数据驱动的多变量控制系统性能监测与诊断
|
摘要
工业过程系统日趋复杂化与精细化,从而使得控制系统日益复杂,控制性能要求更为严格。构建复杂系统往往需要高昂成本,需要长时间稳定服役才能保证投入的经济回报。另外复杂性会导致故障产生的可能性增加,一旦出现异常,可能会产生严重的事故和经济损失。异常事件管理,包括性能监控和故障的诊断成为现代控制系统中不可或缺的组成部分。
针对多变量系统,多变量统计过程控制(MSPC)已经有一些工具和算法,但是对于耦合性以及有严重非线性的系统,故障诊断和传播分析仍然是一个开放性领域。本文就复杂工业过程的控制性能监控与诊断领域中的几个关键问题做了研究,主要内容如下：
1,经典的PCA算法无法灵敏的检测出系统的细微故障,导致它不能对故障进行早期预警。为此,本文结合统计局部方法改进了基于PCA的性能监控方法。针对PCA分解后的残差空间,提出了一个新的监控统计量使得它能更好的适用于数据协方差阵奇异的情况,提高了该方法在数值计算中的鲁棒性。本文经过详尽的分析从理论上证明了改进的PCA算法相对于普通PCA算法对于性能监控具有更好的敏感性。同时,针对基于历史数据基准的性能评估,提出了一种直接分析改进残差均值的性能评估方法,可以在检测到系统性能发生变化后进一步分析各个特征方向上的性能变化趋势。
2,提出一种新的因果测度方法并用于大规模控制系统故障信号的传播路径分析。扰动或者振荡信号会由于厂级回路的耦合进行传播扩散,进而影响到整个生产过程。本文结合因果分析的概念和时间序列的排序处理方法,提出用于检测时间序列耦合强度和方向的符号化有向互信息。将时间序列经过大小排列进行编码,得到符号化序列。计算该对序列引入时间错位的互信息,称为有向符号化互信息,作为测度两个序列耦合强度和方向的因果指数为有向符号化互信息之差。通过因果指数可以计算变量两两耦合的强度和方向,进而可以分析多变量的信息传播途径。
3,提出了针对线性多变量模型预测控制(MPC)系统的模型失配检测与定位的方法。模型失配是引起MPC性能下降的主要因素之一。针对现有MPC系统的双层结构,首先对多变量预测控制层采用子空间辨识方法检测出整体的模型失配。针对基础控制层,提出两种可选的模型失配检测方案。一种是在设定点施加外部激励信号,通过计算激励信号与模型预测残差的相关系数得到适合于统计局部方法的残差信号。另一种非侵入式的模型失配检测无需外部激励信号,根据ARMA模型中AR部分参数的辅助辨识方法构建残差,使之适用于统计局部方法,进而检测模型失配。以上两种方法均可区分过程模型和扰动模型的改变
4,针对非线性多变量系统,提出基于互信息的模型失配检测与分析方法。现有的Kalman滤波方法和相关性分析方法均不适用于非线性多变量系统的模型失配检测。互信息可以作为一种广义的随机变量相关性测度工具,反映了两个随机变量的相互依赖性。通过引入信息流的概念,可以有效地分析外部激励信号在系统中的信息流向与模型误差的关系,进而利用激励信号与模型预测残差的互信息分析并定位多变量系统中产生模型失配的子系统。
Nowadays, industrial process control system become more and more complex and highly sophisticated. Their stability and the reliability of instruments are also enhanced by the progress of advanced technology. But on the other hand, they become frailer than the simple but robust ones. For one thing, long term smooth running have to be guaranteed for return of heavy investment. For the other, large-scale plants have more chance to suffer faults or disturbances and hence fail. Usually, for large-scale plant, faults can propagate through the plant and affect a large number of variables which may lead serious disaster.
For performance monitoring of multivariate systems, multivariate statistical process control (MSPC) has been well developed and lots of algorithms such as PC A, PLS and ICA have been sufficiently studied. In the past four decades, large literature for linear and nonlinear system emerged in the discipline of fault detection and diagnosis (FDD). But for plant-wide level, especially analysis of fault propagation and root cause diagnosis, it is still open for researchers. Starting with fault detection using PCA, in this thesis we propose a comprehensive framework of performance monitoring and diagnosis. Several key issues are studied and the main contributions are as follows:
1. Among the methods of MSPC, PCA attracts much attention for its conceptually simple and dealing with recorded data directly. But it is only appropriate for identical and independent (i.i.d.) Gaussian variables which is a very restrict assumption in practical application. Furthermore, it is proved that it may not detect incipient changes in the variable covariance structure nor changes in the geometry of the underlying variable decomposition. Therefore it can not be used as a early warning tool of process. An improved PCA algorithm is proposed by integrating statistical local approach into PCA to settle this problem. A new monitoring statistic for the residual space is developed which can successfully deal with the case of zero eigenvalues. The new statistic can improve the robustness of numerical computation and decrease false alarm rate. The statistical properties of the residuals and also the monitoring statistics are well analyzed. It is proved that the improved PCA algorithm is more sensitive for different kinds of fault than the proper PCA. A new performance assessment method is proposed based on the analysis of improved residual directly. This simple but effective method can assess the performance trend in every eigen-directions.
2. Method for identifying the propagation path of plant-wide faults is proposed. The problem is solved by two methods. For variables which can be analyzed through time series analysis, an ordinal point of view is introduced. A certian length of data points are coded as a symbol. By sliding the data vector, a new symbolic sequence corresponding to the original time series is derived. The statistical and relative analysis is based on the new symbolic sequence. Causality is a well established conception in physics and economics by Wiener and Granger. Its nonlinear extension is proposed by Schreiber as the transfer entropy. Based on the thought of causality analysis, a new measure of causality named directional symbolic mutual information is proposed. It has the property of robustness, conceptual simplicity and fast computational speed. A new causality index is then stated by subtracting the directional symbolic mutual information and its inversed counterpart. Coupling strength and direction of process variables are measured by the causality index.
3. As a key factor in model-based control technique, model fidelity has significant influence on control performance. Model-Plant-Mismatch (MPM) detection is an important step in the procedure of control performance monitoring and system maintenance. For the majority of existing APC systems, two layers containing the MPC in the upper layer and the basic layer, e.g. PID control, are employed. All the methods are based on the use of statistical local approach and insensitive to the change of disturbance dynamics. The key step of local approach is finding a primary residual that sensitive to the fault concerned. First, residuals are derived using sub-space identification for MIMO system in the MPC layer. For the basic control layer, two methods are proposed. One need dithering signal in the set-point and the correlation between dithering signals and model residuals is used to construct primary residual. The other is non-invasive and the primary residual is obtained by using instrumental variable identification method.
4. The existing MPM detection methods based on Kalman filter and correlation analysis are no longer efficient for nonlinear systems. To tackle this issue, mutual information as a general correlation measure is introduced. Mutual information, which is a well known concept in information theory can reflect the dependence of two stochastic variables, no matter nonlinear or linear. The conception of information flow in systems is introduced to analyze the relationship between the information transfer of exciting signals and model error. This method can help users locate the sub-system that has model-plant-mismatch. The estimation of mutual information, as well as the surrogate method to determine threshold are introduced.
针对多变量系统,多变量统计过程控制(MSPC)已经有一些工具和算法,但是对于耦合性以及有严重非线性的系统,故障诊断和传播分析仍然是一个开放性领域。本文就复杂工业过程的控制性能监控与诊断领域中的几个关键问题做了研究,主要内容如下：
1,经典的PCA算法无法灵敏的检测出系统的细微故障,导致它不能对故障进行早期预警。为此,本文结合统计局部方法改进了基于PCA的性能监控方法。针对PCA分解后的残差空间,提出了一个新的监控统计量使得它能更好的适用于数据协方差阵奇异的情况,提高了该方法在数值计算中的鲁棒性。本文经过详尽的分析从理论上证明了改进的PCA算法相对于普通PCA算法对于性能监控具有更好的敏感性。同时,针对基于历史数据基准的性能评估,提出了一种直接分析改进残差均值的性能评估方法,可以在检测到系统性能发生变化后进一步分析各个特征方向上的性能变化趋势。
2,提出一种新的因果测度方法并用于大规模控制系统故障信号的传播路径分析。扰动或者振荡信号会由于厂级回路的耦合进行传播扩散,进而影响到整个生产过程。本文结合因果分析的概念和时间序列的排序处理方法,提出用于检测时间序列耦合强度和方向的符号化有向互信息。将时间序列经过大小排列进行编码,得到符号化序列。计算该对序列引入时间错位的互信息,称为有向符号化互信息,作为测度两个序列耦合强度和方向的因果指数为有向符号化互信息之差。通过因果指数可以计算变量两两耦合的强度和方向,进而可以分析多变量的信息传播途径。
3,提出了针对线性多变量模型预测控制(MPC)系统的模型失配检测与定位的方法。模型失配是引起MPC性能下降的主要因素之一。针对现有MPC系统的双层结构,首先对多变量预测控制层采用子空间辨识方法检测出整体的模型失配。针对基础控制层,提出两种可选的模型失配检测方案。一种是在设定点施加外部激励信号,通过计算激励信号与模型预测残差的相关系数得到适合于统计局部方法的残差信号。另一种非侵入式的模型失配检测无需外部激励信号,根据ARMA模型中AR部分参数的辅助辨识方法构建残差,使之适用于统计局部方法,进而检测模型失配。以上两种方法均可区分过程模型和扰动模型的改变
4,针对非线性多变量系统,提出基于互信息的模型失配检测与分析方法。现有的Kalman滤波方法和相关性分析方法均不适用于非线性多变量系统的模型失配检测。互信息可以作为一种广义的随机变量相关性测度工具,反映了两个随机变量的相互依赖性。通过引入信息流的概念,可以有效地分析外部激励信号在系统中的信息流向与模型误差的关系,进而利用激励信号与模型预测残差的互信息分析并定位多变量系统中产生模型失配的子系统。
Nowadays, industrial process control system become more and more complex and highly sophisticated. Their stability and the reliability of instruments are also enhanced by the progress of advanced technology. But on the other hand, they become frailer than the simple but robust ones. For one thing, long term smooth running have to be guaranteed for return of heavy investment. For the other, large-scale plants have more chance to suffer faults or disturbances and hence fail. Usually, for large-scale plant, faults can propagate through the plant and affect a large number of variables which may lead serious disaster.
For performance monitoring of multivariate systems, multivariate statistical process control (MSPC) has been well developed and lots of algorithms such as PC A, PLS and ICA have been sufficiently studied. In the past four decades, large literature for linear and nonlinear system emerged in the discipline of fault detection and diagnosis (FDD). But for plant-wide level, especially analysis of fault propagation and root cause diagnosis, it is still open for researchers. Starting with fault detection using PCA, in this thesis we propose a comprehensive framework of performance monitoring and diagnosis. Several key issues are studied and the main contributions are as follows:
1. Among the methods of MSPC, PCA attracts much attention for its conceptually simple and dealing with recorded data directly. But it is only appropriate for identical and independent (i.i.d.) Gaussian variables which is a very restrict assumption in practical application. Furthermore, it is proved that it may not detect incipient changes in the variable covariance structure nor changes in the geometry of the underlying variable decomposition. Therefore it can not be used as a early warning tool of process. An improved PCA algorithm is proposed by integrating statistical local approach into PCA to settle this problem. A new monitoring statistic for the residual space is developed which can successfully deal with the case of zero eigenvalues. The new statistic can improve the robustness of numerical computation and decrease false alarm rate. The statistical properties of the residuals and also the monitoring statistics are well analyzed. It is proved that the improved PCA algorithm is more sensitive for different kinds of fault than the proper PCA. A new performance assessment method is proposed based on the analysis of improved residual directly. This simple but effective method can assess the performance trend in every eigen-directions.
2. Method for identifying the propagation path of plant-wide faults is proposed. The problem is solved by two methods. For variables which can be analyzed through time series analysis, an ordinal point of view is introduced. A certian length of data points are coded as a symbol. By sliding the data vector, a new symbolic sequence corresponding to the original time series is derived. The statistical and relative analysis is based on the new symbolic sequence. Causality is a well established conception in physics and economics by Wiener and Granger. Its nonlinear extension is proposed by Schreiber as the transfer entropy. Based on the thought of causality analysis, a new measure of causality named directional symbolic mutual information is proposed. It has the property of robustness, conceptual simplicity and fast computational speed. A new causality index is then stated by subtracting the directional symbolic mutual information and its inversed counterpart. Coupling strength and direction of process variables are measured by the causality index.
3. As a key factor in model-based control technique, model fidelity has significant influence on control performance. Model-Plant-Mismatch (MPM) detection is an important step in the procedure of control performance monitoring and system maintenance. For the majority of existing APC systems, two layers containing the MPC in the upper layer and the basic layer, e.g. PID control, are employed. All the methods are based on the use of statistical local approach and insensitive to the change of disturbance dynamics. The key step of local approach is finding a primary residual that sensitive to the fault concerned. First, residuals are derived using sub-space identification for MIMO system in the MPC layer. For the basic control layer, two methods are proposed. One need dithering signal in the set-point and the correlation between dithering signals and model residuals is used to construct primary residual. The other is non-invasive and the primary residual is obtained by using instrumental variable identification method.
4. The existing MPM detection methods based on Kalman filter and correlation analysis are no longer efficient for nonlinear systems. To tackle this issue, mutual information as a general correlation measure is introduced. Mutual information, which is a well known concept in information theory can reflect the dependence of two stochastic variables, no matter nonlinear or linear. The conception of information flow in systems is introduced to analyze the relationship between the information transfer of exciting signals and model error. This method can help users locate the sub-system that has model-plant-mismatch. The estimation of mutual information, as well as the surrogate method to determine threshold are introduced.
引文
[1]Isermann, R.,Balle, P., Trends in the application of model-based fault detection and diagnosis of technical processes[J]. Control Engineering Practice, May,1997,5 (5),pp 709-719.
[2]Nimmo, I., Adequately address abnormal operations[J]. Chemical Engineering Progress, Sep,1995,91 (9),pp 36-45.
[3]Desborough, L.,Miller, R. Increasing customer value of industrial control performance monitoring-Honeywell's experience [A], In 2002; pp 169-189.
[4]Paulonis, M. A.,Cox, J. W., A practical approach for large-scale controller performance assessment, diagnosis, and improvement[J]. Journal of Process Control, Mar,2003,13 (2),pp 155-168.
[5]Yamanaka, F.,Nishiya, T., Application of the intelligent alarm system for the plant operation[J]. Computers & Chemical Engineering,1997,21 pp S625-S630.
[6]Venkatasubramanian, V.,Rengaswamy, R.,Kavuri, S. N., etc., A review of process fault detection and diagnosis Part Ⅲ:Process history based methods[J]. Computers & Chemical Engineering, Mar,2003,27 (3),pp 327-346.
[7]Willsky, A. S., A survey of design methods for failure detection in dynamic systems[J]. Automatica,1976,12 (6),pp 601-611.
[8]Shewhart, W. A., Economic Control of Quality of Manufactured Product[M]. N.J.: Van Nostrand Reinhold, Princeton:1931.
[9]Qin, S. J., Statistical process monitoring:basics and beyond[J]. Journal of Chemometrics, Aug-Sep,2003,17 (8-9),pp 480-502.
[10]Astrom, K. J., Introduction to stochastic control theory[M]. New York:Academic Press:1970.
[11]Harris, T. J., Assessment of control loop performance[J]. The Canadian Journal of Chemical Engineering,1989,67 (5),pp 856-861.
[12]Page, E., Continuous inspection schemes[J]. Biometrika,1954,41 (1/2),pp 100-115.
[13]Roberts, S., Control chart tests based on geometric moving averages[J]. Technometrics,1959, pp 239-250.
[14]Hunter, J. S., The exponentially weighted moving average[J]. Jourla of Quality Technology,1986,18 (4),pp 203-210.
[15]Lucas, J. M.,Saccucci, M. S., Exponentially weighted moving average control schemes:properties and enhancements[J]. Technometrics,1990, pp 1-12.
[16]Page, E., Cumulative sum charts[J]. Technometrics,1961, pp 1-9.
[17]Hawkins, D. M., Cumulative sum control charting:an underutilized SPC tool[J]. Quality Engineering,1993,5 (3),pp 463-477.
[18]Hawkins, D. M.,Olwell, D. H., Cumulative sum charts and charting for quality improvement[M]. New York:Springer Verlag:1998.
[19]MacGregor, J. F., Using On-Line Process Data to Improve Quality: Challenges for Statisticians[J]. International Statistical Review,1997,65 (3),pp 309-323.
[20]Pearson, K., On lines and planes of closest fit to systems of points in space[J]. Philosophical Magazine, Jul-Dec,1901,2 (7-12),pp 559-572.
[21]Duda, R. O.,Hart, P. E., Pattern classification and scene analysis[M]. New York:John Wiley & Sons:1973.
[22]Hyvarinen, A.,Karhunen, J.,Oja, E., Independent component analysis[M]. New York:John Wiley & Sons:2001.
[23]Wold, H., Estimation of principal components and related models by iterative least squares[J]. Multivariate analysis,1966,1 pp 391-420.
[24]Wold, H., Nonlinear estimation by iterative least squares procedures[J]. Research papers in statistics,1966, pp 411-444.
[25]Hotelling, H., The most predictable criterion[J]. Journal of Educational Psychology,1935,26 pp 139-142.
[26]Hotelling, H., Relations between two sets of variates[J]. Biometrika, Dec, 1936,28 pp 321-377.
[27]Anderson, T. W., Estimating linear restrictions on regression coefficients for multivariate normal distributions[J]. Annals of Mathematical Statistics,1951,22 (3),pp 327-351.
[28]van den Wollenberg, A. L., Redundancy analysis an alternative for canonical correlation analysis[J]. Psychometrika,1977,42 (2),pp 207-219.
[29]Macgregor, J. F.,Jaeckle, C.,Kiparissides, C., etc., Process monitoring and diagnosis by multiblock PLS methods[J]. AIChE Journal, May,1994,40 (5),pp 826-838.
[30]Wangen, L.,Kowalski, B., A multiblock partial least squares algorithm for investigating complex chemical systems[J]. Journal of Chemometrics,1989,3 (1),pp 3-20.
[31]Qin, S. J.,Valle, S.,Piovoso, M. J., On unifying multiblock analysis with application to decentralized process monitoring[J]. Journal of Chemometrics, Oct, 2001,15 (9),pp 715-742.
[32]Wang, X.,K.ruger, U.,Lennox, B., Recursive partial least squares algorithms for monitoring complex industrial processes[J]. Control Engineering Practice, Jun, 2003,11 (6),pp 613-632.
[33]Westerhuis, J. A.,Kourti, T.,MacGregor, J. F., Analysis of multiblock and hierarchical PCA and PLS models[J]. Journal of Chemometrics, Sep-Oct,1998,12 (5),pp 301-321.
[34]Jackson, J. E.,Mudholkar, G. S., Control Procedures for Residuals Associated with Principal Component Analysis[J]. Technometrics,1979,21 (3),pp 341-349.
[35]Akaike, H. Information theory and an extension of the maximum likelihood principle[A], In Petrov,Caski,2nd International Symposium on Information Theory[C],1974; pp 267-281.
[36]Malinowski, E. R., Determination of the number of factors and the experimental error in a data matrix[J]. Analytical Chemistry,1977,49 (4),pp 612-617.
[37]Rissanen, J., Modeling by shortest data description[J]. Automatica,1978,14 (5),pp 465-471.
[38]Malinowski, E. R., Factor Analysis in Chemistry[M]. New York: Wiley-Interscience:1991.
[39]Cattell, R. B., The scree test for the number of factors[J]. Multivariate Behavioral Research,1966, (April),pp 245-276.
[40]Rozett, R. W.,Petersen, E. M., Methods of factor analysis of mass spectra[J]. Analytical Chemistry,1975,47 (8),pp 1301-1308.
[41]Kaiser, H. F., The application of electronic computers to factor analysis[J]. Euducational Psychological Measure,1960,20 (1),pp 141-151.
[42]Zwick, W. R.,Velicer, W. F., Comparison of five rules for determining the number of components to retain[J]. Psychological Bulletin, May,1986,99 (3),pp 432-442.
[43]Shrager, R. I.,Hendler, R. W., Titration of individual components in a mixture with resolution of difference spectra, pKs, and redox transitions[J]. Analytical Chemistry,1982,54 (7),pp 1147-1152.
[44]Wold, S., Cross validatory estimation of the number of components in factor and principal components analysis[J]. Technometrics,1978,20 (4),pp 397-405.
[45]Carey, R. N.,Wold, S.,Westgard, J. O., Principal component analysis:an alternative to referee methods in method comparison studies[J]. Analytical Chemistry, 1975,47(11),pp 1824-1829.
[46]Qin, S. J.,Dunia, R., Determining the number of principal components for best reconstruction[M].1999; p 357-362.
[47]Qin, S. J.,Dunia, R., Determining the number of principal components for best reconstruction[J]. Journal of Process Control, Apr-Jun,2000,10 (2-3),pp 245-250.
[48]Valle, S.,Li, W. H.,Qin, S. J., Selection of the number of principal components:The variance of the reconstruction error criterion with a comparison to other methods[J]. Industrial & Engineering Chemistry Research, Nov,1999,38 (11),pp 4389-4401.
[49]Morud, T. E., Multivariate statistical process control; Example from the chemical process industry[J]. Journal of Chemometrics, Sep-Dec,1996,10 (5-6),pp 669-675.
[50]Kruger, U.,Chen, Q.,Sandoz, D. J., etc., Extended PLS approach for enhanced condition monitoring of industrial processes[J]. AIChE Journal, Sep,2001, 47 (9),pp 2076-2091.
[51]Geladi, P., PLS model building:theory and application[J]. Chemometrics and Intelligent Laboratory Systems, Nov,1988,5(1),pp 2-3.
[52]王惠文,偏最小二乘回归方法及其应用[M].国防工业出版社：1999.
[53]费正顺.基于PLS框架的动态优化方法研究[D].杭州：浙江大学2012.
[54]Geladi, P.,Kowalski, B. R., Partial least-squares regression:a tutorial[J]. Analytica Chimica Acta,1986,185 pp 1-17.
[55]de Jong, S., SIMPLS:an alternative approach to partial least squares regression [J]. Chemometrics and Intelligent Laboratory Systems,1993,18 (3),pp 251-263.
[56]Huang, B.,Shah, S. L., Performance assessment of control loops:theory and applications[M]. Springer Verlag:1999.
[57]Ordys, A. W.,Uduehi, D.,Johnson, M. A., etc., Process control performance assessment:from theory to implementation[M]. Springer Verlag:2007.
[58]Astrom, K., Computer control of a paper machine—An application of linear stochastic control theory [J]. IBM Journal of research and development,1967,11 (4),pp 389-405.
[59]Huang, B.,Shah, S.,Kwok, E., Good, bad or optimal? Performance assessment of multivariable processes[J]. Automatica,1997,33 (6),pp 1175-1183.
[60]Harris, T.,Boudreau, F.,MacGregor, J., Performance assessment of multivariable feedback controllers[J].Automatica,1996,32 (11),pp 1505-1518.
[61]Ko, B. S.,Edgar, T. F., Performance assessment of multivariable feedback control systems[J]. Automatica,2001,37 (6),pp 899-905.
[62]Huang, B.,Ding, S. X.,Thornhill, N., Practical solutions to multivariate feedback control performance assessment problem:reduced a priori knowledge of interactor matrices[J]. Journal of Process Control,2005,15 (5),pp 573-583.
[63]Shah, S. L.,Patwardhan, R.,Huang, B., Multivariate controller performance analysis:methods, applications and challenges[J]. AICHE Symposium Series,2002, 98 (326),pp 190-207.
[64]Qin, S. J.,Yu, J., Recent developments in multivariable controller performance monitoring[J]. Journal of Process Control, Mar,2007,17 (3),pp 221-227.
[65]Qin, S. J., Control performance monitoring-a review and assessment[J]. Computers & Chemical Engineering, Dec,1998,23 (2),pp 173-186.
[66]Huang, B.,Shah, S. L.,Miller, R., Feedforward plus feedback controller performance assessment of MIMO systems[J]. IEEE Transactions on Control Systems Technology, May,2000,8 (3),pp 580-587.
[67]Desborough, L.,Harris, T., Performance Assessment Measures for Univariate Feedforward Feedback-Control [J]. Canadian Journal of Chemical Engineering, Aug, 1993,71 (4),pp 605-616.
[68]Tyler, M. L.,Morari, M., Performance monitoring of control systems using likelihood methods[J]. Automatica, Aug,1996,32 (8),pp 1145-1162.
[69]Huang, B., Minimum variance control and performance assessment of time-variant processes[J]. Journal of Process Control,2002,12 (6),pp 707-719.
[70]Olaleye, F. B.,Huang, B.,Tamayo, E., Feedforward and feedback controller performance assessment of linear time-variant processes[J]. Industrial & Engineering Chemistry Research, Jan,2004,43 (2),pp 589-596.
[71]Ko, B. S.,Edgar, T. F., Performance assessment of cascade control loops[J]. AIChE Journal, Feb,2000,46 (2),pp 281-291.
[72]McNabb, C. A.,Qin, S. J., Projection based MIMO control performance monitoring:Ⅱ-measured disturbances and setpoint changes[J]. Journal of Process Control, Feb,2005,15 (1),pp 89-102.
[73]Thornhill, N. F.,Cox, J. W.,Paulonis, M. A., Diagnosis of plant-wide oscillation through data-driven analysis and process understanding[J]. Control Engineering Practice, Dec,2003,11 (12),pp 1481-1490.
[74]Horch, A.Isaksson, A. J., A modified index for control performance assessment[J]. Journal of Process Control, Dec,1999,9 (6),pp 475-483.
[75]Huang, B.,Shah, S. L., Practical issues in multivariable feedback control performance assessment[J]. Journal of Process Control, Oct-Dec,1998,8 (5-6),pp 421-430.
[76]Desborough, L.,Harris, T., Performance assessment measures for univariate feedback control[J]. Canadian Journal of Chemical Engineering, Dec,1992,70 (6),pp 1186-1197.
[77]Thornhill, N. F.,Oettinger, M.,Fedenczuk, P., Refinery-wide control loop performance assessment[J]. Journal of Process Control, Apr,1999,9 (2),pp 109-124.
[78]Gao, H.,Patwardhan, R.,Akamatsu, K., etc., Performance evaluation of two industrial MPC controllers[J]. Control Engineering Practice, Dec,2003,11 (12),pp 1371-1387.
[79]Gerry, J., Prioritizing and optimizing problem loops using a loop monitoring system[J]. Technical Papers-ISA,2002,422 pp 145-150.
[80]Grimble, M., Controller performance benchmarking and tuning using generalised minimum variance control[J]. Automatica,2002,38 (12),pp 2111-2119.
[81]Zhao, Y.,Su, H. Y.,Chu, J., etc., Multivariable Control Performance Assessment Based on Generalized Minimum Variance Benchmark[J]. Chinese Journal of Chemical Engineering, Feb,2010,18 (1),pp 86-94.
[82]Grimble, M. J., Performance assessment and benchmarking LQG predictive optimal controllers for discrete-time state-space systems[J]. Transactions of the Institute of Measurement and Control,2003,25 (3),pp 239-264.
[83]Zhao, C.,Zhao, Y.,So, H. Y., etc., Economic performance assessment of advanced process control with LQG benchmarking[J]. Journal of Process Control, Apr,2009,19 (4),pp 557-569.
[84]Pour, N. D.,Huang, B.,Shah, S. L., Performance assessment of advanced supervisory-regulatory control systems with subspace LQG benchmark[J]. Automatica, Aug,2010,46 (8),pp 1363-1368.
[85]Xu, Q. L.,Zhao, C.,Zhang, D. F., etc., Data-driven LQG Benchmaking for Economic Performance Assessment of Advanced Process Control Systems. In 2011 American Control Conference,2011; pp 5085-5090.
[86]Kadali, R.,Huang, B., Controller performance analysis with LQG benchmark obtained under closed loop conditions[J]. ISA Transactions,2002,41 (4),pp 521-537.
[87]Dai, C.,Yang, S., Controller performance assessment with a LQG benchmark obtained by using the subspace method[J]. Proceedings of the Control 2004,2004.
[88]Grimble, M. J., Restricted structure control loop performance assessment for PID controllers and state-space systems[J]. Asian Journal of Control, Mar,2003,5 (1),pp 39-57.
[89]Ko, B. S.,Edgar, T. F., PID control performance assessment:The single-loop case[J]. AIChE Journal, Jun,2004,50 (6),pp 1211-1218.
[90]Yu, Z. P.,Wang, J. D.,Huang, B. A., etc., Performance assessment of PID control loops subject to setpoint changes[J]. Journal of Process Control, Sep,2011,21 (8),pp 1164-1171.
[91]Julien, R. H.,Foley, M. W.,Cluett, W. R., Performance assessment using a model predictive control benchmark[J]. Journal of Process Control,2004,14 (4),pp 441-456.
[92]Ko, B. S.,Edgar, T. F., Performance assessment of constrained model predictive control systems[J]. AIChE Journal, Jun,2001,47 (6),pp 1363-1371.
[93]Schaffer, J.,Cinar, A., Multivariable MPC system performance assessment, monitoring, and diagnosis[J]. Journal of Process Control, Mar,2004,14 (2),pp 113-129.
[94]Zhang, Q.,Li, S. Y., Performance monitoring and diagnosis of multivariable model predictive control using statistical analysis[J]. Chinese Journal of Chemical Engineering, Apr,2006,14 (2),pp 207-215.
[95]Tian, X.,Chen, G.,Chen, S., A data-based approach for multivariate model predictive control performance monitoring[J]. Neurocomputing,2011,74 (4),pp 588-597.
[96]Huang, B.,Ding, S. X.,Thornhill, N., Alternative solutions to multi-variate control performance assessment problems[J]. Journal of Process Control,2006,16 (5),pp 457-471.
[97]Zhao, Y.,Chu, J.,Su, H., etc., Multi-step prediction error approach for controller performance monitoring[J]. Control Engineering Practice,2010,18 (1),pp 1-12.
[98]Jelali, M., Performance assessment of control systems in rolling mills application to strip thickness and flatness control[J]. Journal of Process Control, Dec, 2007,17 (10),pp 805-816.
[99]Lee, K. H.,Tamayo, E. C.,Huang, B., Industrial implementation of controller performance analysis technology[J]. Control Engineering Practice, Feb,2010,18 (2),pp 147-158.
[100]Jelali, M., An overview of control performance assessment technology and industrial applications[J]. Control Engineering Practice, May,2006,14 (5),pp 441-466.
[101]Shardt, Y.,Zhao, Y.,Qi, F., etc., Determining the state of a process control system:Current trends and future challenges[J]. Canadian Journal of Chemical Engineering, Apr,2012,90 (2),pp 217-245.
[102]Beard, R. V. Failure accommodation in linear systems though self-reorganization[D]. Cambridge, MA:MIT 1971.
[103]Mehra, R. K.,Peschon, J., An innovation approach to fault detection and diagnosis in Dynamics[J], Automatica,1971,7.
[104]Willsky, A. S., A suvrey of design methods for failure detection in dynamic systems[S]. Automatica,1976,12.
[105]Venkatasubramanian, V.,Rengaswamy, R.,Kavuri, S. N., A review of process fault detection and diagnosis Part Ⅱ:Quantitative model and search strategies[J]. Computers & Chemical Engineering, Mar,2003,27 (3),pp 313-326.
[106]Venkatsubramanian, V.,Rengaswamy, R.,Yin, K., etc., A review of process fault detection and diagnosis Part I:Quantitative model-based methods[J]. Computers & Chemical Engineering, Mar,2003,27 (3),pp 293-311.
[107]Gertler, J. Analytical redundancy methods in fault detection and isolation[A], In 1991; pp 9-21.
[108]Gertler, J., Residual generation in model-based fault diagnosis[J]. Control-Theory and Advanced Technology, Mar,1993,9 (1),pp 259-285.
[109]Gertler, J. Analytical redundancy methods in fault detection and isolation-survey and synthesis[A], In IFAC symposium on online fault detection and supervision in the chemical process industries[C],1992.
[110]Jackson, J. E.,Wiley, J., A user's guide to principal components[M]. New York:John Wiley 2003.
[111]MacGregor, J.,Kourti, T., Statistical process control of multivariate processes[J]. Control Engineering Practice,1995,3 (3),pp 403-414.
[112]MacGregor, J. F., Using on-line process data to improve quality:challenges for statisticians[J]. International Statistical Review,1997,65 (3),pp 309-323.
[113]Yoon, S.,MacGregor, J. F., Fault diagnosis with multivariate statistical models part I:using steady state fault signatures[J]. Journal of Process Control,2001, 11 (4),pp 387-400.
[114]Miller, P.,Swanson, R.,Heckler, C. E., Contribution plots:a missing link in multivariate quality control[J]. Applied mathematics and computer science,1998,8 pp 775-792.
[115]Kourti, T.,MacGregor, J. F., Multivariate SPC methods for process and product monitoring[J]. Journal of Quality Technology,1996,28 (4),pp 409-428.
[116]Wise, B. M.,Ricker, N. L.,Veltkamp, D. J. Upset and sensor failure detection in multivariate processes[A], In 1989.
[117]Wise, B. M.,Gallagher, N. B., The process chemometrics approach to process monitoring and fault detection[J]. Journal of Process Control,1996,6 (6),pp 329-348.
[118]Dunia, R.,Qin, S. J.,Edgar, T. F., etc., Identification of faulty sensors using principal component analysis[J]. AIChE Journal, Oct,1996,42 (10),pp 2797-2812.
[119]Lieftucht, D.,V6lker, M.,Sonntag, C., etc., Improved fault diagnosis in multivariate systems using regression-based reconstruction[J]. Control Engineering Practice,2009,17 (4),pp 478-493.
[120]Nelson, P. R. C.,Taylor, P. A.,MacGregor, J. F., Missing data methods in PCA and PLS:Score calculations with incomplete observations[J]. Chemometrics and Intelligent Laboratory Systems,1996,35 (1),pp 45-65.
[121]Nelson, P. R. C.,MacGregor, J. F.,Taylor, P. A., The impact of missing measurements on PCA and PLS prediction and monitoring applications[J]. Chemometrics and Intelligent Laboratory Systems,2006,80 (1),pp 1-12.
[122]Arteaga, F.,Ferrer, A., Dealing with missing data in MSPC:several methods, different interpretations, some examples[J]. Journal of Chemometrics,2002,16 (8-10),pp408-418.
[123]Iri, M.,Aoki, K.,O'Shima, E., etc., An algorithm for diagnosis of system failures in the chemical process[J]. Computers & Chemical Engineering,1979,3 (1-4),pp 489-493.
[124]Umeda, T.,Kuriyama, T.,O'shima, E., etc., A graphical approach to cause and effect analysis of chemical processing systems[J]. Chemical Engineering Science, 1980,35 (12),pp 2379-2388.
[125]Kramer, M.,Palowitch Jr, B., A rule-based approach to fault diagnosis using the signed directed graph[J]. AIChE Journal,1987,33 (7),pp 1067-1078.
[126]Zhang, X.,Hoo, K. A., Effective fault detection and isolation using bond graph-based domain decomposition[J]. Computers & Chemical Engineering, Jan, 2011,35 (1),pp 132-148.
[127]Ould-Bouamama, B.,El Harabi, R.,Abdelkrim, M. N., etc., Bond graphs for the diagnosis of chemical processes[J]. Computers & Chemical Engineering, Jan, 2012,36 pp 301-324.
[128]Bauer, M.,Cox, J. W.,Caveness, M. H., etc., Finding the direction of disturbance propagation in a chemical process using transfer entropy[J]. IEEE Transactions on Control Systems Technology, Jan,2007,15 (1),pp 12-21.
[129]Bauer, M.,Cox, J. W.,Caveness, M. H., etc., Nearest neighbors methods for root cause analysis of plantwide disturbances[J]. Industrial & Engineering Chemistry Research, Aug,2007,46 (18),pp 5977-5984.
[130]Bauer, M.,Thornhill, N. F., A practical method for identifying the propagation path of plant-wide disturbances[J]. Journal of Process Control, Aug-Sep,2008,18 (7-8),pp 707-719.
[131]Thambirajah, J.,Benabbas, L.,Bauer, M., etc., Cause-and-effect analysis in chemical processes utilizing XML, plant connectivity and quantitative process history[J]. Computers & Chemical Engineering, Feb,2009,33 (2),pp 503-512.
[132]Cheung, J. T. Y.,Stephanopoulos, G., Representation of process trends—Part I. A formal representation framework[J]. Computers & Chemical Engineering,1990, 14 (4),pp 495-510.
[133]Janusz, M. E.,Venkatasubramanian, V., Automatic generation of qualitative descriptions of process trends for fault detection and diagnosis[J]. Engineering Applications of Artificial Intelligence,1991,4 (5),pp 329-339.
[134]Maurya, M. R.,Rengaswamy, R.,Venkatasubramanian, V., Fault diagnosis using dynamic trend analysis:A review and recent developments[J]. Engineering Applications of Artificial Intelligence, Mar,2007,20 (2),pp 133-146.
[135]Maurya, M. R.,Paritosh, P. K.,Rengaswamy, R., etc., A framework for on-line trend extraction and fault diagnosis[J]. Engineering Applications of Artificial Intelligence, Sep,2010,23 (6),pp 950-960.
[136]Charbonnier, S.,Portet, F., A self-tuning adaptive trend extraction method for process monitoring and diagnosis[J]. Journal of Process Control, Jul,2012,22 (6),pp 1127-1138.
[137]Choudhury, M.,Shah, S. L.,Thornhill, N. F., Diagnosis of poor control-loop performance using higher-order statistics[J]. Automatica, Oct,2004,40 (10),pp 1719-1728.
[138]Choudhury, M.,Shah, S. L.,Thornhill, N. F., etc., Automatic detection and quantification of stiction in control valves[J]. Control Engineering Practice,2006,14 (12),pp 1395-1412.
[139]Choudhury, M., Troubleshooting Plantwide Oscillations using Nonlinearity Information[J]. Journal of Chemical Engineering,2010,24 (0),pp 50-60.
[140]Zang, X.,Howell, J., Isolating the source of whole-plant oscillations through bi-amplitude ratio analysis[J]. Control Engineering Practice,2007,15 (1),pp 69-76.
[141]Jelali, M.,Huang, B., Detection and Diagnosis of Stiction in Control Loops: State of the Art and Advanced Methods[M]. London:Springer:2010.
[142]Kruger, U.,Kumar, S.,Littler, T., Improved principal component monitoring using the local approach[J]. Automatica, Sep,2007,43 (9),pp 1532-1542.
[143]Li, W. H.,Yue, H. H.,Valle-Cervantes, S., etc., Recursive PCA for adaptive process monitoring[J]. Journal of Process Control, Oct,2000,10 (5),pp 471-486.
[144]Kramer, M. A., Nonlinear principal component analysis using autoassociative neural networks[J]. AIChE Journal,1991,37 (2),pp 233-243.
[145]Scholkopf, B.,Smola, A.,MUller, K. R., Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural Computation,1998,10(5),pp 1299-1319.
[146]Ku, W. F.,Storer, R. H.,Georgakis, C., Disturbance detection and isolation by dynamic principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, Nov,1995,30 (1),pp 179-196.
[147]Kruger, U.,Dimitriadis, G., Diagnosis of process faults in chemical systems using a local partial least squares approach[J]. AIChE Journal,2008,54 (10),pp 2581-2596.
[148]Ge, Z.,Kruger, U.,Lamont, L., etc., Fault detection in non-Gaussian vibration systems using dynamic statistical-based approaches[J]. Mechanical Systems and Signal Processing,2010,24 (8),pp 2972-2984.
[149]Ge, Z.,Xie, L.,Kruger, U., etc., Local ICA for multivariate statistical fault diagnosis in systems with unknown signal and error distributions[J]. AIChE Journal, 2012,58 (8),pp 2357-2372..
[150]Basseville, M., On-board component fault detection and isolation using the statistical local approach[J]. Automatica, Nov,1998,34(11),pp 1391-1415.
[151]Benveniste, A.,Basseville, M.,Moustakides, G. V., The Asymptotic Local Approach to Change Detection and Model Validation[J]. IEEE Transactions on Automatic Control, Jul,1987,32 (7),pp 583-592.
[152]Zhang, Q.,Basseville, M.,Benveniste, A., Early Warning of Slight Changes in Systems[J]. Automatica, Jan,1994,30 (1),pp 95-113.
[153]Box, G. E. P., Some theorems on quadratic forms applied in the study of analysis of variance problems, I. Effect of inequality of variance in the one-way classification[J]. The Annals of Mathematical Statistics,1954,25 (2),pp 290-302.
[154]Russell, E. L.,Chiang, L. H.,Braatz, R. D., Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, May,2000,51 (1),pp 81-93.
[155]Sun, L.,Ji, S.,Yu, S., etc. On the equivalence between canonical correlation analysis and orthonormalized partial least squares[A], In 2009; pp 1230-1235.
[156]Hardle, W.,Simar, L., Applied multivariate statistical analysis[M]. Springer Verlag:2011.
[157]McNabb, C. A.,Qin, S. J., Projection based MIMO control performance monitoring:I-covariance monitoring in state space[J]. Journal of Process Control, Dec,2003,13 (8),pp 739-757.
[158]Yu, J.,Qin, S. J., Statistical MIMO controller performance monitoring. Part I: Data-driven covariance benchmark[J]. Journal of Process Control, Mar-Apr,2008,18 (3-4),pp 277-296.
[159]Isserlis, L., On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables[J]. Biometrika,1918,12 (1/2),pp 134-139.
[160]Kramer, M. A.,Palowitch, B. L., A rule-based approach to fault diagnosis using the signed directed graph[J]. AIChE Journal, Jul,1987,33 (7),pp 1067-1078.
[161]Chang, C. C.,Yu, C. C., On-line fault diagnosis using the signed directed graph[J]. Industrial & Engineering Chemistry Research, Jul,1990,29 (7),pp 1290-1299.
[162]Leyval, L.,Gentil, S.,Feraybeaumont, S., Model-based causal reasoning for process supervision[J]. Automatica, Aug,1994,30 (8),pp 1295-1306.
[163]Bhushan, M.,Rengaswamy, R., Design of sensor network based on the signed directed graph of the process for efficient fault diagnosis[J]. Industrial & Engineering Chemistry Research, Apr,2000,39 (4),pp 999-1019.
[164]Montmain, J.,Gentil, S., Dynamic causal model diagnostic reasoning for online technical process supervision[J]. Automatica, Aug,2000,36 (8),pp 1137-1152.
[165]Palmer, C.,Chung, P. W. H., Creating signed directed graph models for process plants[J]. Industrial & Engineering Chemistry Research, Jul,2000,39 (7),pp 2548-2558.
[166]Yang, F.,Shah, S. L.,Xiao, D., Signed directed graph based modeling and its validation from process knowledge and process data[J]. International Journal of Applied Mathematics and Computer Science, Mar,2012,22 (1),pp 41-53.
[167]Maurya, M. R.,Rengaswamy, R.,Venkatasubramanian, V., A signed directed graph and qualitative trend analysis-based framework for incipient fault diagnosis[J]. Chemical Engineering Research & Design, Oct,2007,85 (A10),pp 1407-1422.
[168]Gao, D.,Wu, C.,Zhang, B., etc., Signed Directed Graph and Qualitative Trend Analysis Based Fault Diagnosis in Chemical Industry[J]. Chinese Journal of Chemical Engineering, Apr,2010,18 (2),pp 265-276.
[169]Yim, S. Y.,Ananthakumar, H. G.,Benabbas, L., etc., Using process topology in plant-wide control loop performance assessment[J]. Computers & Chemical Engineering, Dec 1,2006,31 (2),pp 86-99.
[170]Desborough, L.,Miller, R. Increasing customer value of industrial control performance monitoring-Honeywell's experience[A], In AIChE Symposium Series[C], 2002; pp 169-189.
[171]Stockmann, M.,Haber, R.,Schmitz, U., Source identification of plant-wide faults based on k nearest neighbor time delay estimation[J]. Journal of Process Control, Mar,2012,22 (3),pp 583-598.
[172]Wiener, N., The theory of prediction. In Modern mathematics for the engineer F., B. E., Ed. McGraw-Hill.:New York,1956.
[173]Granger, C. W. J., Investigating causal relations by econometric models and cross-spectral methods[J]. Econometrica,1969,37 (3),pp 424-438.
[174]Schreiber, T., Measuring information transfer[J]. Physical Review Letters, Jul 10,2000,85 (2),pp 461-464.
[175]Barnett, L.,Barrett, A. B.,Seth, A. K., Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables[J]. Physical Review Letters, Dec 4, 2009,103 (23),p 238701.
[176]Keller, K.,Sinn, M., Ordinal analysis of time series[J]. Physica A-Statistical Mechanics and Its Applications, Oct 1,2005,356 (1),pp 114-120.
[177]Keller, K.,Sinn, M.,Emonds, J., Time series from the ordinal viewpoint[J]. Stochastics and Dynamics, Jun,2007,7 (2),pp 247-272.
[178]Palus, M.,Komarek, V.,Hrncir, Z., etc., Synchronization as adjustment of information rates:Detection from bivariate time series[J]. Physical Review E, Apr, 2001,63 (4),p 046211.
[179]Schreiber, T., Detecting and analyzing nonstationarity in a time series using nonlinear cross predictions[J]. Physical Review Letters, Feb,1997,78 (5),pp 843-846.
[180]Hlavackova-Schindler, K.,Palus, M.,Vejmelka, M., etc., Causality detection based on information-theoretic approaches in time series analysis[J]. Physics Reports-Review Section of Physics Letters, Mar,2007,441 (1),pp 1-46.
[181]Staniek, M.,Lehnertz, K., Symbolic transfer entropy[J]. Physical Review Letters, Apr 18,2008,100 (15),p 158101.
[182]Kugiumtzis, D., Transfer Entropy on Rank Vectors[J]. e-print arXiv:1007.0357vl,2010.
[183]Downs, J. J.,Vogel, E. F., A plant-wide industrial process control problem[J]. Computers & Chemical Engineering, Mar,1993,17 (3),pp 245-255.
[184]Ricker, N. L., Decentralized control of the Tennessee Eastman Challenge Process[J]. Journal of Process Control, Aug,1996,6 (4),pp 205-221.
[185]丛雨.气动执行阀粘滞特性建模与参数辨识方法研究[D].浙江大学2010.
[186]Kozub, D. J. Controller performance monitoring and diagnosis:experiences and challenges [A], In 1997; pp 83-96.
[187]Qin, S. J.,Badgwell, T. A., A survey of industrial model predictive control technology [J]. Control Engineering Practice,2003,11 (7),pp 733-764.
[188]Huang, B., Multivariable model validation in the presence of time-variant disturbance dynamics[J]. Chemical Engineering Science,2000,55 (20),pp 4583-4595.
[189]Huang, B., On-line closed-loop model validation and detection of abrupt parameter changes[J]. Journal of Process Control,2001,11 (6),pp 699-715.
[190]Jiang, H.,Huang, B.,Shah, S. L., Closed-loop model validation based on the two-model divergence method[J]. Journal of Process Control,2009,19 (4),pp 644-655.
[191]Badwe, A. S.,Gudi, R. D.,Patwardhan, R. S., etc., Detection of model-plant mismatch in MPC applications[J]. Journal of Process Control,2009,19 (8),pp 1305-1313.
[192]Overschee, P. V.,Moor, B.,Hensher, D. A., etc., Subspace Identification for the Linear Systems:Theory-Implementation[M]. Kluwer academic publishers:1996.
[193]Harrison, C. A.,Qin, S. J., Discriminating between disturbance and process model mismatch in model predictive control[J]. Journal of Process Control,2009,19 (10),pp 1610-1616.
[194]Ljung, L., System identification:theory for the user[M]. Prentice Hall PTR: 1999.
[195]Huang, B.,Kadali, R., Dynamic modeling, predictive control and performance monitoring:A data-driven subspace approach[M]. Springer Verlag:2008.
[196]Benveniste, A.,Fuchs, J. J., Single Sample Modal Identification of a Nonstationary Stochastic-Process[J]. IEEE Transactions on Automatic Control,1985, 1985,30(1),pp 66-74.
[197]Moustakides, G. V.,Benveniste, A., Detecting changes in the AR parameters of a nonstationary ARMA process[J]. Stochastics:An International Journal of Probability and Stochastic Processes,1986,16(1-2),pp 137-155.
[198]Kammer, L. C.,Gorinevsky, D.,Dumont, G. A., Semi-intrusive multivariable model invalidation[J]. Automatica, Aug,2003,39 (8),pp 1461-1467.
[199]Selvanathan, S.,Tangirala, A. K., Diagnosis of Poor Control Loop Performance Due to Model-Plant Mismatch[J]. Industrial & Engineering Chemistry Research, May 5,2010,49 (9),pp 4210-4229.
[200]Webber, J. R.,Gupta, Y. P., A closed-loop cross-correlation method for detecting model mismatch in MIMO model-based controllers[J]. ISA Transactions, 2008,47 (4),pp 395-400.
[201]Shannon, C. E., Weaver, W., A mathematical theory of communication[J]. The Bell System Technical Journal,1948,27 (3),pp 379-423.
[202]Zhang, L. F.,Zhu, Q. M.,Longden, A., A set of novel correlation tests for nonlinear system variables[J]. International Journal of Systems Science, Jan,2007,38 (1),pp 47-60.
[203]Zhang, L. F.,Zhu, Q. M.,Longden, A., A Correlation-Test-Based Validation Procedure for Identified Neural Networks[J]. IEEE Transactions on Neural Networks, Jan,2009,20(1),pp 1-13.
[204]Kraskov, A.,Stogbauer, H.,Grassberger, P., Estimating mutual information[J]. Physical Review E, Jun,2004,69 (6),p 066138.
[205]Moon, Y. I.,Rajagopalan, B.,Lall, U., Estimation of mutual information using kernel density estimators[J]. Physical Review E, Sep,1995,52 (3),pp 2318-2321.
[206]Khan, S.,Bandyopadhyay, S.,Ganguly, A. R., etc., Relative performance of mutual information estimation methods for quantifying the dependence among short and noisy data[J]. Physical Review E, Aug,2007,76 (2),p 026209.
[207]Papana, A.,Kugiumtzis, D., Evaluation of Mutual Information Estimators for Time Series[J]. International Journal of Bifurcation and Chaos, Dec,2009,19 (12),pp 4197-4215.
[208]Roulston, M. S., Significance testing of information theoretic functionals[J]. Physica D-Nonlinear Phenomena, Dec 1,1997,110 (1-2),pp 62-66.
[209]Palus, M.,Vejmelka, M., Directionality of coupling from bivariate time series: How to avoid false causalities and missed connections[J]. Physical Review E, May, 2007,75 (5),p 056211.
[210]Schreiber, T.,Schmitz, A., Improved surrogate data for nonlinearity tests[J]. Physical Review Letters, Jul,1996,77 (4),pp 635-638.
[211]Schreiber, T.,Schmitz, A., Surrogate time series[J]. Physica D-Nonlinear Phenomena, Aug 15,2000,142 (3-4),pp 346-382.
[212]Joe, H., Relative Entropy Measures of Multivariate Dependence[J]. Journal of the American Statistical Association,1989,84 (405),pp 157-164.
[213]Estevez, P. A.,Tesmer, M.,Perez, C. A., etc., Normalized Mutual Information Feature Selection[J]. IEEE Transactions on Neural Networks, Feb,2009,20 (2),pp 189-201.
[214]Stanfelj, N.,Marlin, T. E.,Macgregor, J. F., Monitoring and diagnosing process control performance:the single-loop case[J]. Industrial & Engineering Chemistry Research, Feb,1993,32 (2),pp 301-314.
[215]Huang, B. A., On-line closed-loop model validation and detection of abrupt parameter changes[J]. Journal of Process Control,2001,11 (6),pp 699-715.
[216]Jiang, H. L.,Huang, B.,Shah, S. L., Closed-loop model validation based on the two-model divergence method[J]. Journal of Process Control,2009,19 (4),pp 644-655.
[217]Nowak, R. D.,Vanveen, B. D., Random and pseudorandom inputs for Volterra filter identification[J]. IEEE Transactions on Signal Processing, Aug,1994, 42 (8),pp 2124-2135.
[218]Arefi, M. M.,Montazeri, A.,Poshtan, J., etc., Wiener-neural identification and predictive control of a more realistic plug-flow tubular reactor[J]. Chemical Engineering Journal, May 1,2008,138 (1-3),pp 274-282.
[219]Voros, J., Parameter identification of Wiener systems with discontinuous nonlinearities[J]. Systems & Control Letters, Dec 14,2001,44 (5),pp 363-372.
[220]Hasiewicz, Z.,Mzyk, G., Combined parametric-nonparametric identification of Hammerstein systems[J]. IEEE Transactions on Automatic Control, Aug,2004,49 (8),pp 1370-1375.
[221]Sutter, E. E., A practical nonstochastic approach to nonlinear time-domain analysis[J]. Advanced methods of physiological systems modeling,1987,1 pp 303-315.
[222]Zhu, Y., Multivariable system identification for process control[M]. Oxford: Elsevier Science:2001.
[223]Abarbanel, H. D. I., Analysis of observed chaotic data[M]. New York: Springer Verlag:1996.
[224]Grune, L.,Pannek, J., Nonlinear Model Predictive Control:Theory and Algorithms[M]. Springer Verlag:2011.
[2]Nimmo, I., Adequately address abnormal operations[J]. Chemical Engineering Progress, Sep,1995,91 (9),pp 36-45.
[3]Desborough, L.,Miller, R. Increasing customer value of industrial control performance monitoring-Honeywell's experience [A], In 2002; pp 169-189.
[4]Paulonis, M. A.,Cox, J. W., A practical approach for large-scale controller performance assessment, diagnosis, and improvement[J]. Journal of Process Control, Mar,2003,13 (2),pp 155-168.
[5]Yamanaka, F.,Nishiya, T., Application of the intelligent alarm system for the plant operation[J]. Computers & Chemical Engineering,1997,21 pp S625-S630.
[6]Venkatasubramanian, V.,Rengaswamy, R.,Kavuri, S. N., etc., A review of process fault detection and diagnosis Part Ⅲ:Process history based methods[J]. Computers & Chemical Engineering, Mar,2003,27 (3),pp 327-346.
[7]Willsky, A. S., A survey of design methods for failure detection in dynamic systems[J]. Automatica,1976,12 (6),pp 601-611.
[8]Shewhart, W. A., Economic Control of Quality of Manufactured Product[M]. N.J.: Van Nostrand Reinhold, Princeton:1931.
[9]Qin, S. J., Statistical process monitoring:basics and beyond[J]. Journal of Chemometrics, Aug-Sep,2003,17 (8-9),pp 480-502.
[10]Astrom, K. J., Introduction to stochastic control theory[M]. New York:Academic Press:1970.
[11]Harris, T. J., Assessment of control loop performance[J]. The Canadian Journal of Chemical Engineering,1989,67 (5),pp 856-861.
[12]Page, E., Continuous inspection schemes[J]. Biometrika,1954,41 (1/2),pp 100-115.
[13]Roberts, S., Control chart tests based on geometric moving averages[J]. Technometrics,1959, pp 239-250.
[14]Hunter, J. S., The exponentially weighted moving average[J]. Jourla of Quality Technology,1986,18 (4),pp 203-210.
[15]Lucas, J. M.,Saccucci, M. S., Exponentially weighted moving average control schemes:properties and enhancements[J]. Technometrics,1990, pp 1-12.
[16]Page, E., Cumulative sum charts[J]. Technometrics,1961, pp 1-9.
[17]Hawkins, D. M., Cumulative sum control charting:an underutilized SPC tool[J]. Quality Engineering,1993,5 (3),pp 463-477.
[18]Hawkins, D. M.,Olwell, D. H., Cumulative sum charts and charting for quality improvement[M]. New York:Springer Verlag:1998.
[19]MacGregor, J. F., Using On-Line Process Data to Improve Quality: Challenges for Statisticians[J]. International Statistical Review,1997,65 (3),pp 309-323.
[20]Pearson, K., On lines and planes of closest fit to systems of points in space[J]. Philosophical Magazine, Jul-Dec,1901,2 (7-12),pp 559-572.
[21]Duda, R. O.,Hart, P. E., Pattern classification and scene analysis[M]. New York:John Wiley & Sons:1973.
[22]Hyvarinen, A.,Karhunen, J.,Oja, E., Independent component analysis[M]. New York:John Wiley & Sons:2001.
[23]Wold, H., Estimation of principal components and related models by iterative least squares[J]. Multivariate analysis,1966,1 pp 391-420.
[24]Wold, H., Nonlinear estimation by iterative least squares procedures[J]. Research papers in statistics,1966, pp 411-444.
[25]Hotelling, H., The most predictable criterion[J]. Journal of Educational Psychology,1935,26 pp 139-142.
[26]Hotelling, H., Relations between two sets of variates[J]. Biometrika, Dec, 1936,28 pp 321-377.
[27]Anderson, T. W., Estimating linear restrictions on regression coefficients for multivariate normal distributions[J]. Annals of Mathematical Statistics,1951,22 (3),pp 327-351.
[28]van den Wollenberg, A. L., Redundancy analysis an alternative for canonical correlation analysis[J]. Psychometrika,1977,42 (2),pp 207-219.
[29]Macgregor, J. F.,Jaeckle, C.,Kiparissides, C., etc., Process monitoring and diagnosis by multiblock PLS methods[J]. AIChE Journal, May,1994,40 (5),pp 826-838.
[30]Wangen, L.,Kowalski, B., A multiblock partial least squares algorithm for investigating complex chemical systems[J]. Journal of Chemometrics,1989,3 (1),pp 3-20.
[31]Qin, S. J.,Valle, S.,Piovoso, M. J., On unifying multiblock analysis with application to decentralized process monitoring[J]. Journal of Chemometrics, Oct, 2001,15 (9),pp 715-742.
[32]Wang, X.,K.ruger, U.,Lennox, B., Recursive partial least squares algorithms for monitoring complex industrial processes[J]. Control Engineering Practice, Jun, 2003,11 (6),pp 613-632.
[33]Westerhuis, J. A.,Kourti, T.,MacGregor, J. F., Analysis of multiblock and hierarchical PCA and PLS models[J]. Journal of Chemometrics, Sep-Oct,1998,12 (5),pp 301-321.
[34]Jackson, J. E.,Mudholkar, G. S., Control Procedures for Residuals Associated with Principal Component Analysis[J]. Technometrics,1979,21 (3),pp 341-349.
[35]Akaike, H. Information theory and an extension of the maximum likelihood principle[A], In Petrov,Caski,2nd International Symposium on Information Theory[C],1974; pp 267-281.
[36]Malinowski, E. R., Determination of the number of factors and the experimental error in a data matrix[J]. Analytical Chemistry,1977,49 (4),pp 612-617.
[37]Rissanen, J., Modeling by shortest data description[J]. Automatica,1978,14 (5),pp 465-471.
[38]Malinowski, E. R., Factor Analysis in Chemistry[M]. New York: Wiley-Interscience:1991.
[39]Cattell, R. B., The scree test for the number of factors[J]. Multivariate Behavioral Research,1966, (April),pp 245-276.
[40]Rozett, R. W.,Petersen, E. M., Methods of factor analysis of mass spectra[J]. Analytical Chemistry,1975,47 (8),pp 1301-1308.
[41]Kaiser, H. F., The application of electronic computers to factor analysis[J]. Euducational Psychological Measure,1960,20 (1),pp 141-151.
[42]Zwick, W. R.,Velicer, W. F., Comparison of five rules for determining the number of components to retain[J]. Psychological Bulletin, May,1986,99 (3),pp 432-442.
[43]Shrager, R. I.,Hendler, R. W., Titration of individual components in a mixture with resolution of difference spectra, pKs, and redox transitions[J]. Analytical Chemistry,1982,54 (7),pp 1147-1152.
[44]Wold, S., Cross validatory estimation of the number of components in factor and principal components analysis[J]. Technometrics,1978,20 (4),pp 397-405.
[45]Carey, R. N.,Wold, S.,Westgard, J. O., Principal component analysis:an alternative to referee methods in method comparison studies[J]. Analytical Chemistry, 1975,47(11),pp 1824-1829.
[46]Qin, S. J.,Dunia, R., Determining the number of principal components for best reconstruction[M].1999; p 357-362.
[47]Qin, S. J.,Dunia, R., Determining the number of principal components for best reconstruction[J]. Journal of Process Control, Apr-Jun,2000,10 (2-3),pp 245-250.
[48]Valle, S.,Li, W. H.,Qin, S. J., Selection of the number of principal components:The variance of the reconstruction error criterion with a comparison to other methods[J]. Industrial & Engineering Chemistry Research, Nov,1999,38 (11),pp 4389-4401.
[49]Morud, T. E., Multivariate statistical process control; Example from the chemical process industry[J]. Journal of Chemometrics, Sep-Dec,1996,10 (5-6),pp 669-675.
[50]Kruger, U.,Chen, Q.,Sandoz, D. J., etc., Extended PLS approach for enhanced condition monitoring of industrial processes[J]. AIChE Journal, Sep,2001, 47 (9),pp 2076-2091.
[51]Geladi, P., PLS model building:theory and application[J]. Chemometrics and Intelligent Laboratory Systems, Nov,1988,5(1),pp 2-3.
[52]王惠文,偏最小二乘回归方法及其应用[M].国防工业出版社：1999.
[53]费正顺.基于PLS框架的动态优化方法研究[D].杭州：浙江大学2012.
[54]Geladi, P.,Kowalski, B. R., Partial least-squares regression:a tutorial[J]. Analytica Chimica Acta,1986,185 pp 1-17.
[55]de Jong, S., SIMPLS:an alternative approach to partial least squares regression [J]. Chemometrics and Intelligent Laboratory Systems,1993,18 (3),pp 251-263.
[56]Huang, B.,Shah, S. L., Performance assessment of control loops:theory and applications[M]. Springer Verlag:1999.
[57]Ordys, A. W.,Uduehi, D.,Johnson, M. A., etc., Process control performance assessment:from theory to implementation[M]. Springer Verlag:2007.
[58]Astrom, K., Computer control of a paper machine—An application of linear stochastic control theory [J]. IBM Journal of research and development,1967,11 (4),pp 389-405.
[59]Huang, B.,Shah, S.,Kwok, E., Good, bad or optimal? Performance assessment of multivariable processes[J]. Automatica,1997,33 (6),pp 1175-1183.
[60]Harris, T.,Boudreau, F.,MacGregor, J., Performance assessment of multivariable feedback controllers[J].Automatica,1996,32 (11),pp 1505-1518.
[61]Ko, B. S.,Edgar, T. F., Performance assessment of multivariable feedback control systems[J]. Automatica,2001,37 (6),pp 899-905.
[62]Huang, B.,Ding, S. X.,Thornhill, N., Practical solutions to multivariate feedback control performance assessment problem:reduced a priori knowledge of interactor matrices[J]. Journal of Process Control,2005,15 (5),pp 573-583.
[63]Shah, S. L.,Patwardhan, R.,Huang, B., Multivariate controller performance analysis:methods, applications and challenges[J]. AICHE Symposium Series,2002, 98 (326),pp 190-207.
[64]Qin, S. J.,Yu, J., Recent developments in multivariable controller performance monitoring[J]. Journal of Process Control, Mar,2007,17 (3),pp 221-227.
[65]Qin, S. J., Control performance monitoring-a review and assessment[J]. Computers & Chemical Engineering, Dec,1998,23 (2),pp 173-186.
[66]Huang, B.,Shah, S. L.,Miller, R., Feedforward plus feedback controller performance assessment of MIMO systems[J]. IEEE Transactions on Control Systems Technology, May,2000,8 (3),pp 580-587.
[67]Desborough, L.,Harris, T., Performance Assessment Measures for Univariate Feedforward Feedback-Control [J]. Canadian Journal of Chemical Engineering, Aug, 1993,71 (4),pp 605-616.
[68]Tyler, M. L.,Morari, M., Performance monitoring of control systems using likelihood methods[J]. Automatica, Aug,1996,32 (8),pp 1145-1162.
[69]Huang, B., Minimum variance control and performance assessment of time-variant processes[J]. Journal of Process Control,2002,12 (6),pp 707-719.
[70]Olaleye, F. B.,Huang, B.,Tamayo, E., Feedforward and feedback controller performance assessment of linear time-variant processes[J]. Industrial & Engineering Chemistry Research, Jan,2004,43 (2),pp 589-596.
[71]Ko, B. S.,Edgar, T. F., Performance assessment of cascade control loops[J]. AIChE Journal, Feb,2000,46 (2),pp 281-291.
[72]McNabb, C. A.,Qin, S. J., Projection based MIMO control performance monitoring:Ⅱ-measured disturbances and setpoint changes[J]. Journal of Process Control, Feb,2005,15 (1),pp 89-102.
[73]Thornhill, N. F.,Cox, J. W.,Paulonis, M. A., Diagnosis of plant-wide oscillation through data-driven analysis and process understanding[J]. Control Engineering Practice, Dec,2003,11 (12),pp 1481-1490.
[74]Horch, A.Isaksson, A. J., A modified index for control performance assessment[J]. Journal of Process Control, Dec,1999,9 (6),pp 475-483.
[75]Huang, B.,Shah, S. L., Practical issues in multivariable feedback control performance assessment[J]. Journal of Process Control, Oct-Dec,1998,8 (5-6),pp 421-430.
[76]Desborough, L.,Harris, T., Performance assessment measures for univariate feedback control[J]. Canadian Journal of Chemical Engineering, Dec,1992,70 (6),pp 1186-1197.
[77]Thornhill, N. F.,Oettinger, M.,Fedenczuk, P., Refinery-wide control loop performance assessment[J]. Journal of Process Control, Apr,1999,9 (2),pp 109-124.
[78]Gao, H.,Patwardhan, R.,Akamatsu, K., etc., Performance evaluation of two industrial MPC controllers[J]. Control Engineering Practice, Dec,2003,11 (12),pp 1371-1387.
[79]Gerry, J., Prioritizing and optimizing problem loops using a loop monitoring system[J]. Technical Papers-ISA,2002,422 pp 145-150.
[80]Grimble, M., Controller performance benchmarking and tuning using generalised minimum variance control[J]. Automatica,2002,38 (12),pp 2111-2119.
[81]Zhao, Y.,Su, H. Y.,Chu, J., etc., Multivariable Control Performance Assessment Based on Generalized Minimum Variance Benchmark[J]. Chinese Journal of Chemical Engineering, Feb,2010,18 (1),pp 86-94.
[82]Grimble, M. J., Performance assessment and benchmarking LQG predictive optimal controllers for discrete-time state-space systems[J]. Transactions of the Institute of Measurement and Control,2003,25 (3),pp 239-264.
[83]Zhao, C.,Zhao, Y.,So, H. Y., etc., Economic performance assessment of advanced process control with LQG benchmarking[J]. Journal of Process Control, Apr,2009,19 (4),pp 557-569.
[84]Pour, N. D.,Huang, B.,Shah, S. L., Performance assessment of advanced supervisory-regulatory control systems with subspace LQG benchmark[J]. Automatica, Aug,2010,46 (8),pp 1363-1368.
[85]Xu, Q. L.,Zhao, C.,Zhang, D. F., etc., Data-driven LQG Benchmaking for Economic Performance Assessment of Advanced Process Control Systems. In 2011 American Control Conference,2011; pp 5085-5090.
[86]Kadali, R.,Huang, B., Controller performance analysis with LQG benchmark obtained under closed loop conditions[J]. ISA Transactions,2002,41 (4),pp 521-537.
[87]Dai, C.,Yang, S., Controller performance assessment with a LQG benchmark obtained by using the subspace method[J]. Proceedings of the Control 2004,2004.
[88]Grimble, M. J., Restricted structure control loop performance assessment for PID controllers and state-space systems[J]. Asian Journal of Control, Mar,2003,5 (1),pp 39-57.
[89]Ko, B. S.,Edgar, T. F., PID control performance assessment:The single-loop case[J]. AIChE Journal, Jun,2004,50 (6),pp 1211-1218.
[90]Yu, Z. P.,Wang, J. D.,Huang, B. A., etc., Performance assessment of PID control loops subject to setpoint changes[J]. Journal of Process Control, Sep,2011,21 (8),pp 1164-1171.
[91]Julien, R. H.,Foley, M. W.,Cluett, W. R., Performance assessment using a model predictive control benchmark[J]. Journal of Process Control,2004,14 (4),pp 441-456.
[92]Ko, B. S.,Edgar, T. F., Performance assessment of constrained model predictive control systems[J]. AIChE Journal, Jun,2001,47 (6),pp 1363-1371.
[93]Schaffer, J.,Cinar, A., Multivariable MPC system performance assessment, monitoring, and diagnosis[J]. Journal of Process Control, Mar,2004,14 (2),pp 113-129.
[94]Zhang, Q.,Li, S. Y., Performance monitoring and diagnosis of multivariable model predictive control using statistical analysis[J]. Chinese Journal of Chemical Engineering, Apr,2006,14 (2),pp 207-215.
[95]Tian, X.,Chen, G.,Chen, S., A data-based approach for multivariate model predictive control performance monitoring[J]. Neurocomputing,2011,74 (4),pp 588-597.
[96]Huang, B.,Ding, S. X.,Thornhill, N., Alternative solutions to multi-variate control performance assessment problems[J]. Journal of Process Control,2006,16 (5),pp 457-471.
[97]Zhao, Y.,Chu, J.,Su, H., etc., Multi-step prediction error approach for controller performance monitoring[J]. Control Engineering Practice,2010,18 (1),pp 1-12.
[98]Jelali, M., Performance assessment of control systems in rolling mills application to strip thickness and flatness control[J]. Journal of Process Control, Dec, 2007,17 (10),pp 805-816.
[99]Lee, K. H.,Tamayo, E. C.,Huang, B., Industrial implementation of controller performance analysis technology[J]. Control Engineering Practice, Feb,2010,18 (2),pp 147-158.
[100]Jelali, M., An overview of control performance assessment technology and industrial applications[J]. Control Engineering Practice, May,2006,14 (5),pp 441-466.
[101]Shardt, Y.,Zhao, Y.,Qi, F., etc., Determining the state of a process control system:Current trends and future challenges[J]. Canadian Journal of Chemical Engineering, Apr,2012,90 (2),pp 217-245.
[102]Beard, R. V. Failure accommodation in linear systems though self-reorganization[D]. Cambridge, MA:MIT 1971.
[103]Mehra, R. K.,Peschon, J., An innovation approach to fault detection and diagnosis in Dynamics[J], Automatica,1971,7.
[104]Willsky, A. S., A suvrey of design methods for failure detection in dynamic systems[S]. Automatica,1976,12.
[105]Venkatasubramanian, V.,Rengaswamy, R.,Kavuri, S. N., A review of process fault detection and diagnosis Part Ⅱ:Quantitative model and search strategies[J]. Computers & Chemical Engineering, Mar,2003,27 (3),pp 313-326.
[106]Venkatsubramanian, V.,Rengaswamy, R.,Yin, K., etc., A review of process fault detection and diagnosis Part I:Quantitative model-based methods[J]. Computers & Chemical Engineering, Mar,2003,27 (3),pp 293-311.
[107]Gertler, J. Analytical redundancy methods in fault detection and isolation[A], In 1991; pp 9-21.
[108]Gertler, J., Residual generation in model-based fault diagnosis[J]. Control-Theory and Advanced Technology, Mar,1993,9 (1),pp 259-285.
[109]Gertler, J. Analytical redundancy methods in fault detection and isolation-survey and synthesis[A], In IFAC symposium on online fault detection and supervision in the chemical process industries[C],1992.
[110]Jackson, J. E.,Wiley, J., A user's guide to principal components[M]. New York:John Wiley 2003.
[111]MacGregor, J.,Kourti, T., Statistical process control of multivariate processes[J]. Control Engineering Practice,1995,3 (3),pp 403-414.
[112]MacGregor, J. F., Using on-line process data to improve quality:challenges for statisticians[J]. International Statistical Review,1997,65 (3),pp 309-323.
[113]Yoon, S.,MacGregor, J. F., Fault diagnosis with multivariate statistical models part I:using steady state fault signatures[J]. Journal of Process Control,2001, 11 (4),pp 387-400.
[114]Miller, P.,Swanson, R.,Heckler, C. E., Contribution plots:a missing link in multivariate quality control[J]. Applied mathematics and computer science,1998,8 pp 775-792.
[115]Kourti, T.,MacGregor, J. F., Multivariate SPC methods for process and product monitoring[J]. Journal of Quality Technology,1996,28 (4),pp 409-428.
[116]Wise, B. M.,Ricker, N. L.,Veltkamp, D. J. Upset and sensor failure detection in multivariate processes[A], In 1989.
[117]Wise, B. M.,Gallagher, N. B., The process chemometrics approach to process monitoring and fault detection[J]. Journal of Process Control,1996,6 (6),pp 329-348.
[118]Dunia, R.,Qin, S. J.,Edgar, T. F., etc., Identification of faulty sensors using principal component analysis[J]. AIChE Journal, Oct,1996,42 (10),pp 2797-2812.
[119]Lieftucht, D.,V6lker, M.,Sonntag, C., etc., Improved fault diagnosis in multivariate systems using regression-based reconstruction[J]. Control Engineering Practice,2009,17 (4),pp 478-493.
[120]Nelson, P. R. C.,Taylor, P. A.,MacGregor, J. F., Missing data methods in PCA and PLS:Score calculations with incomplete observations[J]. Chemometrics and Intelligent Laboratory Systems,1996,35 (1),pp 45-65.
[121]Nelson, P. R. C.,MacGregor, J. F.,Taylor, P. A., The impact of missing measurements on PCA and PLS prediction and monitoring applications[J]. Chemometrics and Intelligent Laboratory Systems,2006,80 (1),pp 1-12.
[122]Arteaga, F.,Ferrer, A., Dealing with missing data in MSPC:several methods, different interpretations, some examples[J]. Journal of Chemometrics,2002,16 (8-10),pp408-418.
[123]Iri, M.,Aoki, K.,O'Shima, E., etc., An algorithm for diagnosis of system failures in the chemical process[J]. Computers & Chemical Engineering,1979,3 (1-4),pp 489-493.
[124]Umeda, T.,Kuriyama, T.,O'shima, E., etc., A graphical approach to cause and effect analysis of chemical processing systems[J]. Chemical Engineering Science, 1980,35 (12),pp 2379-2388.
[125]Kramer, M.,Palowitch Jr, B., A rule-based approach to fault diagnosis using the signed directed graph[J]. AIChE Journal,1987,33 (7),pp 1067-1078.
[126]Zhang, X.,Hoo, K. A., Effective fault detection and isolation using bond graph-based domain decomposition[J]. Computers & Chemical Engineering, Jan, 2011,35 (1),pp 132-148.
[127]Ould-Bouamama, B.,El Harabi, R.,Abdelkrim, M. N., etc., Bond graphs for the diagnosis of chemical processes[J]. Computers & Chemical Engineering, Jan, 2012,36 pp 301-324.
[128]Bauer, M.,Cox, J. W.,Caveness, M. H., etc., Finding the direction of disturbance propagation in a chemical process using transfer entropy[J]. IEEE Transactions on Control Systems Technology, Jan,2007,15 (1),pp 12-21.
[129]Bauer, M.,Cox, J. W.,Caveness, M. H., etc., Nearest neighbors methods for root cause analysis of plantwide disturbances[J]. Industrial & Engineering Chemistry Research, Aug,2007,46 (18),pp 5977-5984.
[130]Bauer, M.,Thornhill, N. F., A practical method for identifying the propagation path of plant-wide disturbances[J]. Journal of Process Control, Aug-Sep,2008,18 (7-8),pp 707-719.
[131]Thambirajah, J.,Benabbas, L.,Bauer, M., etc., Cause-and-effect analysis in chemical processes utilizing XML, plant connectivity and quantitative process history[J]. Computers & Chemical Engineering, Feb,2009,33 (2),pp 503-512.
[132]Cheung, J. T. Y.,Stephanopoulos, G., Representation of process trends—Part I. A formal representation framework[J]. Computers & Chemical Engineering,1990, 14 (4),pp 495-510.
[133]Janusz, M. E.,Venkatasubramanian, V., Automatic generation of qualitative descriptions of process trends for fault detection and diagnosis[J]. Engineering Applications of Artificial Intelligence,1991,4 (5),pp 329-339.
[134]Maurya, M. R.,Rengaswamy, R.,Venkatasubramanian, V., Fault diagnosis using dynamic trend analysis:A review and recent developments[J]. Engineering Applications of Artificial Intelligence, Mar,2007,20 (2),pp 133-146.
[135]Maurya, M. R.,Paritosh, P. K.,Rengaswamy, R., etc., A framework for on-line trend extraction and fault diagnosis[J]. Engineering Applications of Artificial Intelligence, Sep,2010,23 (6),pp 950-960.
[136]Charbonnier, S.,Portet, F., A self-tuning adaptive trend extraction method for process monitoring and diagnosis[J]. Journal of Process Control, Jul,2012,22 (6),pp 1127-1138.
[137]Choudhury, M.,Shah, S. L.,Thornhill, N. F., Diagnosis of poor control-loop performance using higher-order statistics[J]. Automatica, Oct,2004,40 (10),pp 1719-1728.
[138]Choudhury, M.,Shah, S. L.,Thornhill, N. F., etc., Automatic detection and quantification of stiction in control valves[J]. Control Engineering Practice,2006,14 (12),pp 1395-1412.
[139]Choudhury, M., Troubleshooting Plantwide Oscillations using Nonlinearity Information[J]. Journal of Chemical Engineering,2010,24 (0),pp 50-60.
[140]Zang, X.,Howell, J., Isolating the source of whole-plant oscillations through bi-amplitude ratio analysis[J]. Control Engineering Practice,2007,15 (1),pp 69-76.
[141]Jelali, M.,Huang, B., Detection and Diagnosis of Stiction in Control Loops: State of the Art and Advanced Methods[M]. London:Springer:2010.
[142]Kruger, U.,Kumar, S.,Littler, T., Improved principal component monitoring using the local approach[J]. Automatica, Sep,2007,43 (9),pp 1532-1542.
[143]Li, W. H.,Yue, H. H.,Valle-Cervantes, S., etc., Recursive PCA for adaptive process monitoring[J]. Journal of Process Control, Oct,2000,10 (5),pp 471-486.
[144]Kramer, M. A., Nonlinear principal component analysis using autoassociative neural networks[J]. AIChE Journal,1991,37 (2),pp 233-243.
[145]Scholkopf, B.,Smola, A.,MUller, K. R., Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural Computation,1998,10(5),pp 1299-1319.
[146]Ku, W. F.,Storer, R. H.,Georgakis, C., Disturbance detection and isolation by dynamic principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, Nov,1995,30 (1),pp 179-196.
[147]Kruger, U.,Dimitriadis, G., Diagnosis of process faults in chemical systems using a local partial least squares approach[J]. AIChE Journal,2008,54 (10),pp 2581-2596.
[148]Ge, Z.,Kruger, U.,Lamont, L., etc., Fault detection in non-Gaussian vibration systems using dynamic statistical-based approaches[J]. Mechanical Systems and Signal Processing,2010,24 (8),pp 2972-2984.
[149]Ge, Z.,Xie, L.,Kruger, U., etc., Local ICA for multivariate statistical fault diagnosis in systems with unknown signal and error distributions[J]. AIChE Journal, 2012,58 (8),pp 2357-2372..
[150]Basseville, M., On-board component fault detection and isolation using the statistical local approach[J]. Automatica, Nov,1998,34(11),pp 1391-1415.
[151]Benveniste, A.,Basseville, M.,Moustakides, G. V., The Asymptotic Local Approach to Change Detection and Model Validation[J]. IEEE Transactions on Automatic Control, Jul,1987,32 (7),pp 583-592.
[152]Zhang, Q.,Basseville, M.,Benveniste, A., Early Warning of Slight Changes in Systems[J]. Automatica, Jan,1994,30 (1),pp 95-113.
[153]Box, G. E. P., Some theorems on quadratic forms applied in the study of analysis of variance problems, I. Effect of inequality of variance in the one-way classification[J]. The Annals of Mathematical Statistics,1954,25 (2),pp 290-302.
[154]Russell, E. L.,Chiang, L. H.,Braatz, R. D., Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, May,2000,51 (1),pp 81-93.
[155]Sun, L.,Ji, S.,Yu, S., etc. On the equivalence between canonical correlation analysis and orthonormalized partial least squares[A], In 2009; pp 1230-1235.
[156]Hardle, W.,Simar, L., Applied multivariate statistical analysis[M]. Springer Verlag:2011.
[157]McNabb, C. A.,Qin, S. J., Projection based MIMO control performance monitoring:I-covariance monitoring in state space[J]. Journal of Process Control, Dec,2003,13 (8),pp 739-757.
[158]Yu, J.,Qin, S. J., Statistical MIMO controller performance monitoring. Part I: Data-driven covariance benchmark[J]. Journal of Process Control, Mar-Apr,2008,18 (3-4),pp 277-296.
[159]Isserlis, L., On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables[J]. Biometrika,1918,12 (1/2),pp 134-139.
[160]Kramer, M. A.,Palowitch, B. L., A rule-based approach to fault diagnosis using the signed directed graph[J]. AIChE Journal, Jul,1987,33 (7),pp 1067-1078.
[161]Chang, C. C.,Yu, C. C., On-line fault diagnosis using the signed directed graph[J]. Industrial & Engineering Chemistry Research, Jul,1990,29 (7),pp 1290-1299.
[162]Leyval, L.,Gentil, S.,Feraybeaumont, S., Model-based causal reasoning for process supervision[J]. Automatica, Aug,1994,30 (8),pp 1295-1306.
[163]Bhushan, M.,Rengaswamy, R., Design of sensor network based on the signed directed graph of the process for efficient fault diagnosis[J]. Industrial & Engineering Chemistry Research, Apr,2000,39 (4),pp 999-1019.
[164]Montmain, J.,Gentil, S., Dynamic causal model diagnostic reasoning for online technical process supervision[J]. Automatica, Aug,2000,36 (8),pp 1137-1152.
[165]Palmer, C.,Chung, P. W. H., Creating signed directed graph models for process plants[J]. Industrial & Engineering Chemistry Research, Jul,2000,39 (7),pp 2548-2558.
[166]Yang, F.,Shah, S. L.,Xiao, D., Signed directed graph based modeling and its validation from process knowledge and process data[J]. International Journal of Applied Mathematics and Computer Science, Mar,2012,22 (1),pp 41-53.
[167]Maurya, M. R.,Rengaswamy, R.,Venkatasubramanian, V., A signed directed graph and qualitative trend analysis-based framework for incipient fault diagnosis[J]. Chemical Engineering Research & Design, Oct,2007,85 (A10),pp 1407-1422.
[168]Gao, D.,Wu, C.,Zhang, B., etc., Signed Directed Graph and Qualitative Trend Analysis Based Fault Diagnosis in Chemical Industry[J]. Chinese Journal of Chemical Engineering, Apr,2010,18 (2),pp 265-276.
[169]Yim, S. Y.,Ananthakumar, H. G.,Benabbas, L., etc., Using process topology in plant-wide control loop performance assessment[J]. Computers & Chemical Engineering, Dec 1,2006,31 (2),pp 86-99.
[170]Desborough, L.,Miller, R. Increasing customer value of industrial control performance monitoring-Honeywell's experience[A], In AIChE Symposium Series[C], 2002; pp 169-189.
[171]Stockmann, M.,Haber, R.,Schmitz, U., Source identification of plant-wide faults based on k nearest neighbor time delay estimation[J]. Journal of Process Control, Mar,2012,22 (3),pp 583-598.
[172]Wiener, N., The theory of prediction. In Modern mathematics for the engineer F., B. E., Ed. McGraw-Hill.:New York,1956.
[173]Granger, C. W. J., Investigating causal relations by econometric models and cross-spectral methods[J]. Econometrica,1969,37 (3),pp 424-438.
[174]Schreiber, T., Measuring information transfer[J]. Physical Review Letters, Jul 10,2000,85 (2),pp 461-464.
[175]Barnett, L.,Barrett, A. B.,Seth, A. K., Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables[J]. Physical Review Letters, Dec 4, 2009,103 (23),p 238701.
[176]Keller, K.,Sinn, M., Ordinal analysis of time series[J]. Physica A-Statistical Mechanics and Its Applications, Oct 1,2005,356 (1),pp 114-120.
[177]Keller, K.,Sinn, M.,Emonds, J., Time series from the ordinal viewpoint[J]. Stochastics and Dynamics, Jun,2007,7 (2),pp 247-272.
[178]Palus, M.,Komarek, V.,Hrncir, Z., etc., Synchronization as adjustment of information rates:Detection from bivariate time series[J]. Physical Review E, Apr, 2001,63 (4),p 046211.
[179]Schreiber, T., Detecting and analyzing nonstationarity in a time series using nonlinear cross predictions[J]. Physical Review Letters, Feb,1997,78 (5),pp 843-846.
[180]Hlavackova-Schindler, K.,Palus, M.,Vejmelka, M., etc., Causality detection based on information-theoretic approaches in time series analysis[J]. Physics Reports-Review Section of Physics Letters, Mar,2007,441 (1),pp 1-46.
[181]Staniek, M.,Lehnertz, K., Symbolic transfer entropy[J]. Physical Review Letters, Apr 18,2008,100 (15),p 158101.
[182]Kugiumtzis, D., Transfer Entropy on Rank Vectors[J]. e-print arXiv:1007.0357vl,2010.
[183]Downs, J. J.,Vogel, E. F., A plant-wide industrial process control problem[J]. Computers & Chemical Engineering, Mar,1993,17 (3),pp 245-255.
[184]Ricker, N. L., Decentralized control of the Tennessee Eastman Challenge Process[J]. Journal of Process Control, Aug,1996,6 (4),pp 205-221.
[185]丛雨.气动执行阀粘滞特性建模与参数辨识方法研究[D].浙江大学2010.
[186]Kozub, D. J. Controller performance monitoring and diagnosis:experiences and challenges [A], In 1997; pp 83-96.
[187]Qin, S. J.,Badgwell, T. A., A survey of industrial model predictive control technology [J]. Control Engineering Practice,2003,11 (7),pp 733-764.
[188]Huang, B., Multivariable model validation in the presence of time-variant disturbance dynamics[J]. Chemical Engineering Science,2000,55 (20),pp 4583-4595.
[189]Huang, B., On-line closed-loop model validation and detection of abrupt parameter changes[J]. Journal of Process Control,2001,11 (6),pp 699-715.
[190]Jiang, H.,Huang, B.,Shah, S. L., Closed-loop model validation based on the two-model divergence method[J]. Journal of Process Control,2009,19 (4),pp 644-655.
[191]Badwe, A. S.,Gudi, R. D.,Patwardhan, R. S., etc., Detection of model-plant mismatch in MPC applications[J]. Journal of Process Control,2009,19 (8),pp 1305-1313.
[192]Overschee, P. V.,Moor, B.,Hensher, D. A., etc., Subspace Identification for the Linear Systems:Theory-Implementation[M]. Kluwer academic publishers:1996.
[193]Harrison, C. A.,Qin, S. J., Discriminating between disturbance and process model mismatch in model predictive control[J]. Journal of Process Control,2009,19 (10),pp 1610-1616.
[194]Ljung, L., System identification:theory for the user[M]. Prentice Hall PTR: 1999.
[195]Huang, B.,Kadali, R., Dynamic modeling, predictive control and performance monitoring:A data-driven subspace approach[M]. Springer Verlag:2008.
[196]Benveniste, A.,Fuchs, J. J., Single Sample Modal Identification of a Nonstationary Stochastic-Process[J]. IEEE Transactions on Automatic Control,1985, 1985,30(1),pp 66-74.
[197]Moustakides, G. V.,Benveniste, A., Detecting changes in the AR parameters of a nonstationary ARMA process[J]. Stochastics:An International Journal of Probability and Stochastic Processes,1986,16(1-2),pp 137-155.
[198]Kammer, L. C.,Gorinevsky, D.,Dumont, G. A., Semi-intrusive multivariable model invalidation[J]. Automatica, Aug,2003,39 (8),pp 1461-1467.
[199]Selvanathan, S.,Tangirala, A. K., Diagnosis of Poor Control Loop Performance Due to Model-Plant Mismatch[J]. Industrial & Engineering Chemistry Research, May 5,2010,49 (9),pp 4210-4229.
[200]Webber, J. R.,Gupta, Y. P., A closed-loop cross-correlation method for detecting model mismatch in MIMO model-based controllers[J]. ISA Transactions, 2008,47 (4),pp 395-400.
[201]Shannon, C. E., Weaver, W., A mathematical theory of communication[J]. The Bell System Technical Journal,1948,27 (3),pp 379-423.
[202]Zhang, L. F.,Zhu, Q. M.,Longden, A., A set of novel correlation tests for nonlinear system variables[J]. International Journal of Systems Science, Jan,2007,38 (1),pp 47-60.
[203]Zhang, L. F.,Zhu, Q. M.,Longden, A., A Correlation-Test-Based Validation Procedure for Identified Neural Networks[J]. IEEE Transactions on Neural Networks, Jan,2009,20(1),pp 1-13.
[204]Kraskov, A.,Stogbauer, H.,Grassberger, P., Estimating mutual information[J]. Physical Review E, Jun,2004,69 (6),p 066138.
[205]Moon, Y. I.,Rajagopalan, B.,Lall, U., Estimation of mutual information using kernel density estimators[J]. Physical Review E, Sep,1995,52 (3),pp 2318-2321.
[206]Khan, S.,Bandyopadhyay, S.,Ganguly, A. R., etc., Relative performance of mutual information estimation methods for quantifying the dependence among short and noisy data[J]. Physical Review E, Aug,2007,76 (2),p 026209.
[207]Papana, A.,Kugiumtzis, D., Evaluation of Mutual Information Estimators for Time Series[J]. International Journal of Bifurcation and Chaos, Dec,2009,19 (12),pp 4197-4215.
[208]Roulston, M. S., Significance testing of information theoretic functionals[J]. Physica D-Nonlinear Phenomena, Dec 1,1997,110 (1-2),pp 62-66.
[209]Palus, M.,Vejmelka, M., Directionality of coupling from bivariate time series: How to avoid false causalities and missed connections[J]. Physical Review E, May, 2007,75 (5),p 056211.
[210]Schreiber, T.,Schmitz, A., Improved surrogate data for nonlinearity tests[J]. Physical Review Letters, Jul,1996,77 (4),pp 635-638.
[211]Schreiber, T.,Schmitz, A., Surrogate time series[J]. Physica D-Nonlinear Phenomena, Aug 15,2000,142 (3-4),pp 346-382.
[212]Joe, H., Relative Entropy Measures of Multivariate Dependence[J]. Journal of the American Statistical Association,1989,84 (405),pp 157-164.
[213]Estevez, P. A.,Tesmer, M.,Perez, C. A., etc., Normalized Mutual Information Feature Selection[J]. IEEE Transactions on Neural Networks, Feb,2009,20 (2),pp 189-201.
[214]Stanfelj, N.,Marlin, T. E.,Macgregor, J. F., Monitoring and diagnosing process control performance:the single-loop case[J]. Industrial & Engineering Chemistry Research, Feb,1993,32 (2),pp 301-314.
[215]Huang, B. A., On-line closed-loop model validation and detection of abrupt parameter changes[J]. Journal of Process Control,2001,11 (6),pp 699-715.
[216]Jiang, H. L.,Huang, B.,Shah, S. L., Closed-loop model validation based on the two-model divergence method[J]. Journal of Process Control,2009,19 (4),pp 644-655.
[217]Nowak, R. D.,Vanveen, B. D., Random and pseudorandom inputs for Volterra filter identification[J]. IEEE Transactions on Signal Processing, Aug,1994, 42 (8),pp 2124-2135.
[218]Arefi, M. M.,Montazeri, A.,Poshtan, J., etc., Wiener-neural identification and predictive control of a more realistic plug-flow tubular reactor[J]. Chemical Engineering Journal, May 1,2008,138 (1-3),pp 274-282.
[219]Voros, J., Parameter identification of Wiener systems with discontinuous nonlinearities[J]. Systems & Control Letters, Dec 14,2001,44 (5),pp 363-372.
[220]Hasiewicz, Z.,Mzyk, G., Combined parametric-nonparametric identification of Hammerstein systems[J]. IEEE Transactions on Automatic Control, Aug,2004,49 (8),pp 1370-1375.
[221]Sutter, E. E., A practical nonstochastic approach to nonlinear time-domain analysis[J]. Advanced methods of physiological systems modeling,1987,1 pp 303-315.
[222]Zhu, Y., Multivariable system identification for process control[M]. Oxford: Elsevier Science:2001.
[223]Abarbanel, H. D. I., Analysis of observed chaotic data[M]. New York: Springer Verlag:1996.
[224]Grune, L.,Pannek, J., Nonlinear Model Predictive Control:Theory and Algorithms[M]. Springer Verlag:2011.