachusetts,1971.
[10]Mehra R. K. and Peschon J., Innovations approach to fault detection and diagnosis in dynamic systems,1971,7:637-640.
[11]Willsky A. S., Survey of design methods for failure detection in dynamic systems, Automatica,1976,12:601-611.
[12]DeckerJ. C. t, Desai M. N., Deyst J. J., and Willsky A. S., F-8 DFBW sensor failure identification using analytic redundancy, IEEE Transactions on Automatic Control,1977, 22:795-804.
[13]Himmelblau D. M., Fault detection and diagnosis in chemical and Petrochemical Process. Amsterdam, Elsevier Press,1978.
[14]Kitamura M. and UpadhyayaB. R., Improved time series modeling approach for diagnosis and surveillance of reactors, Knoxville, TN, Belg,1980.
[15]Isermann R., Fault diagnosis of machines via parameter estimation and knowledge processing--tutorial paper, Automatica,1993,29:815-835.
[16]Frank P. M., Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy:A survey and some new results, Automatica,1990,26:459-474.
[17]叶银忠,潘日芳,蒋慰孙,动态系统的故障检测与诊断方法,信息与控制.1985,14：27-34.
[18]周东华,孙优贤,控制系统的故障检测与诊断技术.北京：清华大学出版社,1994.
[19]张育林,李东旭,动态系统故障诊断理论与应用.长沙：国防科技大学出版社,1997：
[20]Rodriguez P. V. Jover and Arkkio A., Detection of stator winding fault in induction motor using fuzzy logic, Applied Soft Computing Journal,2008,8:1112-1120.
[21]El-Farra N. H. and Giridhar A., Detection and management of actuator faults in controlled particulate processes using population balance models, Chemical Engineering Science,. 2008,63:1185-1204.
[22]Li Z, He Z, Zi Y, and Jiang H, Rotating machinery fault diagnosis using signal-adapted lifting scheme, Mechanical Systems and Signal Processing,2008,22:542-556.
[23]Zhang Y, Wu J, Huang M, and Chen Q, Liquid-propellant rocket engine health-monitoring techniques, Journal of Propulsion and Power,1998,14:657-663.
[24]Ding X and Frank P. M., An approach to robust residual generation and evaluation, Brighton, Engl,1992.
[25]胡昌华,许化龙,控制系统故障诊断与容错控制的分析和设计.北京：国防工业出版社,2000.
[26]Li W, Shah S. L. and Xiao D, Kalman filters in non-uniformly sampled multirate systems: For FDI and beyond, Automatica, vol.44, pp.199-208,2008.
[27]Yu D, Fault diagnosis for a hydraulic drive system using a parameter-estimation method, Control Engineering Practice, vol.5, pp.1283-1291,1997.
[28]Hou M and Muller P. C., Design of robust observers for fault isolation, Baden-Baden, Ger, 1992.
[29]Chen J., Patton R. J., and Zhang H Y, Design of unknown input observers and robust fault detection filters, International Journal of Control,1996,63:85-105.
[30]Hwang D.-S., Chang S.-K., and Hsu P.-L., Practical design for a robust fault detection and isolation system, International Journal of Systems Science,1997,28:265-275.
[31]Park T. G. and Lee K. S., Process fault isolation for linear systems with unknown inputs, IEE Proceedings:Control Theory and Applications,2004,151:720-726.
[32]Gertler J., Diagnosing parametric faults:from parameter estimation to parity relations, Seattle, WA, USA,1995.
[33]Jiang B, Staroswiecki M., and Cocquempot V., Fault identification for a class of time-delay systems, Anchorage, AK, United States,2002.
[34]Edwards C., Spurgeon S. K., and Patton R. J., Sliding mode observers for fault detection and isolation, Automatica,2000,36:541-553.
[35]Tan C P and Edwards C., Sliding mode observers for robust detection and reconstruction of actuator and sensor faults, International Journal of Robust and Nonlinear Control,2003, 13:443-463.
[36]Larson E. C., Parker B. E. Jr, and Clark B. R., Model-based sensor and actuator fault detection and isolation, Anchorage, AK, United States,2002.
[37]Gao Z and Ding S X, Fault estimation and fault-tolerant control for descriptor systems via proportional, multiple-integral and derivative observer design, IET Control Theory and Applications,2007, (1):1208-1218.
[38]Caliskan F. and Hajiyev C. M., Aircraft sensor fault diagnosis based on Kalman filter innovation sequence, Proceedings of the IEEE Conference on Decision and Control,1998, 2:1313-1314.
[39]Keller J. Y., Fault isolation filter design for linear stochastic systems, Automatica,1999, 35:1701-1706.
[40]Shafai B., Pi C. T., and Nork S., Simultaneous disturbance attenuation and fault detection using proportional integral observers, Anchorage, AK, United States,2002.
[41]Persis C. De, Necessary condition and backstepping observer for nonlinear fault detection, Phoenix, AZ, USA,1999.
[42]F. P. M., Analytical and Qualitative Model-Based Fault Diagnosis-a Survey and Some New Results, European Journal of Control,1996,2:6-23.
[43]张萍,王桂增,周东华,动态系统的故障诊断方法,控制理论与应用,2000,17(3)：153-158.
[44]王德军,故障诊断与容错控制方法研究,[博士学位论文].长春：吉林大学,2004.
[45]Isermann R. and Balle P., Trends in the application of model-based fault detection and diagnosis of technical processes, Control Engineering Practice,1997,5:709-719.
[46]Patton R. J. and Chen J., Review of parity space approaches to fault diagnosis for aerospace systems, Journal of Guidance, Control, and Dynamics,1994,17:278-285.
[47]Patton R. J. and Chen J., Review of parity space approaches to fault diagnosis, Baden-Baden, Ger,1992.
[48]Ye H, Zhang P, Ding S X, and Wang G Z, A time-frequency domain fault detection approach based on parity relation and wavelet transform, Sydney, NSW,2000.
[49]Ye H, Wang G., and Ding S X, A New Parity Space Approach for Fault Detection Based on Stationary Wavelet Transform, IEEE Transactions on Automatic Control,2004,49: 281-287.
[50]Gertler J., Fault detection and isolation using parity relations, Control Engineering Practice,1997,5:653-661.
[51]Magni J.-F. and Mouyou P., On residual generation by observer and parity space approaches, IEEE Transactions on Automatic Control,1994,39:441-447.
[52]P. F. E. A. Garcia, On the Relationship between Observer and Parameter Identification Based Approaches to Fault Diagnosis, presented at Proc. of IFAC World Congress, San Francisco, USA,1996.
[53]Ye H, Wang G. Z, and Ding S X, A New Parity Space Approach for Fault Detection Based on Stationary Wavelet Transform, IEEE Transactions on Automatic Control,2004,49: 281-287.
[54]Kar C. and Mohanty A. R., Vibration and current transient monitoring for gearbox fault detection using multiresolution Fourier transform, Journal of Sound and Vibration,2008, 311:109-132.
[55]胡寿松,周川,王源,基于小波神经网络的组合故障模式识别,自动化学报,2002,28：540-543.
[56]Zhang J Q and Yan Y, A wavelet-based approach to abrupt fault detection and diagnosis of sensors, IEEE Transactions on Instrumentation and Measurement,2001,50:1389-1396.
[57]Frank P. M. and Ding X, Survey of robust residual generation and evaluation methods in observer-based fault detection systems, Journal of Process Control,1997,7:403-424.
[58]Frank P. M., Analytical and qualitative model-based fault diagnosis-a survey and some new results, European Journal of Control,1996,2:6-23.
[59]Quteishat A. and Lim C. P., A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification, Applied Soft Computing Journal,2008,8:985-995.
[60]He Y and Feng L, Diesel fuel injection system faults diagnosis based on fuzzy injection pressure pattern recognition, Hangzhou, China,2004.
[61]Heiming B. and Lunze J., Parallel process diagnosis based on qualitative models, International Journal of Control,2000,73:1061-1077.
[62]Azorin J. M., Reinoso O., Aracil R., and Ferre M., Control of teleoperators with communication time delay through state convergence, Journal of Robotic Systems,2004, 21:167-182.
[63]Yue D and Han Q-L, Delayed feedback control of uncertain systems with time-varying input delay, Automatica,2005,41:233-240.
[64]Han Q-L, On stability of linear neutral systems with mixed time delays:A discretized Lyapunov functional approach, Automatica,2005,41:1209-1218.
[65]Kazantzis N. and Wright R. A., Nonlinear observer design in the presence of delayed output measurements, Systems and Control Letters,2005,54:877-886.
[66]Yue D, Robust stabilization of uncertain systems with unknown input delay, Automatica, 2004,40:331-336.
[67]Tang G. Y, Li C, and Sun L, Optimal tracking control for large-scale interconnected systems with time-delays, Computers and Mathematics with Applications,2007, 53:80-88.
[68]Bai L, Tian Z, and Shi S, Robust fault detection for a class of nonlinear time-delay systems, Journal of the Franklin Institute,2007,344:873-888.
[69]Zhong M-Y, Tang B-Y, Ding S X, and Huang X-Y, LMI approach to design state-delayed fault detection system, Kongzhi yu Juece/Control and Decision,2002,17:15-18.
[70]Yang H and M. Saif, Observer design and fault diagnosis for state-retarded dynamical systems, Automatica,1998,34:217-227.
[71]A. Bulsari, A. Medvedev, and H. Saxen, Sensor fault detection using state vector estimator and feed-forward neural networks applied to a simulated biochemical process, Acta Polytechnica Scandinavica, Chemical Technology and Metallurgy Series,1991,199:1-20.
[72]Zheng Y, Fang H-J, Wang H, and Li, L, Observer-based FDI design of networked control system with output transfer delay, Kongzhi Lilun Yu Yinyong/Control Theory and Applications,2003,20:653.
[73]Mao Z H and Jiang B, Fault estimation and accommodation for networked control systems with transfer delay, Zidonghua Xuebao/Acta Automatica Sinica,2007, 33:738-743.
[74]Ma C F, Zhong M Y, and He N, H infinity approach to fault detection filter for linear time-delay systems, Kongzhi yu Juece/Control and Decision,2006,21:550-554.
[75]Chen W and M. Saif, Observer-based strategies for actuator fault detection, isolation and estimation for certain class of uncertain nonlinear systems, IET Control Theory and Applications,2007,1:1672-1680.
[76]Zhong M Y, Ye H, Sun T J., and Wang G Z, Iterative LMI approach to RFDF for linear system with time-varying delays, Asian Journal of Control,2006,8:86-90.
[77]Zhong M, Ding S X, J. Lam, and Zhang C, Fault Detection Filter Design for LTI System with Time Delays, Maui, HI, United States,2003.
[78]邓晓刚,田学民.基于鲁棒规范变量分析的故障诊断方法[J].控制与决策,2008,23(4)：415-419.
[79]Li J, Tang G Y. Sensor fault detection and isolation based on reduced order observer and fault tolerant control [J]. Dynamics Continuous Discrete and Inputse System-Series A, 2006,13(2S):728-732.
[80]唐功友,李娟,张勇.时滞系统基于对偶原理的故障诊断[J].控制理论与应用,2008,25(5)：1-4.
[81]Li X B, Zhou K M. A time domain approach to robust fault detection of linear time-varying systems[J]. Automatica,2009,45 (4):94-102.
[82]李娟,叶若红,唐功友.含控制时滞系统的实时故障诊断和最优容错控制[J].控制与决策,2008,23(4)：339-444.
[83]Seema Manuja, Shankar Narasimhan, Sachin C. Patwardhan. Unknown input modeling and robust fault diagnosis using black box observers[J]. Journal of Process Control,2009, 19:25-37.
[84]Zhong M Y, Ye T, Chen G Y, et al. An LMI approach to RFDF for uncertainty linear systems with nonlinear perturbations[J]. Acta Automatica Sinica,2005,31(2):297-300.
[85]颜秉勇,田作华,施颂椒等.基于故障跟踪估计器的非线性时滞系统故障诊断[J].控 制与决策,2009,24(1)：133-136.
[86]Dunia R, Qin S J. Subspace approach to multidimensional fault identification and reconstruction. American Institute Chemical Engineers[J],2004,44(8):1813-1831.
[87]Wen C, Saif M. Fault detection and accommodation in nonlinear time-delay systems [C]. Proc of the ACC Conf. Colorado,2003:4255-4260.
[88]李娟,时滞系统基于观测器的故障诊断,[博士学位论文].青岛：中国海洋大学,2008.
[2]N. Sawalhi, R. B. Randall, and H. Endo, The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis, Mechanical Systems and Signal Processing,2007 21:2616-2633.
[3]Randall R. B., Detection and diagnosis of incipient bearing failure in helicopter gearboxes, Engineering Failure Analysis,2004,11:177-190.
[4]Daviu J. Antonino, Riera M.Guasp, Roger J. Folch, Martinez F. Gimenez, and Peris A., Application and optimization of the discrete wavelet transform for the detection of broken rotor bars in induction machines, Applied and Computational Harmonic Analysis,2006, 21:268-279.
[5]Calis H. and Cakir A., Rotor bar fault diagnosis in three phase induction motors by monitoring fluctuations of motor current zero crossing instants, Electric Power Systems Research,2007,77:385-392.
[6]Isermann R. and Balle P., Trends in the application of model-based fault detection and diagnosis of technical processes, Control Engineering Practice,1997,5:709-719.
[7]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,2000.
[8]王红茹,动态系统的鲁棒故障检测与分离方法研究.[博士学位论文],哈尔滨：哈尔滨工业大学,2006.
[9]Beard R. V., Failure accommodation in linear systems through self-reorganization. Report MVT-71-1, Man Vehicle Lab, MIT, Cambridge, Massachusetts,1971.
[10]Mehra R. K. and Peschon J., Innovations approach to fault detection and diagnosis in dynamic systems,1971,7:637-640.
[11]Willsky A. S., Survey of design methods for failure detection in dynamic systems, Automatica,1976,12:601-611.
[12]DeckerJ. C. t, Desai M. N., Deyst J. J., and Willsky A. S., F-8 DFBW sensor failure identification using analytic redundancy, IEEE Transactions on Automatic Control,1977, 22:795-804.
[13]Himmelblau D. M., Fault detection and diagnosis in chemical and Petrochemical Process. Amsterdam, Elsevier Press,1978.
[14]Kitamura M. and UpadhyayaB. R., Improved time series modeling approach for diagnosis and surveillance of reactors, Knoxville, TN, Belg,1980.
[15]Isermann R., Fault diagnosis of machines via parameter estimation and knowledge processing--tutorial paper, Automatica,1993,29:815-835.
[16]Frank P. M., Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy:A survey and some new results, Automatica,1990,26:459-474.
[17]叶银忠,潘日芳,蒋慰孙,动态系统的故障检测与诊断方法,信息与控制.1985,14：27-34.
[18]周东华,孙优贤,控制系统的故障检测与诊断技术.北京：清华大学出版社,1994.
[19]张育林,李东旭,动态系统故障诊断理论与应用.长沙：国防科技大学出版社,1997：
[20]Rodriguez P. V. Jover and Arkkio A., Detection of stator winding fault in induction motor using fuzzy logic, Applied Soft Computing Journal,2008,8:1112-1120.
[21]El-Farra N. H. and Giridhar A., Detection and management of actuator faults in controlled particulate processes using population balance models, Chemical Engineering Science,. 2008,63:1185-1204.
[22]Li Z, He Z, Zi Y, and Jiang H, Rotating machinery fault diagnosis using signal-adapted lifting scheme, Mechanical Systems and Signal Processing,2008,22:542-556.
[23]Zhang Y, Wu J, Huang M, and Chen Q, Liquid-propellant rocket engine health-monitoring techniques, Journal of Propulsion and Power,1998,14:657-663.
[24]Ding X and Frank P. M., An approach to robust residual generation and evaluation, Brighton, Engl,1992.
[25]胡昌华,许化龙,控制系统故障诊断与容错控制的分析和设计.北京：国防工业出版社,2000.
[26]Li W, Shah S. L. and Xiao D, Kalman filters in non-uniformly sampled multirate systems: For FDI and beyond, Automatica, vol.44, pp.199-208,2008.
[27]Yu D, Fault diagnosis for a hydraulic drive system using a parameter-estimation method, Control Engineering Practice, vol.5, pp.1283-1291,1997.
[28]Hou M and Muller P. C., Design of robust observers for fault isolation, Baden-Baden, Ger, 1992.
[29]Chen J., Patton R. J., and Zhang H Y, Design of unknown input observers and robust fault detection filters, International Journal of Control,1996,63:85-105.
[30]Hwang D.-S., Chang S.-K., and Hsu P.-L., Practical design for a robust fault detection and isolation system, International Journal of Systems Science,1997,28:265-275.
[31]Park T. G. and Lee K. S., Process fault isolation for linear systems with unknown inputs, IEE Proceedings:Control Theory and Applications,2004,151:720-726.
[32]Gertler J., Diagnosing parametric faults:from parameter estimation to parity relations, Seattle, WA, USA,1995.
[33]Jiang B, Staroswiecki M., and Cocquempot V., Fault identification for a class of time-delay systems, Anchorage, AK, United States,2002.
[34]Edwards C., Spurgeon S. K., and Patton R. J., Sliding mode observers for fault detection and isolation, Automatica,2000,36:541-553.
[35]Tan C P and Edwards C., Sliding mode observers for robust detection and reconstruction of actuator and sensor faults, International Journal of Robust and Nonlinear Control,2003, 13:443-463.
[36]Larson E. C., Parker B. E. Jr, and Clark B. R., Model-based sensor and actuator fault detection and isolation, Anchorage, AK, United States,2002.
[37]Gao Z and Ding S X, Fault estimation and fault-tolerant control for descriptor systems via proportional, multiple-integral and derivative observer design, IET Control Theory and Applications,2007, (1):1208-1218.
[38]Caliskan F. and Hajiyev C. M., Aircraft sensor fault diagnosis based on Kalman filter innovation sequence, Proceedings of the IEEE Conference on Decision and Control,1998, 2:1313-1314.
[39]Keller J. Y., Fault isolation filter design for linear stochastic systems, Automatica,1999, 35:1701-1706.
[40]Shafai B., Pi C. T., and Nork S., Simultaneous disturbance attenuation and fault detection using proportional integral observers, Anchorage, AK, United States,2002.
[41]Persis C. De, Necessary condition and backstepping observer for nonlinear fault detection, Phoenix, AZ, USA,1999.
[42]F. P. M., Analytical and Qualitative Model-Based Fault Diagnosis-a Survey and Some New Results, European Journal of Control,1996,2:6-23.
[43]张萍,王桂增,周东华,动态系统的故障诊断方法,控制理论与应用,2000,17(3)：153-158.
[44]王德军,故障诊断与容错控制方法研究,[博士学位论文].长春：吉林大学,2004.
[45]Isermann R. and Balle P., Trends in the application of model-based fault detection and diagnosis of technical processes, Control Engineering Practice,1997,5:709-719.
[46]Patton R. J. and Chen J., Review of parity space approaches to fault diagnosis for aerospace systems, Journal of Guidance, Control, and Dynamics,1994,17:278-285.
[47]Patton R. J. and Chen J., Review of parity space approaches to fault diagnosis, Baden-Baden, Ger,1992.
[48]Ye H, Zhang P, Ding S X, and Wang G Z, A time-frequency domain fault detection approach based on parity relation and wavelet transform, Sydney, NSW,2000.
[49]Ye H, Wang G., and Ding S X, A New Parity Space Approach for Fault Detection Based on Stationary Wavelet Transform, IEEE Transactions on Automatic Control,2004,49: 281-287.
[50]Gertler J., Fault detection and isolation using parity relations, Control Engineering Practice,1997,5:653-661.
[51]Magni J.-F. and Mouyou P., On residual generation by observer and parity space approaches, IEEE Transactions on Automatic Control,1994,39:441-447.
[52]P. F. E. A. Garcia, On the Relationship between Observer and Parameter Identification Based Approaches to Fault Diagnosis, presented at Proc. of IFAC World Congress, San Francisco, USA,1996.
[53]Ye H, Wang G. Z, and Ding S X, A New Parity Space Approach for Fault Detection Based on Stationary Wavelet Transform, IEEE Transactions on Automatic Control,2004,49: 281-287.
[54]Kar C. and Mohanty A. R., Vibration and current transient monitoring for gearbox fault detection using multiresolution Fourier transform, Journal of Sound and Vibration,2008, 311:109-132.
[55]胡寿松,周川,王源,基于小波神经网络的组合故障模式识别,自动化学报,2002,28：540-543.
[56]Zhang J Q and Yan Y, A wavelet-based approach to abrupt fault detection and diagnosis of sensors, IEEE Transactions on Instrumentation and Measurement,2001,50:1389-1396.
[57]Frank P. M. and Ding X, Survey of robust residual generation and evaluation methods in observer-based fault detection systems, Journal of Process Control,1997,7:403-424.
[58]Frank P. M., Analytical and qualitative model-based fault diagnosis-a survey and some new results, European Journal of Control,1996,2:6-23.
[59]Quteishat A. and Lim C. P., A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification, Applied Soft Computing Journal,2008,8:985-995.
[60]He Y and Feng L, Diesel fuel injection system faults diagnosis based on fuzzy injection pressure pattern recognition, Hangzhou, China,2004.
[61]Heiming B. and Lunze J., Parallel process diagnosis based on qualitative models, International Journal of Control,2000,73:1061-1077.
[62]Azorin J. M., Reinoso O., Aracil R., and Ferre M., Control of teleoperators with communication time delay through state convergence, Journal of Robotic Systems,2004, 21:167-182.
[63]Yue D and Han Q-L, Delayed feedback control of uncertain systems with time-varying input delay, Automatica,2005,41:233-240.
[64]Han Q-L, On stability of linear neutral systems with mixed time delays:A discretized Lyapunov functional approach, Automatica,2005,41:1209-1218.
[65]Kazantzis N. and Wright R. A., Nonlinear observer design in the presence of delayed output measurements, Systems and Control Letters,2005,54:877-886.
[66]Yue D, Robust stabilization of uncertain systems with unknown input delay, Automatica, 2004,40:331-336.
[67]Tang G. Y, Li C, and Sun L, Optimal tracking control for large-scale interconnected systems with time-delays, Computers and Mathematics with Applications,2007, 53:80-88.
[68]Bai L, Tian Z, and Shi S, Robust fault detection for a class of nonlinear time-delay systems, Journal of the Franklin Institute,2007,344:873-888.
[69]Zhong M-Y, Tang B-Y, Ding S X, and Huang X-Y, LMI approach to design state-delayed fault detection system, Kongzhi yu Juece/Control and Decision,2002,17:15-18.
[70]Yang H and M. Saif, Observer design and fault diagnosis for state-retarded dynamical systems, Automatica,1998,34:217-227.
[71]A. Bulsari, A. Medvedev, and H. Saxen, Sensor fault detection using state vector estimator and feed-forward neural networks applied to a simulated biochemical process, Acta Polytechnica Scandinavica, Chemical Technology and Metallurgy Series,1991,199:1-20.
[72]Zheng Y, Fang H-J, Wang H, and Li, L, Observer-based FDI design of networked control system with output transfer delay, Kongzhi Lilun Yu Yinyong/Control Theory and Applications,2003,20:653.
[73]Mao Z H and Jiang B, Fault estimation and accommodation for networked control systems with transfer delay, Zidonghua Xuebao/Acta Automatica Sinica,2007, 33:738-743.
[74]Ma C F, Zhong M Y, and He N, H infinity approach to fault detection filter for linear time-delay systems, Kongzhi yu Juece/Control and Decision,2006,21:550-554.
[75]Chen W and M. Saif, Observer-based strategies for actuator fault detection, isolation and estimation for certain class of uncertain nonlinear systems, IET Control Theory and Applications,2007,1:1672-1680.
[76]Zhong M Y, Ye H, Sun T J., and Wang G Z, Iterative LMI approach to RFDF for linear system with time-varying delays, Asian Journal of Control,2006,8:86-90.
[77]Zhong M, Ding S X, J. Lam, and Zhang C, Fault Detection Filter Design for LTI System with Time Delays, Maui, HI, United States,2003.
[78]邓晓刚,田学民.基于鲁棒规范变量分析的故障诊断方法[J].控制与决策,2008,23(4)：415-419.
[79]Li J, Tang G Y. Sensor fault detection and isolation based on reduced order observer and fault tolerant control [J]. Dynamics Continuous Discrete and Inputse System-Series A, 2006,13(2S):728-732.
[80]唐功友,李娟,张勇.时滞系统基于对偶原理的故障诊断[J].控制理论与应用,2008,25(5)：1-4.
[81]Li X B, Zhou K M. A time domain approach to robust fault detection of linear time-varying systems[J]. Automatica,2009,45 (4):94-102.
[82]李娟,叶若红,唐功友.含控制时滞系统的实时故障诊断和最优容错控制[J].控制与决策,2008,23(4)：339-444.
[83]Seema Manuja, Shankar Narasimhan, Sachin C. Patwardhan. Unknown input modeling and robust fault diagnosis using black box observers[J]. Journal of Process Control,2009, 19:25-37.
[84]Zhong M Y, Ye T, Chen G Y, et al. An LMI approach to RFDF for uncertainty linear systems with nonlinear perturbations[J]. Acta Automatica Sinica,2005,31(2):297-300.
[85]颜秉勇,田作华,施颂椒等.基于故障跟踪估计器的非线性时滞系统故障诊断[J].控 制与决策,2009,24(1)：133-136.
[86]Dunia R, Qin S J. Subspace approach to multidimensional fault identification and reconstruction. American Institute Chemical Engineers[J],2004,44(8):1813-1831.
[87]Wen C, Saif M. Fault detection and accommodation in nonlinear time-delay systems [C]. Proc of the ACC Conf. Colorado,2003:4255-4260.
[88]李娟,时滞系统基于观测器的故障诊断,[博士学位论文].青岛：中国海洋大学,2008.