非线性系统故障诊断若干方法及其应用研究
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摘要
随着现代控制系统的规模和复杂程度的逐渐增加,传感器、执行器及系统内部元器件都不可避免的会发生故障,因此对其安全性和可靠性的要求越来越高。故障诊断与容错控制技术是提高动态系统的安全性、可靠性的重要途径之一。因而深入研究故障诊断与容错控制技术,不但具有重要的理论意义,而且也具有巨大的实际应用价值。由于实际系统都是一定程度的非线性系统,加上系统建模误差和各种外部干扰的存在,使得对系统进行故障诊断变得更加复杂和困难。目前关于非线性系统的故障诊断技术受到广大学者的关注,是当前研究的热点和难点内容之一。本文针对非线性系统故障诊断技术领域内存在的一些问题及其发展趋势,结合相关学科理论的最新研究成果,研究了非线性系统故障诊断的若干方法及其应用。所取得的主要成果有:
研究了一类非线性摄动时滞系统的鲁棒故障检测问题。分别采用了基于H∞/H?滤波器的方法和基于参考模型的方法设计了故障检测滤波器。通过分析所研究系统的特点,设计故障检测滤波器,利用鲁棒控制技术,将故障检测滤波器增益矩阵的求解问题转化为鲁棒稳定性分析问题,并给出了该问题解的存在的LMI条件和求解方法。所设计的故障检测滤波器,既考虑了故障检测结果对系统故障的敏感性,又考虑了对外界干扰的鲁棒性。
研究了一类非线性不确定时滞系统的故障检测问题。首先根据系统的状态方程,建立反步观测器,并根据原系统的状态方程和反步观测器方程建立广义残差系统。然后,采用Backstepping设计方法,结合广义残差系统的特点,选取一个合适的李雅普诺夫函数,在证明故障检测系统稳定的同时,给出了反步观测器增益矩阵的存在条件和求解方法。
结合预测控制中滚动优化的思想和迭代学习控制理论,设计了一种新型的故障跟踪估计器进行故障诊断。该方法是根据预测控制中滚动优化的思想,在一个优化时域内,根据系统实际运行时的输出和估计出的系统输出之差,通过一个迭代算法进行反复迭代运算来调节故障跟踪估计器中的虚拟故障,直到跟踪误差满足要求为止,此时的虚拟故障接近于实际故障,从而实现故障的检测与估计。文中给出了迭代学习算法的收敛性证明,并进行了故障跟踪特性分析。结果表明:迭代运算的初始条件,不影响最终故障估计的准确性;系统的不确定性(包含建模误差和外界扰动)会对故障估计带来误差。该方法不但适用于线性系统,也适用于非线性系统;既可用于确定性系统,也可用于不确定系统,适用面宽广。
将自抗扰控制器中扩展状态观测器的思想用于故障诊断之中。根据扩展状态观测器的设计思想,将系统中发生的故障和不确定项看作系统的一个增广状态,通过对增广系统构建观测器进行状态估计(其中包括对增广状态的估计),来达到故障估计的目的。在非线性系统不确定部分范数有界的假设前提下,通过选取合适的阈值,可以有效的检测和估计非线性系统中的故障。同基于神经网络的故障诊断方法相比,该方法可以实时在线的进行故障检测和估计,大大提高了故障诊断的实时性。将该方法应用到了Vander Pol自激系统和机器手系统中。
Modern systems are becoming more and more complex and sophisticated in their demand for performance, reliability and increasing autonomy. It is inevitable for sensors, actuators, and impotents inside the system that the fault occurs. Fault diagnosis and tolerant control technology is an important approach to improve the safety and reliability for dynamic systems. Research on fault diagnosis and tolerant control strategy has both theoretical and practical importance. However, the existence of the nonlinearity in the practical plant and uncertainty and noise of the plant models make it more and more difficult. At present, it has drawn wide attention, and has been one of the main topics in the control domain. In the thesis, according to the problems existed in the field of nonlinear fault diagnosis and the development trend of this subject, the fault diagnosis approaches for nonlinear systems and its applications are studied, and the main research results are given as follows:
Robust fault detection approaches for a class of time-delay systems with nonlinear perturbations are studied. The design procedure of the fault detection filter is based on two methods: the H∞/ H? filter based approach and the reference model approach. By analyzing the characteristics of the system, design a fault detection filter, and then based on the robust control theory, the problem of designing the gain matrix of the fault detection filter can be solved by using the system’s robust stability analysis method. The existence and calculating methods of the gain matrix of the fault detection filter are also given in terms of LMI equality. The fault detection methods mentioned in this chapter,takes into account the sensitivity to system faults and robustness against system uncertainty simultaneously.
Fault detection approach for a class of nonlinear time-delay systems with uncertainty is studied. First of all, according to the system equation, a Backstepping observer is constructed. For the purpose of fault detection, a general residual system is constructed by the system equation and residual system. Then, based on the Backstepping methods, a Lyapunov function is used to prove the stability of the Backstepping observer, and the solvable conditions of the gain matrix of the Backstepping observer are given at the same time.
A novel fault tracking approximator (FTA) is proposed for fault diagnosis based on the predictive control and iterative learning control theory. Based on the predictive control theory, choose an optimization time span and adjust the virtual fault by using iterative learning algorithm according to the errors of the outputs of fault tracking approximator and the actual system outputs, until the errors meet the requirements. At this time, the virtual faults can approach the real system faults to diagnose the system faults. The convergence of this algorithm and the analysis of the tracking characteristics of the FTA are given in this paper. The results are given as follows: the initial conditions of the iterative learning algorithm do not have an effect on the accuracy of the fault tracking in the time axis. The system uncertainty, including the modelling errors and the noises, will bring fault tracking error. If the system uncertainty can be erased, the FTA can track the system faults。The fault tracking approximator can not only be used in linear systems, but in nonlinear systems; not only in general systems, but in uncertainty systems, and can be widely applied in real systems.
The extended states observer (ESO) of the active disturbance rejection controller is used for fault diagnosis. According to the theory of ESO, the system faults and uncertainty are viewed as an extended system state. An fault diagnosis observer is constructed for the purpose of fault diagnosis and the estimation of the system states. Under the assumption that the uncertainty is bounded in terms of norm, the system faults can be detected by selecting appropriate threshold. Compared with the neural-network based fault diagnosis approach, the approach proposed in this chapter can detect and estimate the system fault in real time, which improve the efficiency of the fault diagnosis. Moreover, the approach is applied to Vander Pol oscillator system and robot arm system.
研究了一类非线性摄动时滞系统的鲁棒故障检测问题。分别采用了基于H∞/H?滤波器的方法和基于参考模型的方法设计了故障检测滤波器。通过分析所研究系统的特点,设计故障检测滤波器,利用鲁棒控制技术,将故障检测滤波器增益矩阵的求解问题转化为鲁棒稳定性分析问题,并给出了该问题解的存在的LMI条件和求解方法。所设计的故障检测滤波器,既考虑了故障检测结果对系统故障的敏感性,又考虑了对外界干扰的鲁棒性。
研究了一类非线性不确定时滞系统的故障检测问题。首先根据系统的状态方程,建立反步观测器,并根据原系统的状态方程和反步观测器方程建立广义残差系统。然后,采用Backstepping设计方法,结合广义残差系统的特点,选取一个合适的李雅普诺夫函数,在证明故障检测系统稳定的同时,给出了反步观测器增益矩阵的存在条件和求解方法。
结合预测控制中滚动优化的思想和迭代学习控制理论,设计了一种新型的故障跟踪估计器进行故障诊断。该方法是根据预测控制中滚动优化的思想,在一个优化时域内,根据系统实际运行时的输出和估计出的系统输出之差,通过一个迭代算法进行反复迭代运算来调节故障跟踪估计器中的虚拟故障,直到跟踪误差满足要求为止,此时的虚拟故障接近于实际故障,从而实现故障的检测与估计。文中给出了迭代学习算法的收敛性证明,并进行了故障跟踪特性分析。结果表明:迭代运算的初始条件,不影响最终故障估计的准确性;系统的不确定性(包含建模误差和外界扰动)会对故障估计带来误差。该方法不但适用于线性系统,也适用于非线性系统;既可用于确定性系统,也可用于不确定系统,适用面宽广。
将自抗扰控制器中扩展状态观测器的思想用于故障诊断之中。根据扩展状态观测器的设计思想,将系统中发生的故障和不确定项看作系统的一个增广状态,通过对增广系统构建观测器进行状态估计(其中包括对增广状态的估计),来达到故障估计的目的。在非线性系统不确定部分范数有界的假设前提下,通过选取合适的阈值,可以有效的检测和估计非线性系统中的故障。同基于神经网络的故障诊断方法相比,该方法可以实时在线的进行故障检测和估计,大大提高了故障诊断的实时性。将该方法应用到了Vander Pol自激系统和机器手系统中。
Modern systems are becoming more and more complex and sophisticated in their demand for performance, reliability and increasing autonomy. It is inevitable for sensors, actuators, and impotents inside the system that the fault occurs. Fault diagnosis and tolerant control technology is an important approach to improve the safety and reliability for dynamic systems. Research on fault diagnosis and tolerant control strategy has both theoretical and practical importance. However, the existence of the nonlinearity in the practical plant and uncertainty and noise of the plant models make it more and more difficult. At present, it has drawn wide attention, and has been one of the main topics in the control domain. In the thesis, according to the problems existed in the field of nonlinear fault diagnosis and the development trend of this subject, the fault diagnosis approaches for nonlinear systems and its applications are studied, and the main research results are given as follows:
Robust fault detection approaches for a class of time-delay systems with nonlinear perturbations are studied. The design procedure of the fault detection filter is based on two methods: the H∞/ H? filter based approach and the reference model approach. By analyzing the characteristics of the system, design a fault detection filter, and then based on the robust control theory, the problem of designing the gain matrix of the fault detection filter can be solved by using the system’s robust stability analysis method. The existence and calculating methods of the gain matrix of the fault detection filter are also given in terms of LMI equality. The fault detection methods mentioned in this chapter,takes into account the sensitivity to system faults and robustness against system uncertainty simultaneously.
Fault detection approach for a class of nonlinear time-delay systems with uncertainty is studied. First of all, according to the system equation, a Backstepping observer is constructed. For the purpose of fault detection, a general residual system is constructed by the system equation and residual system. Then, based on the Backstepping methods, a Lyapunov function is used to prove the stability of the Backstepping observer, and the solvable conditions of the gain matrix of the Backstepping observer are given at the same time.
A novel fault tracking approximator (FTA) is proposed for fault diagnosis based on the predictive control and iterative learning control theory. Based on the predictive control theory, choose an optimization time span and adjust the virtual fault by using iterative learning algorithm according to the errors of the outputs of fault tracking approximator and the actual system outputs, until the errors meet the requirements. At this time, the virtual faults can approach the real system faults to diagnose the system faults. The convergence of this algorithm and the analysis of the tracking characteristics of the FTA are given in this paper. The results are given as follows: the initial conditions of the iterative learning algorithm do not have an effect on the accuracy of the fault tracking in the time axis. The system uncertainty, including the modelling errors and the noises, will bring fault tracking error. If the system uncertainty can be erased, the FTA can track the system faults。The fault tracking approximator can not only be used in linear systems, but in nonlinear systems; not only in general systems, but in uncertainty systems, and can be widely applied in real systems.
The extended states observer (ESO) of the active disturbance rejection controller is used for fault diagnosis. According to the theory of ESO, the system faults and uncertainty are viewed as an extended system state. An fault diagnosis observer is constructed for the purpose of fault diagnosis and the estimation of the system states. Under the assumption that the uncertainty is bounded in terms of norm, the system faults can be detected by selecting appropriate threshold. Compared with the neural-network based fault diagnosis approach, the approach proposed in this chapter can detect and estimate the system fault in real time, which improve the efficiency of the fault diagnosis. Moreover, the approach is applied to Vander Pol oscillator system and robot arm system.
引文
[1]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.
[2] Murray R M, Astrom K J, Boyd SP, et al. Future directions in control in an information-rich world[J]. IEEE Control Systems 2003,23(5):20-33.
[3]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.
[4] Chen J., Patton R. J. Robust Model-Based Fault Diagnosis for Dynamic Systems [A]. Kluwer Academic Press. 1999
[5] Willsky A S. A survey of design methods for failure detection in dynamic systems[J]. Automatica. 1976, 12: 601-611.
[6] Patton R J, Frank P M, Clark R. Fault diagnosis in dynamic systems [A]. Theory and application, Prentice Hall, Herfordshire, 1989.
[7] Gertler J. Fault Detection and Diagnosis in Engineering Systems [A]. Marcel Dekker, New York, 1998.
[8] Frank P M. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—A survey and some new results[J]. Automatica, 1990, 26(3): 459-474.
[9] Isermann R., Balle P. Trends in the application of model based fault detection and diagnosis of technical processes[C]. In Proceedings of 13th IFAC world congress, San Francisco, USA, 1996: 1-12.
[10] Frank P M., Koppen-Seliger. New developments using AI in fault diagnosis[C]. In Proceedings of the IFAC Artificial Intellgence in Real-Time Control, Beld, Slovenia, 1995: 1-12.
[11] Dash S, Venkatasubramnian V. Challenges in the industrial applications of fault diagnosis systems[J]. Computers and Chemical Engineering, 2000, 24: 785-791.
[12] Mehra R K, Peschon J. An innovation approach to fault detection and diagnosis in dynamics[J]. Automatica, 1971, 7: 637-640.
[13]陈玉东,施颂椒,翁正新.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.
[14] Patton R J, Chen J. Review of parity space approaches to fault diagnosis for aerospace systems[J]. Journal of Guidance, Control and Dynamics, 1994,17(2): 278-285.
[15]李令莱,周东华.基于解析模型的非线性系统鲁棒故障诊断方法综述.信息与控制. 2004, 33(4): 451-462.
[16] Beard RV. Failure accommodation in linear systems through self-reorganization, [Dissertation], Report MVT-71-1, Man Vehicle Lab, MIT, Cambridge, Massachusetts, 1971
[17]朱张青.动态系统的鲁棒故障检测与诊断技术[博士论文].南京:南京理工大学. 2005
[18] Mehra R K, Peshon I. An innovations approach to fault detection and diagnosis in dynamic systems[J]. Automatica, 1971 ,7:637-640.
[19] Luenberger D G.. Observers for Multivariable Systems. Contemporary Classics in Engineering and Applied Science. ISI Press, 1986.
[20] Watanabe K, Himmelblau D M. Instrument fault detection in systems with uncertainties [J]. International Journal of Systems Science, 1982 ,13(2):137-158.
[21] Gertler, J.J. Survey of model-based failure detection and isolation in complex plants[J]. IEEE Control systems Magazine, 1988; 8:3-11.
[22] Liu W, Wang H. Linearization techniques in fault diagnosis of nonlinear systems [J]. Journal of systems and control engineering proceedings, 2000,214(4): 241-245.
[23] Wei C, M Saif. An actuator fault isolation strategy for linear and nonlinear systems [C]. In Proceedings of ACC, Portland, 2005: 3321-3326
[24]陈玉东,施颂椒,翁正新.基于线性化技术的非线性系统故障诊断.应用科学学报. 2002,20(4): 365-368.
[25] Xu A, Zhang QH. Nonlinear system fault diagnosis based on adaptive estimation [J]. Automatica, 2004,40:1181-1193.
[26] C. De Persis and A. Isidori. A geometric approach to nonlinear fault detection and isolation[J]. IEEE Trans. on Automatic Control, 2002,46(6): 853–865.
[27] S. Diop, R Martinez-Guerra. On an algebraic and differential approach of nonlinear systems diagnosis [C]. Proc. of 4th IEEE conf. on Decision and Control, Orlando, USA, 2001: 585-589.
[28] Ding X., Frank P. M. Fault detection via factorization approach[J]. systems and Control Letters, 1990, 14(5): 431- 436
[29] Marcin W, Andrzej O, Jozef K. Generic programming based approach to identification and fault diagnosis of nonlinear dynamic systems [J]. Int. J. Control, 2002, 75(13): 1012- 1031.
[30] P. Zhang, S.X. Ding. A simple fault detection scheme for nonlinear systems[C]. Proc. of International symposium on intelligent control, Limassol, Cyprus, 2005: 838-842.
[31] Watanabe K, Himmelblau D M. Incipient fault diagnosis of nonlinear processed with multiple causes of faults [J]. Chemical Engineering Science, 1984 ,39(3):491-508.
[32] Chen J, Patton R.J, and Zhang H Y. Design of unknown input observers and robust fault detection filters [J]. Int. J. Control, 1996, 63: 85- 105.
[33] Yu DL , Shields DN. A bilinear fault detection observer [J]. Automatica, 1996,32(11):1597-1602.
[34] Yu DL,Shields DN. A bilinear fault detection filter [J]. Int. J. Control, 1997, 68(3): 417- 430.
[35] Kinnaert M. Robust fault detection based on observers for bilinear systems [J]. Automatica, 1999,35(11):1829-1842.
[36] Kinnaert M, El Bahir L. Innovation generation for bilinear systems with unknown inputs [A]. Lecture notes in control and information science, London, 1999:445-465.
[37] Zasadzinski M, Magarotto E, Rafaralahy H, et al. Residual generator design for singular bilinear systems subjected to unmeasurable disturbances: an LMI approach [J]. Automatica, 2003,39(4): 703-713.
[38] Padalker S. Real-time fault diagnostic with multiple aspect models [C]. Proc. of the 1991 IEEE international conf. on robotics and automation. Sacramento, Canada. 1991: 803-808.
[39] Shields D.N, Du S. Fault detection observers for continuous nonlinear polynomial systems of general degree [J]. Int. J. Control, 2003, 76(5): 437- 452.
[40] Dan. T, Horak. Failure detection in dynamic systems with modeling errors [A]. J. Guaidance. Novdec,1998: 508-516.
[41] Wei C, M Saif. Fault detection and isolation based on novel unknown input observer design [C]. In Proceedings of ACC, Minnesota,USA, 2006: 5129-5134.
[42] Vemuri A T, Polycarpou M M. Robust nonlinear fault diagnosis in input-ouput systems [J]. Int. J. Control, 1997, 68(2): 343- 360.
[43] Jiang B, Lu S, Wang X. Robust fault diagnosis for a class of nonlinear systems with unknown parameters [J]. Control theory and application, 2001,18(3): 421-426.
[44] V. Kadirkamamathan, P Li, M H Jaward, et al. A sequential Monte Carlo filtering approach to fault detection and isolation in nonlinear systems [C]. Proc. of the 39th IEEE conf. on Decision and Control. Sydney, Australia, 2000: 4335-4340.
[45] Cheng Q, Pramod K V, James M. Distributed fault detection via particle filtering and decision fusion [C]. In Proceedings of 7th international conf. on information fusion, Minnesota, USA, 2005: 1239-1246.
[46]周东华,孙优贤,席裕庚等.一类非线性系统参数偏差型故障的实时检测与诊断.自动化学报, 1993, 19(2): 184-189.
[47]周东华.一类非线性系统的传感器故障检测与诊断新方法.自动化学报,1995, 21(3): 362-365.
[48] Yu D. Fault diagnosis for a hydraulic drive system using a parameter estimation method [J]. Control Engineering Practice. 1997,5(9): 1283-1291.
[49] Gerica E A, Hou Z, Frank P M. FDI based parameter and output estimation: an integrated approach [C]. European Control Conference, Kerlsmhe, Germany,1999: 1125-1137.
[50] Hofling T, Isermann R. A adaptive parity equation and advanced parameter estimation for fault detection and diagnosis[C]. Proc. of IFAC World Congress, San Francisco, USA, 1996:55-60.
[51] Zhang Q. Adaptive Observer for MIMO Linear Time Varying System [J]. Rapprotde Recherche IRISA No.1379, submitted to IEEE Trans. on Automatic Control 2001
[52] Zhou D H, Frank P M. Actuator fault diagnosis of a class of nonlinear systems in closed-loops: a case study [A]. Proc. of international conf. on Control, UK, 1996:311-316.
[53] Kinnaert M. Design of redundancy relations for failure detection and isolation by constrained optimization[J]. International Journal of Control.1996, 63(3): 609-622
[54] Hwang D S, Chang S K and Hsu P L.A practical design for a robust fault detection and isolation system [J]. International Journal of System science.1997, 28(3): 265-275
[55] Gerrler J and Monajemy R. Generating directional residuals with dynamic parity relations[J]. Automatica. 1995,61(2): 395-421
[56] Gertler J and Kunwer M M. Optimal residual decoupling for robust fault diagnosis[J]. International Journal of Control.1995, 61(2): 395-421
[57] Magri J F, Monyon P. On residual generation by observer and parity space approaches [J]. IEEE Trans. On Automatic control. 1994,39(2): 441-447.
[58] Carcia J, Frank P M. On the relationship between observer and parameter identification based approaches to fault detection [A]. Proc. of IFAC world congress. 1996: 25-29.
[59] Gertler J. Diagnosing parametric faults: from parameter estimation to parity relation[C]. America Control Conference, Seattle, USA, 1995: 1615-1620.
[60]宋华,张洪戊等.模糊非线性奇偶方程故障诊断方法.自动化学报,2003, 29(6): 965-970.
[61] Tor F, Tyrone L V, Rahmat S. Optimization based fault detection for nonlinear sytems [A]. Proc of the ACC. Arington,VA, 2001: 1747-1752.
[62] Yu D L. Diagnosing simulated faults for an industrial furnace based on bilinear mode [J]. IEEE Trans on systems technology. 2000,8(3): 435-442.
[63] Gertler J., Singer D. A new structural framework for parity equation based failure detection and isolation[J]. Automatica, 1990, 26: 381-388.
[64] Gertler J. Analytic redundancy methods in fault detection and isolation[C]. IFAC Symposia Series, 1992, 6: 9-21.
[65] Ding S. X., Ding E. L., Jeinsch T. An approach to analysis and design of observer and parity relation based FDI systems[C]. In Proceedings of the 14th IFAC World Congress, Beijing, P. R. China, 1999: 37- 42.
[66] Gertler J. All linear methods are equal - and extendible to (some) nonlinearities[J]. International Journal of Robust and Nonlinear Control, 2002, 12(8): 629– 648.
[67] Sing K N, Zhang P, Ding S. Parity based fault estimation for nonlinear systems: an LMI approach [A]. Proc. of ACC conference, Minnesota, USA, 2006: 5141-5146.
[68] Lei Guo and Wang Hong. Fault detection and diagnosis for general stochastic systems using B-spine expansions and nonlinear filters[J]. IEEE Trans. on Automatic Control. 2005, 52(8): 1644–1652.
[69] Elisa F, Reza O S, Thomas P etc. Distributed fault diagnosis using sensor networks and consensus-based filters[C]. Proceeding of the 45th IEEE conference on Decision and control. San Diego, USA. 2006, 386-391.
[70] Kumamaru K, Hu J, Inoue K and Soderstrom T. Robust Fault detection using index of Kullback discrimination information[C]. Proceeding of IFAC World Congress, San Francisco, USA, 1996, 205-210.
[71]王俊.一种利用子波变换多尺度分辨特性的信号消噪技术.信号处理.1996,12(2): 104-109.
[72]陈涛.小波分析及其在机械诊断中的应用.机械工程学报.1997,33(3): 76-79
[73]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
[74]叶昊,王桂增,方崇智等.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
[75] Yang K, Shan G, Zhao L. Application of Wavelet Packet Analysis and Probabilistic Neural Networks in Fault Diagnosis[C]. Proc of the 6th world congress on intelligent control and automation, Dalian, China, 2006: 4378-4381.
[76]李渭华,萧德云,方崇智.一种基于δ算子的格型故障检测滤波器.自动化学报.1994, 20(4): 413-419.
[77]闻新.故障系统的智能容错控制理论研究与应[博士论文].哈尔滨工业大学. 1995.
[78]颜东.导航、制导系统状态估计方法及容错控制理论研究[博士论文].北京航空航天大学. 1995.
[79]伍学奎,陈进,周铁尘等.基于多指标融合的故障诊断理论与方法.振动工程学报, 1999,12(1):55-63.
[80] Sang W C, In-Beum L. Nonlinear dynamic process monitoring based on dynamic kernel PCA [J]. Chemical Engineering Science, 2004,5898-5908.
[81] Sang W C, Chang L, Jong M L, et al. Fault detection and identification of nonlinear processes based on kernel PCA [J]. chemometrics and intelligent laboratory systems, 2005: 55-67.
[82]丁海生,庄志洪,祝石龙等.混沌、分形和小波理论在被动信号特征提取中的应用.声学学报, 1999, 24(2): 197-203.
[83]萧德云,李渭华.双通道Lattice滤波器及其在故障检测中的应用.控制与决策.1998,13(3): 277-280
[84]萧德云,李渭华,方崇智.一种使用于故障检测德归一化滑动窗协方差格形滤波器.控制理论与应用.1995, 12(2): 230-235
[85]李渭华,萧德云,方崇智.一种基于自适应滑动窗格形滤波算法的故障监测器.自动化学报.1996, 22(2): 251-253
[86]李尔国.故障诊断方法的研究及其应用[博士论文].华东理工大学, 2002.
[87] Jiang J and Jia F.A robust fault diagnosis scheme based on signal modal estimation[J]. International Journal of Control.1995, 62(2): 461-175
[88]吕柏权.一种基于小波网络的故障检测方法.控制理论与应用, 1998,15(5): 802-805.
[89]朱大奇,于盛林.基于知识的故障诊断方法综述.安徽工业大学学报, 2002,19(3): 197-204.
[90]吴今培,肖建华.智能故障诊断与专家系统.北京:科学出版社,1997
[91] Pazzani M J. Failure driven Learning of fault diagnosis heuristics[J]. IEEE Tran. On Systems. Man and Cybernetics. 1987.17(3): 380-384
[92]彭志刚,张纪会,徐心和.基于遗传算法的知识获取及其在故障诊断中的应用研究.信息与控制.1999,28(5):391-395
[93]张雪江,朱向阳,钟秉林等.基于模拟退火演化算法的知识获取方法的研究.控制与决策.1997,12(4): 327-331。
[94]张雪江,朱向阳,钟秉林等.基于退火演化算法的知识获取机制的研究.控制理论与应用.1998,15(1): 93-99。
[95]金宏.导航系统的精度及容错性能研究[博士论文].北京:北京航空航天大学,1998.
[96]杨叔子.基于知识的故障诊断技术.北京:清华大学出版社. 1993
[97]张雪江.机械设备故障诊断系统知识自动获取及更新的研究[博士论文]..南京:东南大学. 1993
[98]吴今培.智能故障诊断与专家系统.北京:科学出版社.1999.
[99]虞和济,陈长征,张省.基于神经网络的智能诊断.北京:冶金工业出版社. 2000.
[100]杨良士.动态系统故障诊断的新方法-专家系统.信息与控制, 1998, 17(5), 26-31.
[101] Pazznai M J. Failure driven learning of fault diagnosis heuristics[J]. IEEE trans. on system, man and cybemetics, 1998, 17(3): 380-384.
[102] Huang C Y, Stengel R F. Failure model determination is a knowledge based control system[C].. Proc. of American Control Conference, USA, 1987, 1642-1649.
[103] Youn W C, Hammen J M. Aiding the operation during novel fault diagnosis[J]. IEEE trans. on system, man and cybemetics, 1998, 18(1): 142-147.
[104]张雪江.汽轮发电机组故障诊断专家系统知识处理技术的研究.振动工程学报, 1996, 9(3): 230-136.
[105]王飓舵.基于专家系统和神经网络的机车电路故障诊断系统研究.北方交通大学学报. 1996, 20(4): 495-501.
[106]魏永军,胡光锐,汪亚平.一种新的故障诊断专家系统.上海交通大学学报.1998,32(6): 137-139
[107]黄洪钟.模糊机械科学与技术.机械工程学报.1996,32(3):1-8
[108] Sauter D, Mary N, Sirou F and Thieltgen A. Fault Diagnosis in systems using fuzzy logic[C]. Proceeding of 3rd IEEE Conference on Control applications. Glasgow, UK, 883-888
[109] Schneider H. and Frank P M. Fuzzy logic based threshold adaption for fault detection in robots[C]. Proceeding of 3rd.IEEE Conference on control applications. Glasgow, UK, 1127-1132
[110] Vachkov G.. Identification for fuzzy rule based system for fault diagnosis in chemical plants[C]. Proceeding IFAC Symp. on On-line Fault Detection and Supervision in the Chemical Process Industries. Newark, Delaware, USA, 315-318
[111]方敏,陈雁翔.基于神经网络的故障诊断方法的研究.北京航空航天大学学报, 1998, 15(3): 460-465.
[112] Maruyama N, Benouarets M and Dexter A L. Fuzzy model-based fault detection and diagnosis[C]. Proceeding of IFAC World Congress, San Francisco, USA. 1996,121-126
[113] Frank P M and Kiupel N. Residual evaluation for fault diagnosis using adaptive thresholds and fuzzy inference[J]. Proceeding of IFAC World Congress, San Francisco, USA.1996, 115-120
[114] Kiran K V, Joanne B D. Automatic systhesis of fault trees for computer-based systems [J]. IEEE Trans on Reliability, 1999,48(4): 392-401.
[115] Juan A C. An algorithm to find minimal cuts of coherent fault-trees with event-classes[J]. IEEE trans. on reliability. 1999, 48(1): 31-41.
[116] Cros X E, Lowden D W. Bayesian approach to NDT data fusion[J]. Non-destructive testing and condition monitoring. 1995, 37(5): 462-468.
[117] Luo R C, Key M G. Multisensor integration and fusion in intelligent system[J]. IEEE trans. on system, man and cybemetics, 1989, 19(5): 901-931.
[118] Bogler P L. Shafer-dempster reasoning with application to multisensor target identification system[J]. IEEE trans. on system, man and cybemetics, 1987, SMC-17: 968-977.
[119]韩静,陶云刚.基于D-S证据理论和模糊数学的多传感器数据融合方法.仪器仪表学报, 2000, 21(6): 644-647.
[120]徐从富,耿卫东,潘云鹤.面向数据融合的DS方法综述.电子学报, 2001, 29(3): 393-396.
[121]朱大奇,于盛林.基于证据理论的电机故障诊断方法研究.华中科技大学学报, 2001, 12(3): 58-61.
[122]罗志增,蒋静萍.应用模糊信息融合实现目标物的分类.仪器仪表学报, 1999, 20(4): 401-404.
[123]覃祖旭.智能、信息及其在导航系统中的应用[博士论文].北京:北京航空航天大学. 1995.
[124]何友.多传感器信息融合及其应用.北京:电子工业出版社. 2000
[125]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, 12: 87-92.
[126]王建波,于达仁.液体火箭发动机故障诊断的信息融合技术.航空动力学报, 2001, 16(1):38-40.
[127] Abide M A, Conzalez R C. Data fusion in robotics and machine intelligence. American Academic press, 1992.
[128] Luo R C, Scherp R S. Dynamic multisensor data fusion system for intellegient robots[J]. Journal of robotics and automation. 1998, 4(4): 386-396.
[129] Muid M, Vachtsevanos G. Automated fault detection and identification using a fuzzy-wavelet analyais technique[C]. Proc. of Autotestcon by IEEE. 1995, 169-175.
[130] Tzafestas S G and Dalianis P J. Fault diagnosis in complex systems using artificial neural networks[C]. Proceeding of the IEEE Conf. On Control Applications.1994,2: 877-882
[131] Ungar L H. Adaptive networks for fault diagnosis and process control[J]. Computers Chemical Engineering. 1990,14:561-573
[132] Goode P V, Chow M Y. Hybrid fuzzy/neural system used to extract heuristic knowledge from a fault detection problem [J]. IEEE International conference on Fuzzy systems, 1994, 1731-1736.
[133] Mihiar S, Isermann R. Neuro-fuzzy systems for diagnosis[J]. Fuzzy Sets and Systems,1997, 89(3): 289-307.
[134] Wang H. Detecting and diagnosing saturation faults [J]. IEE conference publication,1996: 809-813.
[135] Zhou J, Bennett. Adaptive error compensation for fault detection [J]. International journal of system science, 1998, 29(1): 57-64.
[136] M.M Polycarpou and A.B. Trunov Learning methodology for failure detection and accommodation[J]. IEEE Control systems, 1995, 45(4): 16-25.
[137] Polycarpou M M, Heimichi A J. Automated fault detection and accommodation: a learning systems approach[J]. IEEE Transactions on Systems, Man and Cybernetics, 1995, 25(11): 1447-1458
[138] Polycarpou M, Trunov A. Learning approach to nonlinear fault diagnosis: Detectability analysis[J]. IEEE Transactions on Automatic Control, 2000, 45(4): 806-812
[139] Polycarpou M M. Fault accommodation of a class of multivariable nonlinear dynamical systems using a learning approach [J]. IEEE Transactions on Automatic Control, 2001, 46(5): 756-742
[140] A.T. Vemuri. Sensor bias fault diagnosis in a class of nonlinear systems[J]. IEEE Trans. on Automatic Control, 2001, 46(6): 949–954.
[141] X.D. Zhang, T. Parisini, and M.M Polycarpou. Sensor bias fault isolation in a class of nonlinear systems[J]. IEEE Trans. on Automatic Control, 2005, 50(3): 370–376.
[142] X.D. Zhang, M.M Polycarpou and T. Parisini. A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems [J]. IEEE Trans. on Automatic Control, 2002, 47(4): 576–593.
[143] R. R. Selmic, M. M. Polycarpou, and T. Parisini. Actuator fault detection in nonlinear uncertain systems using neural on-line approximation models [J]. Proc. Of the 2006 ACC, Minnestoa, USA, 2006: 5123-5128.
[144] Gomm J B. On-line learning for fault classification using an adaptive neuro-fuzzy network[C]. Proceeding of IFAC World Congress, San Francisco, USA, 1996,175-180
[145]李捷辉.RBF神经网络用于发动机控制系统的故障诊断.江苏理工大学学报(自然科学版).1999,20(5):36-39
[146]武研,施鸿宝.一种模糊规则动态调整BP算法中参数的方法.计算机研究与发展.1998,35(8):659-693
[147]李绍滋,李堂秋,周风利等.一种新型BP算法及其在故障诊断专家系统中的应用.厦门大学学报(自然科学版).1999,38(2): 186-192
[148]施洪宝,王秋荷.专家系统.西安:西安交通大学出版社,1990
[149]吴军强,梁军.基于图论的故障诊断技术及其发展.机电工程, 2003,20(5): 188-190.
[150]邓聚龙.灰色控制系统.武汉:华中理工大学,1989
[151]吴祖堂,李岳,温熙森.灰关联分析在机械设备故障诊断中的应用.系统工程理论与实践.1999,6:126-132
[152]吕锋.灰色系统关联度之分辨系数的研究.系统工程理论与实践.1997,17(6):49-54.
[153]施国洪,姚冠新.灰色系统理论在故障诊断决策中的应用.系统工程理论与实践.2001,4:120-123
[154]张建华,王占林.基于模糊神经网络的故障诊断方法的研究.北京航空航天大学学报.1997,23(4):502-506
[155] Thomas Pfeufer, Mihiar Ayoubi. Application of a hybrid neuro-fuzzy system to the fault diagnosis of an automotive electromechanical actuator [J]. Fuzzy sets and systems. 1997,89:351-360
[156]陈小平.进化计算及其应用研究[博士论文].南京:南京航空航天大学. 2001.
[157]吴伟蔚,扬叔子.故障诊断Agent研究.振动工程学报, 2000, 13(3): 393-399.
[158] Zhong M.Y, Ye Tao, Chen G.Y., Wang G.Z. An ILMI approach to RFDF for uncertainty linear systems with nonlinear perturbations[J]. ACTA AUTOMATICA SINICA. 2005,31(2): 297-300.
[159]彭涛,桂卫华等.一种故障检测滤波器的多目标优化设计方法.控制与决策. 7(2005): 773-777.
[160]钟麦英,汤兵勇等.状态时滞系统故障诊断问题的LMI方法研究.控制与决策. 17(2002): 15-18.
[161]白雷石,田作华等.基于时滞依赖H∞滤波器的时滞系统故障诊断.控制与决策. 20(2005): 1012-1016.
[162] Wang Y, Xie L, De Souza. Robust control for a class of uncertain nonlinear systems[J]. System Control Letter, 1992, 19(12): 139–149.
[163] YANG H.L MEHRDAD S. Oberver design and fault diagnosis for state-retarded dynamic systems[J]. Automatica, 1998,34(2): 217-227.
[164] W. Chen, M Saif. An iterative learning observer for fault detection and accommodation in nonlinear time-delay systems[J]. Int. J. robust and nonlinear conl. 2006, 16: 1-19
[165]谢胜利.迭代学习控制的理论与应用.北京:科学出版社,2004
[166] Shengli. Xie, Senping Tian, Yuli FuD. A new algorithm of iterative learning control with forgetting factors[C]. IFAC Control Eng. Prac.,2004;5: 625–630.
[167] Zhang P, Ding S X. A simple fault detection scheme for nonlinear systems[C]. Proc. of the 2005 IEEE International Symposium on intelligent control. Limassol, Cyprus. 2005: 838–842.
[168]韩京清.自抗扰控制器及其应用.控制与决策, 1998, 13(1):19-23
[169]韩京清.一类不确定对象的扩张状态观测器.控制与决策, 1995, 10(1):85-88
[170]黄一,韩京清.非线性连续二阶扩张状态观测器的分析与设计.科学通报, 2000, 45(13):1373-1378
[171]韩京清,张荣.二阶扩张状态观测器的误差分析.系统科学与数学, 1999, 19(4):465-471
[172] Riccardo M. G., Thomas Parisini, Marios M. Polycarpou. Distributed Fault Diagnosis With Overlapping Decompositions: An Adaptive Approximation Approa [J]. IEEE Trans. on Automatic Control. 2009, 54(4): 794–799.
[173] B. jiang, M. staroswiecki, V. Cocquempot. Fault accommodation for nonlinear dynamic systems. IEEE Trans. on Automatic Control, 2006, 51(9): 1578–1583.
[2] Murray R M, Astrom K J, Boyd SP, et al. Future directions in control in an information-rich world[J]. IEEE Control Systems 2003,23(5):20-33.
[3]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.
[4] Chen J., Patton R. J. Robust Model-Based Fault Diagnosis for Dynamic Systems [A]. Kluwer Academic Press. 1999
[5] Willsky A S. A survey of design methods for failure detection in dynamic systems[J]. Automatica. 1976, 12: 601-611.
[6] Patton R J, Frank P M, Clark R. Fault diagnosis in dynamic systems [A]. Theory and application, Prentice Hall, Herfordshire, 1989.
[7] Gertler J. Fault Detection and Diagnosis in Engineering Systems [A]. Marcel Dekker, New York, 1998.
[8] Frank P M. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—A survey and some new results[J]. Automatica, 1990, 26(3): 459-474.
[9] Isermann R., Balle P. Trends in the application of model based fault detection and diagnosis of technical processes[C]. In Proceedings of 13th IFAC world congress, San Francisco, USA, 1996: 1-12.
[10] Frank P M., Koppen-Seliger. New developments using AI in fault diagnosis[C]. In Proceedings of the IFAC Artificial Intellgence in Real-Time Control, Beld, Slovenia, 1995: 1-12.
[11] Dash S, Venkatasubramnian V. Challenges in the industrial applications of fault diagnosis systems[J]. Computers and Chemical Engineering, 2000, 24: 785-791.
[12] Mehra R K, Peschon J. An innovation approach to fault detection and diagnosis in dynamics[J]. Automatica, 1971, 7: 637-640.
[13]陈玉东,施颂椒,翁正新.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.
[14] Patton R J, Chen J. Review of parity space approaches to fault diagnosis for aerospace systems[J]. Journal of Guidance, Control and Dynamics, 1994,17(2): 278-285.
[15]李令莱,周东华.基于解析模型的非线性系统鲁棒故障诊断方法综述.信息与控制. 2004, 33(4): 451-462.
[16] Beard RV. Failure accommodation in linear systems through self-reorganization, [Dissertation], Report MVT-71-1, Man Vehicle Lab, MIT, Cambridge, Massachusetts, 1971
[17]朱张青.动态系统的鲁棒故障检测与诊断技术[博士论文].南京:南京理工大学. 2005
[18] Mehra R K, Peshon I. An innovations approach to fault detection and diagnosis in dynamic systems[J]. Automatica, 1971 ,7:637-640.
[19] Luenberger D G.. Observers for Multivariable Systems. Contemporary Classics in Engineering and Applied Science. ISI Press, 1986.
[20] Watanabe K, Himmelblau D M. Instrument fault detection in systems with uncertainties [J]. International Journal of Systems Science, 1982 ,13(2):137-158.
[21] Gertler, J.J. Survey of model-based failure detection and isolation in complex plants[J]. IEEE Control systems Magazine, 1988; 8:3-11.
[22] Liu W, Wang H. Linearization techniques in fault diagnosis of nonlinear systems [J]. Journal of systems and control engineering proceedings, 2000,214(4): 241-245.
[23] Wei C, M Saif. An actuator fault isolation strategy for linear and nonlinear systems [C]. In Proceedings of ACC, Portland, 2005: 3321-3326
[24]陈玉东,施颂椒,翁正新.基于线性化技术的非线性系统故障诊断.应用科学学报. 2002,20(4): 365-368.
[25] Xu A, Zhang QH. Nonlinear system fault diagnosis based on adaptive estimation [J]. Automatica, 2004,40:1181-1193.
[26] C. De Persis and A. Isidori. A geometric approach to nonlinear fault detection and isolation[J]. IEEE Trans. on Automatic Control, 2002,46(6): 853–865.
[27] S. Diop, R Martinez-Guerra. On an algebraic and differential approach of nonlinear systems diagnosis [C]. Proc. of 4th IEEE conf. on Decision and Control, Orlando, USA, 2001: 585-589.
[28] Ding X., Frank P. M. Fault detection via factorization approach[J]. systems and Control Letters, 1990, 14(5): 431- 436
[29] Marcin W, Andrzej O, Jozef K. Generic programming based approach to identification and fault diagnosis of nonlinear dynamic systems [J]. Int. J. Control, 2002, 75(13): 1012- 1031.
[30] P. Zhang, S.X. Ding. A simple fault detection scheme for nonlinear systems[C]. Proc. of International symposium on intelligent control, Limassol, Cyprus, 2005: 838-842.
[31] Watanabe K, Himmelblau D M. Incipient fault diagnosis of nonlinear processed with multiple causes of faults [J]. Chemical Engineering Science, 1984 ,39(3):491-508.
[32] Chen J, Patton R.J, and Zhang H Y. Design of unknown input observers and robust fault detection filters [J]. Int. J. Control, 1996, 63: 85- 105.
[33] Yu DL , Shields DN. A bilinear fault detection observer [J]. Automatica, 1996,32(11):1597-1602.
[34] Yu DL,Shields DN. A bilinear fault detection filter [J]. Int. J. Control, 1997, 68(3): 417- 430.
[35] Kinnaert M. Robust fault detection based on observers for bilinear systems [J]. Automatica, 1999,35(11):1829-1842.
[36] Kinnaert M, El Bahir L. Innovation generation for bilinear systems with unknown inputs [A]. Lecture notes in control and information science, London, 1999:445-465.
[37] Zasadzinski M, Magarotto E, Rafaralahy H, et al. Residual generator design for singular bilinear systems subjected to unmeasurable disturbances: an LMI approach [J]. Automatica, 2003,39(4): 703-713.
[38] Padalker S. Real-time fault diagnostic with multiple aspect models [C]. Proc. of the 1991 IEEE international conf. on robotics and automation. Sacramento, Canada. 1991: 803-808.
[39] Shields D.N, Du S. Fault detection observers for continuous nonlinear polynomial systems of general degree [J]. Int. J. Control, 2003, 76(5): 437- 452.
[40] Dan. T, Horak. Failure detection in dynamic systems with modeling errors [A]. J. Guaidance. Novdec,1998: 508-516.
[41] Wei C, M Saif. Fault detection and isolation based on novel unknown input observer design [C]. In Proceedings of ACC, Minnesota,USA, 2006: 5129-5134.
[42] Vemuri A T, Polycarpou M M. Robust nonlinear fault diagnosis in input-ouput systems [J]. Int. J. Control, 1997, 68(2): 343- 360.
[43] Jiang B, Lu S, Wang X. Robust fault diagnosis for a class of nonlinear systems with unknown parameters [J]. Control theory and application, 2001,18(3): 421-426.
[44] V. Kadirkamamathan, P Li, M H Jaward, et al. A sequential Monte Carlo filtering approach to fault detection and isolation in nonlinear systems [C]. Proc. of the 39th IEEE conf. on Decision and Control. Sydney, Australia, 2000: 4335-4340.
[45] Cheng Q, Pramod K V, James M. Distributed fault detection via particle filtering and decision fusion [C]. In Proceedings of 7th international conf. on information fusion, Minnesota, USA, 2005: 1239-1246.
[46]周东华,孙优贤,席裕庚等.一类非线性系统参数偏差型故障的实时检测与诊断.自动化学报, 1993, 19(2): 184-189.
[47]周东华.一类非线性系统的传感器故障检测与诊断新方法.自动化学报,1995, 21(3): 362-365.
[48] Yu D. Fault diagnosis for a hydraulic drive system using a parameter estimation method [J]. Control Engineering Practice. 1997,5(9): 1283-1291.
[49] Gerica E A, Hou Z, Frank P M. FDI based parameter and output estimation: an integrated approach [C]. European Control Conference, Kerlsmhe, Germany,1999: 1125-1137.
[50] Hofling T, Isermann R. A adaptive parity equation and advanced parameter estimation for fault detection and diagnosis[C]. Proc. of IFAC World Congress, San Francisco, USA, 1996:55-60.
[51] Zhang Q. Adaptive Observer for MIMO Linear Time Varying System [J]. Rapprotde Recherche IRISA No.1379, submitted to IEEE Trans. on Automatic Control 2001
[52] Zhou D H, Frank P M. Actuator fault diagnosis of a class of nonlinear systems in closed-loops: a case study [A]. Proc. of international conf. on Control, UK, 1996:311-316.
[53] Kinnaert M. Design of redundancy relations for failure detection and isolation by constrained optimization[J]. International Journal of Control.1996, 63(3): 609-622
[54] Hwang D S, Chang S K and Hsu P L.A practical design for a robust fault detection and isolation system [J]. International Journal of System science.1997, 28(3): 265-275
[55] Gerrler J and Monajemy R. Generating directional residuals with dynamic parity relations[J]. Automatica. 1995,61(2): 395-421
[56] Gertler J and Kunwer M M. Optimal residual decoupling for robust fault diagnosis[J]. International Journal of Control.1995, 61(2): 395-421
[57] Magri J F, Monyon P. On residual generation by observer and parity space approaches [J]. IEEE Trans. On Automatic control. 1994,39(2): 441-447.
[58] Carcia J, Frank P M. On the relationship between observer and parameter identification based approaches to fault detection [A]. Proc. of IFAC world congress. 1996: 25-29.
[59] Gertler J. Diagnosing parametric faults: from parameter estimation to parity relation[C]. America Control Conference, Seattle, USA, 1995: 1615-1620.
[60]宋华,张洪戊等.模糊非线性奇偶方程故障诊断方法.自动化学报,2003, 29(6): 965-970.
[61] Tor F, Tyrone L V, Rahmat S. Optimization based fault detection for nonlinear sytems [A]. Proc of the ACC. Arington,VA, 2001: 1747-1752.
[62] Yu D L. Diagnosing simulated faults for an industrial furnace based on bilinear mode [J]. IEEE Trans on systems technology. 2000,8(3): 435-442.
[63] Gertler J., Singer D. A new structural framework for parity equation based failure detection and isolation[J]. Automatica, 1990, 26: 381-388.
[64] Gertler J. Analytic redundancy methods in fault detection and isolation[C]. IFAC Symposia Series, 1992, 6: 9-21.
[65] Ding S. X., Ding E. L., Jeinsch T. An approach to analysis and design of observer and parity relation based FDI systems[C]. In Proceedings of the 14th IFAC World Congress, Beijing, P. R. China, 1999: 37- 42.
[66] Gertler J. All linear methods are equal - and extendible to (some) nonlinearities[J]. International Journal of Robust and Nonlinear Control, 2002, 12(8): 629– 648.
[67] Sing K N, Zhang P, Ding S. Parity based fault estimation for nonlinear systems: an LMI approach [A]. Proc. of ACC conference, Minnesota, USA, 2006: 5141-5146.
[68] Lei Guo and Wang Hong. Fault detection and diagnosis for general stochastic systems using B-spine expansions and nonlinear filters[J]. IEEE Trans. on Automatic Control. 2005, 52(8): 1644–1652.
[69] Elisa F, Reza O S, Thomas P etc. Distributed fault diagnosis using sensor networks and consensus-based filters[C]. Proceeding of the 45th IEEE conference on Decision and control. San Diego, USA. 2006, 386-391.
[70] Kumamaru K, Hu J, Inoue K and Soderstrom T. Robust Fault detection using index of Kullback discrimination information[C]. Proceeding of IFAC World Congress, San Francisco, USA, 1996, 205-210.
[71]王俊.一种利用子波变换多尺度分辨特性的信号消噪技术.信号处理.1996,12(2): 104-109.
[72]陈涛.小波分析及其在机械诊断中的应用.机械工程学报.1997,33(3): 76-79
[73]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
[74]叶昊,王桂增,方崇智等.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
[75] Yang K, Shan G, Zhao L. Application of Wavelet Packet Analysis and Probabilistic Neural Networks in Fault Diagnosis[C]. Proc of the 6th world congress on intelligent control and automation, Dalian, China, 2006: 4378-4381.
[76]李渭华,萧德云,方崇智.一种基于δ算子的格型故障检测滤波器.自动化学报.1994, 20(4): 413-419.
[77]闻新.故障系统的智能容错控制理论研究与应[博士论文].哈尔滨工业大学. 1995.
[78]颜东.导航、制导系统状态估计方法及容错控制理论研究[博士论文].北京航空航天大学. 1995.
[79]伍学奎,陈进,周铁尘等.基于多指标融合的故障诊断理论与方法.振动工程学报, 1999,12(1):55-63.
[80] Sang W C, In-Beum L. Nonlinear dynamic process monitoring based on dynamic kernel PCA [J]. Chemical Engineering Science, 2004,5898-5908.
[81] Sang W C, Chang L, Jong M L, et al. Fault detection and identification of nonlinear processes based on kernel PCA [J]. chemometrics and intelligent laboratory systems, 2005: 55-67.
[82]丁海生,庄志洪,祝石龙等.混沌、分形和小波理论在被动信号特征提取中的应用.声学学报, 1999, 24(2): 197-203.
[83]萧德云,李渭华.双通道Lattice滤波器及其在故障检测中的应用.控制与决策.1998,13(3): 277-280
[84]萧德云,李渭华,方崇智.一种使用于故障检测德归一化滑动窗协方差格形滤波器.控制理论与应用.1995, 12(2): 230-235
[85]李渭华,萧德云,方崇智.一种基于自适应滑动窗格形滤波算法的故障监测器.自动化学报.1996, 22(2): 251-253
[86]李尔国.故障诊断方法的研究及其应用[博士论文].华东理工大学, 2002.
[87] Jiang J and Jia F.A robust fault diagnosis scheme based on signal modal estimation[J]. International Journal of Control.1995, 62(2): 461-175
[88]吕柏权.一种基于小波网络的故障检测方法.控制理论与应用, 1998,15(5): 802-805.
[89]朱大奇,于盛林.基于知识的故障诊断方法综述.安徽工业大学学报, 2002,19(3): 197-204.
[90]吴今培,肖建华.智能故障诊断与专家系统.北京:科学出版社,1997
[91] Pazzani M J. Failure driven Learning of fault diagnosis heuristics[J]. IEEE Tran. On Systems. Man and Cybernetics. 1987.17(3): 380-384
[92]彭志刚,张纪会,徐心和.基于遗传算法的知识获取及其在故障诊断中的应用研究.信息与控制.1999,28(5):391-395
[93]张雪江,朱向阳,钟秉林等.基于模拟退火演化算法的知识获取方法的研究.控制与决策.1997,12(4): 327-331。
[94]张雪江,朱向阳,钟秉林等.基于退火演化算法的知识获取机制的研究.控制理论与应用.1998,15(1): 93-99。
[95]金宏.导航系统的精度及容错性能研究[博士论文].北京:北京航空航天大学,1998.
[96]杨叔子.基于知识的故障诊断技术.北京:清华大学出版社. 1993
[97]张雪江.机械设备故障诊断系统知识自动获取及更新的研究[博士论文]..南京:东南大学. 1993
[98]吴今培.智能故障诊断与专家系统.北京:科学出版社.1999.
[99]虞和济,陈长征,张省.基于神经网络的智能诊断.北京:冶金工业出版社. 2000.
[100]杨良士.动态系统故障诊断的新方法-专家系统.信息与控制, 1998, 17(5), 26-31.
[101] Pazznai M J. Failure driven learning of fault diagnosis heuristics[J]. IEEE trans. on system, man and cybemetics, 1998, 17(3): 380-384.
[102] Huang C Y, Stengel R F. Failure model determination is a knowledge based control system[C].. Proc. of American Control Conference, USA, 1987, 1642-1649.
[103] Youn W C, Hammen J M. Aiding the operation during novel fault diagnosis[J]. IEEE trans. on system, man and cybemetics, 1998, 18(1): 142-147.
[104]张雪江.汽轮发电机组故障诊断专家系统知识处理技术的研究.振动工程学报, 1996, 9(3): 230-136.
[105]王飓舵.基于专家系统和神经网络的机车电路故障诊断系统研究.北方交通大学学报. 1996, 20(4): 495-501.
[106]魏永军,胡光锐,汪亚平.一种新的故障诊断专家系统.上海交通大学学报.1998,32(6): 137-139
[107]黄洪钟.模糊机械科学与技术.机械工程学报.1996,32(3):1-8
[108] Sauter D, Mary N, Sirou F and Thieltgen A. Fault Diagnosis in systems using fuzzy logic[C]. Proceeding of 3rd IEEE Conference on Control applications. Glasgow, UK, 883-888
[109] Schneider H. and Frank P M. Fuzzy logic based threshold adaption for fault detection in robots[C]. Proceeding of 3rd.IEEE Conference on control applications. Glasgow, UK, 1127-1132
[110] Vachkov G.. Identification for fuzzy rule based system for fault diagnosis in chemical plants[C]. Proceeding IFAC Symp. on On-line Fault Detection and Supervision in the Chemical Process Industries. Newark, Delaware, USA, 315-318
[111]方敏,陈雁翔.基于神经网络的故障诊断方法的研究.北京航空航天大学学报, 1998, 15(3): 460-465.
[112] Maruyama N, Benouarets M and Dexter A L. Fuzzy model-based fault detection and diagnosis[C]. Proceeding of IFAC World Congress, San Francisco, USA. 1996,121-126
[113] Frank P M and Kiupel N. Residual evaluation for fault diagnosis using adaptive thresholds and fuzzy inference[J]. Proceeding of IFAC World Congress, San Francisco, USA.1996, 115-120
[114] Kiran K V, Joanne B D. Automatic systhesis of fault trees for computer-based systems [J]. IEEE Trans on Reliability, 1999,48(4): 392-401.
[115] Juan A C. An algorithm to find minimal cuts of coherent fault-trees with event-classes[J]. IEEE trans. on reliability. 1999, 48(1): 31-41.
[116] Cros X E, Lowden D W. Bayesian approach to NDT data fusion[J]. Non-destructive testing and condition monitoring. 1995, 37(5): 462-468.
[117] Luo R C, Key M G. Multisensor integration and fusion in intelligent system[J]. IEEE trans. on system, man and cybemetics, 1989, 19(5): 901-931.
[118] Bogler P L. Shafer-dempster reasoning with application to multisensor target identification system[J]. IEEE trans. on system, man and cybemetics, 1987, SMC-17: 968-977.
[119]韩静,陶云刚.基于D-S证据理论和模糊数学的多传感器数据融合方法.仪器仪表学报, 2000, 21(6): 644-647.
[120]徐从富,耿卫东,潘云鹤.面向数据融合的DS方法综述.电子学报, 2001, 29(3): 393-396.
[121]朱大奇,于盛林.基于证据理论的电机故障诊断方法研究.华中科技大学学报, 2001, 12(3): 58-61.
[122]罗志增,蒋静萍.应用模糊信息融合实现目标物的分类.仪器仪表学报, 1999, 20(4): 401-404.
[123]覃祖旭.智能、信息及其在导航系统中的应用[博士论文].北京:北京航空航天大学. 1995.
[124]何友.多传感器信息融合及其应用.北京:电子工业出版社. 2000
[125]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, 12: 87-92.
[126]王建波,于达仁.液体火箭发动机故障诊断的信息融合技术.航空动力学报, 2001, 16(1):38-40.
[127] Abide M A, Conzalez R C. Data fusion in robotics and machine intelligence. American Academic press, 1992.
[128] Luo R C, Scherp R S. Dynamic multisensor data fusion system for intellegient robots[J]. Journal of robotics and automation. 1998, 4(4): 386-396.
[129] Muid M, Vachtsevanos G. Automated fault detection and identification using a fuzzy-wavelet analyais technique[C]. Proc. of Autotestcon by IEEE. 1995, 169-175.
[130] Tzafestas S G and Dalianis P J. Fault diagnosis in complex systems using artificial neural networks[C]. Proceeding of the IEEE Conf. On Control Applications.1994,2: 877-882
[131] Ungar L H. Adaptive networks for fault diagnosis and process control[J]. Computers Chemical Engineering. 1990,14:561-573
[132] Goode P V, Chow M Y. Hybrid fuzzy/neural system used to extract heuristic knowledge from a fault detection problem [J]. IEEE International conference on Fuzzy systems, 1994, 1731-1736.
[133] Mihiar S, Isermann R. Neuro-fuzzy systems for diagnosis[J]. Fuzzy Sets and Systems,1997, 89(3): 289-307.
[134] Wang H. Detecting and diagnosing saturation faults [J]. IEE conference publication,1996: 809-813.
[135] Zhou J, Bennett. Adaptive error compensation for fault detection [J]. International journal of system science, 1998, 29(1): 57-64.
[136] M.M Polycarpou and A.B. Trunov Learning methodology for failure detection and accommodation[J]. IEEE Control systems, 1995, 45(4): 16-25.
[137] Polycarpou M M, Heimichi A J. Automated fault detection and accommodation: a learning systems approach[J]. IEEE Transactions on Systems, Man and Cybernetics, 1995, 25(11): 1447-1458
[138] Polycarpou M, Trunov A. Learning approach to nonlinear fault diagnosis: Detectability analysis[J]. IEEE Transactions on Automatic Control, 2000, 45(4): 806-812
[139] Polycarpou M M. Fault accommodation of a class of multivariable nonlinear dynamical systems using a learning approach [J]. IEEE Transactions on Automatic Control, 2001, 46(5): 756-742
[140] A.T. Vemuri. Sensor bias fault diagnosis in a class of nonlinear systems[J]. IEEE Trans. on Automatic Control, 2001, 46(6): 949–954.
[141] X.D. Zhang, T. Parisini, and M.M Polycarpou. Sensor bias fault isolation in a class of nonlinear systems[J]. IEEE Trans. on Automatic Control, 2005, 50(3): 370–376.
[142] X.D. Zhang, M.M Polycarpou and T. Parisini. A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems [J]. IEEE Trans. on Automatic Control, 2002, 47(4): 576–593.
[143] R. R. Selmic, M. M. Polycarpou, and T. Parisini. Actuator fault detection in nonlinear uncertain systems using neural on-line approximation models [J]. Proc. Of the 2006 ACC, Minnestoa, USA, 2006: 5123-5128.
[144] Gomm J B. On-line learning for fault classification using an adaptive neuro-fuzzy network[C]. Proceeding of IFAC World Congress, San Francisco, USA, 1996,175-180
[145]李捷辉.RBF神经网络用于发动机控制系统的故障诊断.江苏理工大学学报(自然科学版).1999,20(5):36-39
[146]武研,施鸿宝.一种模糊规则动态调整BP算法中参数的方法.计算机研究与发展.1998,35(8):659-693
[147]李绍滋,李堂秋,周风利等.一种新型BP算法及其在故障诊断专家系统中的应用.厦门大学学报(自然科学版).1999,38(2): 186-192
[148]施洪宝,王秋荷.专家系统.西安:西安交通大学出版社,1990
[149]吴军强,梁军.基于图论的故障诊断技术及其发展.机电工程, 2003,20(5): 188-190.
[150]邓聚龙.灰色控制系统.武汉:华中理工大学,1989
[151]吴祖堂,李岳,温熙森.灰关联分析在机械设备故障诊断中的应用.系统工程理论与实践.1999,6:126-132
[152]吕锋.灰色系统关联度之分辨系数的研究.系统工程理论与实践.1997,17(6):49-54.
[153]施国洪,姚冠新.灰色系统理论在故障诊断决策中的应用.系统工程理论与实践.2001,4:120-123
[154]张建华,王占林.基于模糊神经网络的故障诊断方法的研究.北京航空航天大学学报.1997,23(4):502-506
[155] Thomas Pfeufer, Mihiar Ayoubi. Application of a hybrid neuro-fuzzy system to the fault diagnosis of an automotive electromechanical actuator [J]. Fuzzy sets and systems. 1997,89:351-360
[156]陈小平.进化计算及其应用研究[博士论文].南京:南京航空航天大学. 2001.
[157]吴伟蔚,扬叔子.故障诊断Agent研究.振动工程学报, 2000, 13(3): 393-399.
[158] Zhong M.Y, Ye Tao, Chen G.Y., Wang G.Z. An ILMI approach to RFDF for uncertainty linear systems with nonlinear perturbations[J]. ACTA AUTOMATICA SINICA. 2005,31(2): 297-300.
[159]彭涛,桂卫华等.一种故障检测滤波器的多目标优化设计方法.控制与决策. 7(2005): 773-777.
[160]钟麦英,汤兵勇等.状态时滞系统故障诊断问题的LMI方法研究.控制与决策. 17(2002): 15-18.
[161]白雷石,田作华等.基于时滞依赖H∞滤波器的时滞系统故障诊断.控制与决策. 20(2005): 1012-1016.
[162] Wang Y, Xie L, De Souza. Robust control for a class of uncertain nonlinear systems[J]. System Control Letter, 1992, 19(12): 139–149.
[163] YANG H.L MEHRDAD S. Oberver design and fault diagnosis for state-retarded dynamic systems[J]. Automatica, 1998,34(2): 217-227.
[164] W. Chen, M Saif. An iterative learning observer for fault detection and accommodation in nonlinear time-delay systems[J]. Int. J. robust and nonlinear conl. 2006, 16: 1-19
[165]谢胜利.迭代学习控制的理论与应用.北京:科学出版社,2004
[166] Shengli. Xie, Senping Tian, Yuli FuD. A new algorithm of iterative learning control with forgetting factors[C]. IFAC Control Eng. Prac.,2004;5: 625–630.
[167] Zhang P, Ding S X. A simple fault detection scheme for nonlinear systems[C]. Proc. of the 2005 IEEE International Symposium on intelligent control. Limassol, Cyprus. 2005: 838–842.
[168]韩京清.自抗扰控制器及其应用.控制与决策, 1998, 13(1):19-23
[169]韩京清.一类不确定对象的扩张状态观测器.控制与决策, 1995, 10(1):85-88
[170]黄一,韩京清.非线性连续二阶扩张状态观测器的分析与设计.科学通报, 2000, 45(13):1373-1378
[171]韩京清,张荣.二阶扩张状态观测器的误差分析.系统科学与数学, 1999, 19(4):465-471
[172] Riccardo M. G., Thomas Parisini, Marios M. Polycarpou. Distributed Fault Diagnosis With Overlapping Decompositions: An Adaptive Approximation Approa [J]. IEEE Trans. on Automatic Control. 2009, 54(4): 794–799.
[173] B. jiang, M. staroswiecki, V. Cocquempot. Fault accommodation for nonlinear dynamic systems. IEEE Trans. on Automatic Control, 2006, 51(9): 1578–1583.