控制系统故障检测与诊断方法研究
摘要
随着现代控制系统的规模和复杂度的不断增加,现代工业过程对系统安全性、可靠性的需求也日益提高。故障诊断技术可为复杂系统的安全性和可靠性提供保障,因此其研究具有重要的理论意义和广泛的应用价值。
本文在第一章介绍了故障检测与诊断技术的研究背景和发展现状。介绍了现有的一些故障检测与诊断方法,讨论了这些方法的基本思想和特点。在第二章中介绍了线性矩阵不等式、Lyapunov稳定性及控制器设计的相关基础知识。第三章和第四章是本文的主要研究工作,也是主要创新之处,其主要内容如下：
首先,针对一类简单的线性非时变系统,提出了传感器和执行器的故障诊断方法。利用线性系统理论和矩阵理论的相关知识,对系统可能的故障情况作出详细的分析；通过系统的实际输出值和理论输出值作比较的方法来确定系统的故障原因。并且通过数值算例验证了该方法的有效性。
其次,研究基于观测器的故障检测与诊断方法。介绍了传感器、执行器和状态的故障模型；针对具有精确数学模型的线性非时变系统,分别给出了单输出系统和多输出系统的故障检测观测器的设计方法；提出了基于观测器的故障诊断方法。最后通过数值算例,说明了该方法的有效性。
最后,对全文所作的工作进行了总结,并展望了故障诊断技术进一步的研究方向。
Due to modem control systems are tended to be huger and more complex, the demand for high reliability and safety in modem industry increase rapidly. Fault diagnosis technology provides security for the reliability and safety of complex system. Hence, research on fault diagnosis is of important theoretic sense and wide applications.
In Chapter 1 we introduce the research background and the latest development of fault detection and diagnosis technology. We introduce some existing fault detection and diagnosis methods, and discuss the basic idea and features of these methods; in Chapter 2 we introduce the relevant basic knowledge of linear matrix inequality, the Lyapunov stability and the controller design; Chapter 3 and Chapter 4 are the main research work of this paper and also are where the innovation lies, they can be summarized as follows:
Firstly, to a kind of simple linear time-invariant system, a new fault diagnosis method based on sensors and actuators are proposed in the thesis. By using the relevant knowledge of linear system theory and matrix theory, the system possible fault cases are made detailed analysis; compare the actual output value with the theory output value of system, we determine to the system fault causes. And the approaches discussed in this chapter are demonstrated to be affective through numerical examples.
Secondly, a new fault detection and diagnosis method based on observer are proposed in the thesis. We introduce fault model of sensors, actuators and state; to linear time-invariant system with accurate mathematical model, fault detection observer of single output system and multiple output system are proposed respectively. A new fault diagnosis method based on observer is proposed. Finally, the methods discussed in this chapter are demonstrated to be affective through numerical examples.
Finally, the tasks in the paper are summarized, and next research direction of fault diagnosis is pointed out.
本文在第一章介绍了故障检测与诊断技术的研究背景和发展现状。介绍了现有的一些故障检测与诊断方法,讨论了这些方法的基本思想和特点。在第二章中介绍了线性矩阵不等式、Lyapunov稳定性及控制器设计的相关基础知识。第三章和第四章是本文的主要研究工作,也是主要创新之处,其主要内容如下：
首先,针对一类简单的线性非时变系统,提出了传感器和执行器的故障诊断方法。利用线性系统理论和矩阵理论的相关知识,对系统可能的故障情况作出详细的分析；通过系统的实际输出值和理论输出值作比较的方法来确定系统的故障原因。并且通过数值算例验证了该方法的有效性。
其次,研究基于观测器的故障检测与诊断方法。介绍了传感器、执行器和状态的故障模型；针对具有精确数学模型的线性非时变系统,分别给出了单输出系统和多输出系统的故障检测观测器的设计方法；提出了基于观测器的故障诊断方法。最后通过数值算例,说明了该方法的有效性。
最后,对全文所作的工作进行了总结,并展望了故障诊断技术进一步的研究方向。
Due to modem control systems are tended to be huger and more complex, the demand for high reliability and safety in modem industry increase rapidly. Fault diagnosis technology provides security for the reliability and safety of complex system. Hence, research on fault diagnosis is of important theoretic sense and wide applications.
In Chapter 1 we introduce the research background and the latest development of fault detection and diagnosis technology. We introduce some existing fault detection and diagnosis methods, and discuss the basic idea and features of these methods; in Chapter 2 we introduce the relevant basic knowledge of linear matrix inequality, the Lyapunov stability and the controller design; Chapter 3 and Chapter 4 are the main research work of this paper and also are where the innovation lies, they can be summarized as follows:
Firstly, to a kind of simple linear time-invariant system, a new fault diagnosis method based on sensors and actuators are proposed in the thesis. By using the relevant knowledge of linear system theory and matrix theory, the system possible fault cases are made detailed analysis; compare the actual output value with the theory output value of system, we determine to the system fault causes. And the approaches discussed in this chapter are demonstrated to be affective through numerical examples.
Secondly, a new fault detection and diagnosis method based on observer are proposed in the thesis. We introduce fault model of sensors, actuators and state; to linear time-invariant system with accurate mathematical model, fault detection observer of single output system and multiple output system are proposed respectively. A new fault diagnosis method based on observer is proposed. Finally, the methods discussed in this chapter are demonstrated to be affective through numerical examples.
Finally, the tasks in the paper are summarized, and next research direction of fault diagnosis is pointed out.
引文
[1]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,1994
[2]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
[3]蒋浩天.工业系统的故障检测与诊断.北京：机械工业出版,2003
[4]周鸣岐,徐军.控制系统故障诊断.计算机自动测量与控制,2000,8(3)：5-8页
[5]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页
[6]侯颖,孙秀霞,甘业君.控制系统故障诊断技术的应用与发展.中国设备工程,2004,3：46-49页
[7]任伟建,赵永玲,杨克远,董宏丽.控制系统故障诊断的方法.大庆石油学院学报,2003,27(3)：44-47页
[8]K. Watanebe, D.M. Himmelblau. Insuument Fault Detection in Systems with Uncertainties. International Journal of System Science,1982,13:137-158P
[9]J. Chen, H.Y. Zhang. Robust Detection of Faulty Actuators via Unknown Input Observers.Intemational Journal of System Science,1991,22:1829-1839P
[10]徐似春,萧德云.一种新的基于参数估计的故障诊断方法.控制理论与应用,2001,18(4)：493-497页
[11]周川,胡寿松.基于神经网络的非线性观测器及在线故障检测.数据采集与处理,1998,13(3)：206-209页
[12]胡寿松,王源.基于支持向量机的非线性系统故障诊断.控制与决策,2001,16(5)：617-620页
[13]王占山,李平,任正云,李奇安.非线性系统的故障诊断技术.自动化与仪器仪表,2001,5：8-10页
[14]闻新,张洪钺,周露.控制系统的故障诊断和容错控制.北京：机械工业出版,1998
[15]P.M. Frank. Fault Diagnosis in Dynamic Systems Using Analytical and Knowledge based redundancy-a Survey and Some New Results Automatica.1990,26(3):459-474P
[16]J J. Gertler. Fault detection and diagnosis in engineering systems. Marcel Dekker, NewYork,1998
[17]R. Patton, P.M. Frank, R.N. Clark, Fault Diagnosis in Dynamic Systems. Theoryand Application, Englewood Cliffs, NJ:PrenticeHall,1989
[18]S. Dash, V. Venkatasubramnian. Challenges in the industrial application of fault diagnosis systems. Computers and chemicl engineering,2000,24:785-791P
[19]P.M. Frank. Fault Diagnosis in Dynamic Systems Using Analytical and Knowledge based redundancy-a Survey and Some New Results. Automatica,1990,26(3):459-474P
[20]周东华,王桂增.故障诊断技术综述.化工自动化及仪表,1998,25(1)：58-62页
[21]RJ. Patton, J. Chen. Review of parity space approaches to fault diagnosis for aerospace system. Journal of guidance, control and dynamics,1994,17(2):278-285P
[22]RJ. Patton. Robustness in model based fault diagnosis:1995 situation. Annual reviews in control, 1997,21:103-123P
[23]J J. Gertler. Analytical redundancy methods in fault detection and isolation. Proceedings of IFAC fault detection, supervision and safety for technical processe, Baden Baden, Germany,1991:9-21P
[24]R J. Patton, J. Chen. A review of parity space approaches to fault diagnosis. Proceedings of IFAC fault detection, supervision and safety for technical processe. Baden Baden. Germany,1991:65-81P
[25]M.S.Z. Abidin, R. Yusof, M. Kahlid etal. Application of a Model-based fault detection and diagnosis using parameter estimation and fuzzy inference to a DC-servomotor:Proc.2002 IEEE International Symposium on Intelligent Control, Vancouver, Canada,2002:783-788P
[26]A.P. Wang, H. Wang. Fault diagnosis for nonlinear systems via neural networks and parameter estimation. Proc.2005 International Conference on Control and Automation, Budapest,2005: 55-563P
[27]B. Youssef, M. Alamir, F. Ibrahim. Diagnosis and on-line parametric estimation of simulated moving bed. Proc. the 44th IEEE CDC-ECC'05, Seville, Spain,2005:7656-7661P
[28]S. Ibaraki, S. Suryanarayanan, M. Tomizuka Design of Luenberger state observers using fixed-structure Hβ optimization and its application to fault detection in lane-keeping control of automated vehicles. IEEE/ASME Trans. on Mechatronics,2005,10(1):34-42P
[29]F. Castang, A.Zolghadri, D. Henry etal. A LMI approach for designing robust FDI filters with guaranteed fault sensitivity performance. Proc.2002 IEEE Int. Conf.on Systems, Man and Cybernetics, Yasmine Hammamet, Tunisia,2002:503-508P
[30]M.S. Fadali, P. Colaneri, M. Nel. H2 robust fault estimation for periodic systems. Proc, American Control Conference, Denver, Colorado,2003:2973-2978P
[31]W.H. Li, S. Shah. Data driven Kalman filters for non—uniformly sampled multirate systems with application to fault diagnosis. Proc.2005 American Control Conference, Portland,2005,4: 2768-2774P
[32]C.P. Tan. C. Edwards. Sliding mode observers for reconstruction of simultaneous actuator and sensor faults. Proc. the 42nd IEEE Conference on Decision and Control, Maul, USA,2003:1455-1460P
[33]N. Kabbaj, A. Doncescu, B.Dahhou etal. Residual evaluation using time-frequency analysis for fault detection and isolation.Proc.2002 IEEE Int. Conf.on Systems, Man and Cybernetics, Yasmine Tunisia,2002:468-472P
[34]钟麦英,张承慧,S.X.Ding.一种鲁棒故障检测与反馈控制的最优集成设计方法.自动化学报,2004,30(2)：294-299页
[35]H. Ye, G.Z. Wang, S.X. Ding. An IIR. filter based parity space approach for fault detection. Proc. the 15th IFAC World Congress, Barcelona,2002
[36]H. Ye, P. Zhang, S.X. Ding etal. A time—frequency domain fault detection approach based on parity relation and wavelet transform. Proc. the 39th IEEE CDC, Sydney, Australia,2000:4156-4161P
[37]H. Ye, G.Z. Wang, S.X. Ding. A new parity space approach for fault detection based on stationary wavelet transform. IEEE Trans. on Automatic Control,2004,49(2):281-287P
[38]魏春岭,张洪钺.多传感器斜置系统故障检测的奇偶向量补偿方法.北京航空航天大学学报,2001,27(16)：698-701页
[39]谢铁钢,张宇河.基于时问冗余的一致空间法在故障检测及隔离中的应用.北京理工大学学报,2002,22(4)：476-480页
[40]AR. Bolivar, G. Garcia, F. Szigeti, J. Bernussou. A fault detection and isolation filter for linear systems with perturbations. Proceedings of the IFAC 14th world congress, Beijing P. R. China,1999: 563-568P
[41]R. Tarantino, F. Szigeti, E. Colina-Morles. Generalizde luenberger observer based fault detection fulter design:an industrial application Control engineering practice,2000,8:665-671P
[42]G.R. Duan, D. Howe, R.J. Patton. Robust fault detection in descriptor systems via generalized unknown input observers. Proceedings of the IFAC 14th world congress, Beijing, P.R.China,1999: 43-48P
[43]J. Chen, R. Patton, H. Zhang. Design of unknown input observers and robust detection filters. International journal of control,1996,63:85-105P
[44]C. Edwards, S.K.Spurgeon, R.J. Patton. Sliding mode observers for fault detection and isolation. Automatica,2000,36:541-553P
[45]R. Sreedhar, B. Fernadez, G. Masada. Robust fault detection in nonlinear systems using sliding mode observers. Proceedings of the IEEE congerence on control applications,1993:715-721P
[46]P. Amann, J.M. Peronne, G.L. Gissinger, P.M. frank. Fuzzy observer for fault detection in complex system applied to detection of critical driving sitations. Proceedings of 3th Joint Cosy workshop, Budapest,1997:153-158P
[47]C.D. Persis. A necessary Condition and a back stepping Pobserver for nonlinear fault detection. Proceedings of the IFAC 14th world congress, Beijing, P.R. China,1999:2896-2901P
[48]叶吴,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页
[49]叶吴,王桂增,方祟智,张永光,刘志军.一种基于小波变换的导弹运输车辆故障诊断方法.自动化学报,1998,24(3)：301-306页
[50]J. Jiang, F. Jia. A robust fault diagnosis scheme based on signal model estimation. International Journal of control,1995,62(2):461-475P
[51]S.B. Koppen, P.M. Frank. Neural networks in model-based fault diagnosis. Proc.1996 IFAC World Congress. San Francisco,1996:67-72P
[52]L Shi, N. Sepehri. Adaptive fuzzy neural based multiple models for fault diagnosis of a pneumatic actuator. Proc.2004 American Control Conference, Boston,2004:3753-3758P
[53]J. Zhang. Improved on-line process fault diagnosis using stacked neural networks. Proc.2002 IEEE Int. Conj. Control Applications, Glesgow,2002:689-694P
[54]杨天奇.基于神经网络自适应滤波器的故障检测与诊断.数据采集与处理,2000,15(1)：128-131页
[55]刘锋,夏春先,黄振和.基于人工神经网络的故障诊断专家系统.国外电子测量技术2004(4)：34-36页
[56]朱大奇,于盛林.电子电路故障诊断的神经网络数据融合算法.东南大学学报,2001,31(6)：87-91
[57]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(2)：153-158页
[58]朱大奇,于盛林.电子电路故障诊断的神经网络数据融合算法.东南大学学报,2001,31(6)：87-91页
[59]MA Awadallah, M.M. Morcos, S. Gopalakrishnan etal. Aneuro-fuzzy approach to automatic diagnosis and location of stator interfturn faults in CSI-fed PM brushless DC motors. IEEE Trans. on Energy Conversion,2005,20(2):253-259P
[60]QJ. Guo, H.B. Yu, A.D. Xu. Wavelet fuzzy neural network for fault diagnosis. Proc.2005 International Conference on Communications, Circuits and Systems, Hong Kong,2005,2:993-998P
[61]P. Arpaia, G. Lucariello, A. Zanesco. Muhi-agent remote predictive diagnosis of dangerous good transports. Proc. the IEEE Instrumentation and Measurement Technology Conference, Ottawa, Canada,2005:1685-1690P
[62]王文辉,周东华.基于定性和半定性方法的故障检测与诊断技术.控制理论与应用,2002,19(5)：653-659页
[63]方培培,李永丽,杨晓军.Petri网与专家系统结合的输电网络故障诊断方法.电力系统及其自动化学报,2005,17(2)：26-30页
[64]戴忠健,苏利敏.基于遗传算法的网络故障诊断专家系统的设计与实现.北京理工大学学报,2005,25(1)：38-40页
[65]余浩章,陈新华.基于故障树的故障诊断推理新方法.上海海运学院学报,2001,22(3)：65-67页
[66]王永传,郁文贤,庄钊文.一种故障树模糊重要度分析的新方法.国防科技大学学报,1999,21(3)：63-66页
[67]朱大奇,于盛林.基于故障树最小割集的故障诊断方法研究.数据采集与处理2002,17(3)：341-344页
[68]朱大奇.电子设备故障诊断原理与实践.北京：电子工业出版,2004
[69]苏羽,赵海,田富俊等.基于信息融合原理的智能故障诊断模型.计算机工程与应用,2004(8)：1-2页
[70]罗跃纲,陈长征.基于信息融合的集成小波神经网络故障诊断.东北大学学报,2002,23(8)：802-805页
[71]Z.H. Hu, Y.Z. Cai, X. He etal. Fusion of muhi-class support vector machines for fault diagnosis. Proc. 2005 American Control Conference, Portland,2005:1941-1945P
[72]王海涛.RBF神经网络与证据理论相结合的特征级信息融合方法的研究.哈尔滨工业大学学报,2004
[73]李志芳,冯秀芳.基于模糊模型的多Agent故障诊断系统的研究.太原理工大学学报,2005,36(2)：220-223页
[74]朱大奇,徐振斌.基于证据理论的电机故障诊断种方法的研究.华中科技大学学报,2001,29(12)：68-70页
[75]朱群雄,刘光.CBR技术在Multi Agent故障诊断中的应用.计算机工程与应用,2004(21)：111-113页
[76]俞立.鲁棒控制一线性矩阵不等式处理方法.北京：清华大学出版,2002
[77]仝茂达.线性系统理论和设计.合肥：中国科学技术大学出版,2004
[78]段广仁.线性系统理论.哈尔滨：哈尔滨工业大学出版,1997
[79]郑大钟.线性系统理论.北京：清华大学出版社,1990
[80]卜长江,罗跃生.矩阵论.哈尔滨：哈尔滨工程大学,2003
[2]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
[3]蒋浩天.工业系统的故障检测与诊断.北京：机械工业出版,2003
[4]周鸣岐,徐军.控制系统故障诊断.计算机自动测量与控制,2000,8(3)：5-8页
[5]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页
[6]侯颖,孙秀霞,甘业君.控制系统故障诊断技术的应用与发展.中国设备工程,2004,3：46-49页
[7]任伟建,赵永玲,杨克远,董宏丽.控制系统故障诊断的方法.大庆石油学院学报,2003,27(3)：44-47页
[8]K. Watanebe, D.M. Himmelblau. Insuument Fault Detection in Systems with Uncertainties. International Journal of System Science,1982,13:137-158P
[9]J. Chen, H.Y. Zhang. Robust Detection of Faulty Actuators via Unknown Input Observers.Intemational Journal of System Science,1991,22:1829-1839P
[10]徐似春,萧德云.一种新的基于参数估计的故障诊断方法.控制理论与应用,2001,18(4)：493-497页
[11]周川,胡寿松.基于神经网络的非线性观测器及在线故障检测.数据采集与处理,1998,13(3)：206-209页
[12]胡寿松,王源.基于支持向量机的非线性系统故障诊断.控制与决策,2001,16(5)：617-620页
[13]王占山,李平,任正云,李奇安.非线性系统的故障诊断技术.自动化与仪器仪表,2001,5：8-10页
[14]闻新,张洪钺,周露.控制系统的故障诊断和容错控制.北京：机械工业出版,1998
[15]P.M. Frank. Fault Diagnosis in Dynamic Systems Using Analytical and Knowledge based redundancy-a Survey and Some New Results Automatica.1990,26(3):459-474P
[16]J J. Gertler. Fault detection and diagnosis in engineering systems. Marcel Dekker, NewYork,1998
[17]R. Patton, P.M. Frank, R.N. Clark, Fault Diagnosis in Dynamic Systems. Theoryand Application, Englewood Cliffs, NJ:PrenticeHall,1989
[18]S. Dash, V. Venkatasubramnian. Challenges in the industrial application of fault diagnosis systems. Computers and chemicl engineering,2000,24:785-791P
[19]P.M. Frank. Fault Diagnosis in Dynamic Systems Using Analytical and Knowledge based redundancy-a Survey and Some New Results. Automatica,1990,26(3):459-474P
[20]周东华,王桂增.故障诊断技术综述.化工自动化及仪表,1998,25(1)：58-62页
[21]RJ. Patton, J. Chen. Review of parity space approaches to fault diagnosis for aerospace system. Journal of guidance, control and dynamics,1994,17(2):278-285P
[22]RJ. Patton. Robustness in model based fault diagnosis:1995 situation. Annual reviews in control, 1997,21:103-123P
[23]J J. Gertler. Analytical redundancy methods in fault detection and isolation. Proceedings of IFAC fault detection, supervision and safety for technical processe, Baden Baden, Germany,1991:9-21P
[24]R J. Patton, J. Chen. A review of parity space approaches to fault diagnosis. Proceedings of IFAC fault detection, supervision and safety for technical processe. Baden Baden. Germany,1991:65-81P
[25]M.S.Z. Abidin, R. Yusof, M. Kahlid etal. Application of a Model-based fault detection and diagnosis using parameter estimation and fuzzy inference to a DC-servomotor:Proc.2002 IEEE International Symposium on Intelligent Control, Vancouver, Canada,2002:783-788P
[26]A.P. Wang, H. Wang. Fault diagnosis for nonlinear systems via neural networks and parameter estimation. Proc.2005 International Conference on Control and Automation, Budapest,2005: 55-563P
[27]B. Youssef, M. Alamir, F. Ibrahim. Diagnosis and on-line parametric estimation of simulated moving bed. Proc. the 44th IEEE CDC-ECC'05, Seville, Spain,2005:7656-7661P
[28]S. Ibaraki, S. Suryanarayanan, M. Tomizuka Design of Luenberger state observers using fixed-structure Hβ optimization and its application to fault detection in lane-keeping control of automated vehicles. IEEE/ASME Trans. on Mechatronics,2005,10(1):34-42P
[29]F. Castang, A.Zolghadri, D. Henry etal. A LMI approach for designing robust FDI filters with guaranteed fault sensitivity performance. Proc.2002 IEEE Int. Conf.on Systems, Man and Cybernetics, Yasmine Hammamet, Tunisia,2002:503-508P
[30]M.S. Fadali, P. Colaneri, M. Nel. H2 robust fault estimation for periodic systems. Proc, American Control Conference, Denver, Colorado,2003:2973-2978P
[31]W.H. Li, S. Shah. Data driven Kalman filters for non—uniformly sampled multirate systems with application to fault diagnosis. Proc.2005 American Control Conference, Portland,2005,4: 2768-2774P
[32]C.P. Tan. C. Edwards. Sliding mode observers for reconstruction of simultaneous actuator and sensor faults. Proc. the 42nd IEEE Conference on Decision and Control, Maul, USA,2003:1455-1460P
[33]N. Kabbaj, A. Doncescu, B.Dahhou etal. Residual evaluation using time-frequency analysis for fault detection and isolation.Proc.2002 IEEE Int. Conf.on Systems, Man and Cybernetics, Yasmine Tunisia,2002:468-472P
[34]钟麦英,张承慧,S.X.Ding.一种鲁棒故障检测与反馈控制的最优集成设计方法.自动化学报,2004,30(2)：294-299页
[35]H. Ye, G.Z. Wang, S.X. Ding. An IIR. filter based parity space approach for fault detection. Proc. the 15th IFAC World Congress, Barcelona,2002
[36]H. Ye, P. Zhang, S.X. Ding etal. A time—frequency domain fault detection approach based on parity relation and wavelet transform. Proc. the 39th IEEE CDC, Sydney, Australia,2000:4156-4161P
[37]H. Ye, G.Z. Wang, S.X. Ding. A new parity space approach for fault detection based on stationary wavelet transform. IEEE Trans. on Automatic Control,2004,49(2):281-287P
[38]魏春岭,张洪钺.多传感器斜置系统故障检测的奇偶向量补偿方法.北京航空航天大学学报,2001,27(16)：698-701页
[39]谢铁钢,张宇河.基于时问冗余的一致空间法在故障检测及隔离中的应用.北京理工大学学报,2002,22(4)：476-480页
[40]AR. Bolivar, G. Garcia, F. Szigeti, J. Bernussou. A fault detection and isolation filter for linear systems with perturbations. Proceedings of the IFAC 14th world congress, Beijing P. R. China,1999: 563-568P
[41]R. Tarantino, F. Szigeti, E. Colina-Morles. Generalizde luenberger observer based fault detection fulter design:an industrial application Control engineering practice,2000,8:665-671P
[42]G.R. Duan, D. Howe, R.J. Patton. Robust fault detection in descriptor systems via generalized unknown input observers. Proceedings of the IFAC 14th world congress, Beijing, P.R.China,1999: 43-48P
[43]J. Chen, R. Patton, H. Zhang. Design of unknown input observers and robust detection filters. International journal of control,1996,63:85-105P
[44]C. Edwards, S.K.Spurgeon, R.J. Patton. Sliding mode observers for fault detection and isolation. Automatica,2000,36:541-553P
[45]R. Sreedhar, B. Fernadez, G. Masada. Robust fault detection in nonlinear systems using sliding mode observers. Proceedings of the IEEE congerence on control applications,1993:715-721P
[46]P. Amann, J.M. Peronne, G.L. Gissinger, P.M. frank. Fuzzy observer for fault detection in complex system applied to detection of critical driving sitations. Proceedings of 3th Joint Cosy workshop, Budapest,1997:153-158P
[47]C.D. Persis. A necessary Condition and a back stepping Pobserver for nonlinear fault detection. Proceedings of the IFAC 14th world congress, Beijing, P.R. China,1999:2896-2901P
[48]叶吴,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页
[49]叶吴,王桂增,方祟智,张永光,刘志军.一种基于小波变换的导弹运输车辆故障诊断方法.自动化学报,1998,24(3)：301-306页
[50]J. Jiang, F. Jia. A robust fault diagnosis scheme based on signal model estimation. International Journal of control,1995,62(2):461-475P
[51]S.B. Koppen, P.M. Frank. Neural networks in model-based fault diagnosis. Proc.1996 IFAC World Congress. San Francisco,1996:67-72P
[52]L Shi, N. Sepehri. Adaptive fuzzy neural based multiple models for fault diagnosis of a pneumatic actuator. Proc.2004 American Control Conference, Boston,2004:3753-3758P
[53]J. Zhang. Improved on-line process fault diagnosis using stacked neural networks. Proc.2002 IEEE Int. Conj. Control Applications, Glesgow,2002:689-694P
[54]杨天奇.基于神经网络自适应滤波器的故障检测与诊断.数据采集与处理,2000,15(1)：128-131页
[55]刘锋,夏春先,黄振和.基于人工神经网络的故障诊断专家系统.国外电子测量技术2004(4)：34-36页
[56]朱大奇,于盛林.电子电路故障诊断的神经网络数据融合算法.东南大学学报,2001,31(6)：87-91
[57]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(2)：153-158页
[58]朱大奇,于盛林.电子电路故障诊断的神经网络数据融合算法.东南大学学报,2001,31(6)：87-91页
[59]MA Awadallah, M.M. Morcos, S. Gopalakrishnan etal. Aneuro-fuzzy approach to automatic diagnosis and location of stator interfturn faults in CSI-fed PM brushless DC motors. IEEE Trans. on Energy Conversion,2005,20(2):253-259P
[60]QJ. Guo, H.B. Yu, A.D. Xu. Wavelet fuzzy neural network for fault diagnosis. Proc.2005 International Conference on Communications, Circuits and Systems, Hong Kong,2005,2:993-998P
[61]P. Arpaia, G. Lucariello, A. Zanesco. Muhi-agent remote predictive diagnosis of dangerous good transports. Proc. the IEEE Instrumentation and Measurement Technology Conference, Ottawa, Canada,2005:1685-1690P
[62]王文辉,周东华.基于定性和半定性方法的故障检测与诊断技术.控制理论与应用,2002,19(5)：653-659页
[63]方培培,李永丽,杨晓军.Petri网与专家系统结合的输电网络故障诊断方法.电力系统及其自动化学报,2005,17(2)：26-30页
[64]戴忠健,苏利敏.基于遗传算法的网络故障诊断专家系统的设计与实现.北京理工大学学报,2005,25(1)：38-40页
[65]余浩章,陈新华.基于故障树的故障诊断推理新方法.上海海运学院学报,2001,22(3)：65-67页
[66]王永传,郁文贤,庄钊文.一种故障树模糊重要度分析的新方法.国防科技大学学报,1999,21(3)：63-66页
[67]朱大奇,于盛林.基于故障树最小割集的故障诊断方法研究.数据采集与处理2002,17(3)：341-344页
[68]朱大奇.电子设备故障诊断原理与实践.北京：电子工业出版,2004
[69]苏羽,赵海,田富俊等.基于信息融合原理的智能故障诊断模型.计算机工程与应用,2004(8)：1-2页
[70]罗跃纲,陈长征.基于信息融合的集成小波神经网络故障诊断.东北大学学报,2002,23(8)：802-805页
[71]Z.H. Hu, Y.Z. Cai, X. He etal. Fusion of muhi-class support vector machines for fault diagnosis. Proc. 2005 American Control Conference, Portland,2005:1941-1945P
[72]王海涛.RBF神经网络与证据理论相结合的特征级信息融合方法的研究.哈尔滨工业大学学报,2004
[73]李志芳,冯秀芳.基于模糊模型的多Agent故障诊断系统的研究.太原理工大学学报,2005,36(2)：220-223页
[74]朱大奇,徐振斌.基于证据理论的电机故障诊断种方法的研究.华中科技大学学报,2001,29(12)：68-70页
[75]朱群雄,刘光.CBR技术在Multi Agent故障诊断中的应用.计算机工程与应用,2004(21)：111-113页
[76]俞立.鲁棒控制一线性矩阵不等式处理方法.北京：清华大学出版,2002
[77]仝茂达.线性系统理论和设计.合肥：中国科学技术大学出版,2004
[78]段广仁.线性系统理论.哈尔滨：哈尔滨工业大学出版,1997
[79]郑大钟.线性系统理论.北京：清华大学出版社,1990
[80]卜长江,罗跃生.矩阵论.哈尔滨：哈尔滨工程大学,2003