复杂工况过程统计监测方法研究
|
摘要
保证过程安全和提高产品质量是现代流程工业迫切需要解决的两个问题,作为过程系统工程领域的关键技术之一,过程监测技术是解决这两个问题的有效途径。由于集散控制系统(DCS)在流程工业中的广泛应用,极大地丰富了过程的数据信息,基于数据驱动的过程监测技术在过去十多年间得到了长足的发展。其中,多变量统计过程监测方法(MSPC)更是受到了学术界和工业界的普遍关注,已经成为过程监测领域的研究热点之一。
但是,传统的MSPC方法对过程的限制条件较多,如过程数据必须服从高斯分布、线性、稳定单一工况等,本文在前人研究工作的基础上,针对不同的复杂工况过程,提出多种有效的过程监测方法,具体包括:
(1)针对过程数据的复杂分布情况,提出一种基于独立成分分析和主元分析(ICA-PCA)两步信息提取策略的过程监测方法,并引入支持向量数据描述(SVDD)和因子分析(FA)方法对提出的方法进行了改进。其中,提出的基于SVDD重构的故障诊断方法解决了非高斯故障诊断的难点,是对重构类故障诊断方法的重要补充;提出的基于混合因子的故障识别方法也在很大程度上改善了故障识别的效果。
(2)针对非线性工况过程,在基本的核主元分析(KPCA)方法的基础上,引入统计局部技术(LA)对其进行改进,新的方法消除了对过程数据分布的限制条件。为了简化传统非线性过程监测算法建模和在线实施的复杂性,提出了一种基于线性子空间集成和Bayesian推理的过程监测方法,不仅有效地提高了监测效果,而且在很大程度上降低了算法的复杂度。
(3)针对过程的时变和多工况特性,并同时考虑到过程的复杂数据分布和非线性情况,提出了三种新的方法,即基于局部最小二乘回归(LSSVR)模型的方法、基于非线性外部分析的鲁棒方法以及基于二维线性子空间集成和Bayesian推理的方法。其中,基于局部模型的方法有效地改善了传统递归方法的不足,增强了时变过程监测的实时性;非线性外部分析鲁棒算法不仅有效地推广了外部分析方法在非线性过程中的应用,而且增强了该算法抗击噪声和离群点的能力;提出的二维监测方法不仅在很大程度上减弱了过程监测对知识和经验的依赖性,而且取得了很好的效果。
(4)同时考虑过程动态性和非高斯性的监测方法研究目前还比较少,本文在子空间模型辨识方法的基础上,通过引入统计局部技术,提出一种基于SMILA的动态非高斯过程监测方法,相比已有的动态非高斯过程监测方法,新的方法获得了更满意的监测性能。
(5)相比连续生产过程,间歇生产过程要复杂的多,由于目前对于多工况复杂间歇过程监测的研究还比较少,本文特别针对这种类型的间歇过程,提出一种基于Bayesian推理的监测方法,并将其推广至更为复杂的多阶段间歇生产过程中。
最后,在总结全文的基础上,对过程监测领域的未来工作进行了展望。
Process safety and product quality are two important issues that should be paid great attentions by modern industrial processes. As one of the key technologies in the process system engineering area, process monitoring can be employed to solve those two aspects. According to the wide use of the distribution control system in industrial processes, large amounts of data were collected, which have greatly accelerated the development of data-based process monitoring methods along the past decade. Particularly, the multivariate statistical process control (MSPC) method has gained greatly attentions both in industry and academy. and has become a hot spot in process monitoring area.
However, the traditional MSPC method is limited to Gaussian, linear, stationary and single mode processes. Based on the existing research works, this dissertation proposes several efficient monitoring methods for different complex processes, which are summarized as follows.
(1) According to the complex distribution of the process data, a two-step independent component analysis and principal component analysis (ICA-PCA) based information extraction strategy is proposed for process monitoring, which is sequently improved by support vector data description (SVDD) and factor analysis (FA). A new SVDD reconstruction based method is proposed to address the difficulty of non-Gausian fault diagnosis problem, which can be considered as a complement of reconstruction-based methods for fault diagnosis. Additionally, a mixed index is proposed for fault identification, which can also improve the performance of process monitoring.
(2) For nonlinear process monitoring, the statistical local approach (LA) is introduced upon the traditional kernel PCA modeling structure, which can effectively eliminate the restriction of Gaussian distribution for the process data. Due to the offline modeling and online implementation difficulties of the existing methods, a new viewpoint is proposed for nonlinear process monitoring, which is based on linear subspace integration and Bayesian inference. Compared to the existing nonlinear methods, the new method can both improve the monitoring performance and reduce the algorithm complexity.
(3) In order to improve the monitoring performance for time-varying and multimode processes, three new methods are proposed. A local least squares support vector regression (LSSVR) based method is proposed, which can greatly enhance the real-time performance of the method; A robust nonlinear external analyais is proposed, which extends the conventional linear external analysis to nonlinear processes, and simultaneously improves the robustness of the method to noises and outliers; A two-dimensional process monitoring method is also proposed, which greatly alleviates the lean of the monitoring method to process knowledge and experiences.
(4) Due to the research status on non-Gaussian dynamic processes, few works have been reported. In this dissertation, a new monitoring method is proposed for these special processes, which is based on subspace model identification (SMI) and local approach (LA). In contrast to other methods, the new proposed SMILA method is more efficient in monitoring non-Gaussian dynamic processes.
(5) The batch process is considered to be more complicated than continuous processes. Due to the lack of the monitoring research work on multimode batch process monitoring, a Bayesian inference based method is proposed for this special kind of processes, which is also extended for monitoring multiphase batch processes.
Finally, Conlcusions and future research studies of the process monitoring ares are illustrated.
但是,传统的MSPC方法对过程的限制条件较多,如过程数据必须服从高斯分布、线性、稳定单一工况等,本文在前人研究工作的基础上,针对不同的复杂工况过程,提出多种有效的过程监测方法,具体包括:
(1)针对过程数据的复杂分布情况,提出一种基于独立成分分析和主元分析(ICA-PCA)两步信息提取策略的过程监测方法,并引入支持向量数据描述(SVDD)和因子分析(FA)方法对提出的方法进行了改进。其中,提出的基于SVDD重构的故障诊断方法解决了非高斯故障诊断的难点,是对重构类故障诊断方法的重要补充;提出的基于混合因子的故障识别方法也在很大程度上改善了故障识别的效果。
(2)针对非线性工况过程,在基本的核主元分析(KPCA)方法的基础上,引入统计局部技术(LA)对其进行改进,新的方法消除了对过程数据分布的限制条件。为了简化传统非线性过程监测算法建模和在线实施的复杂性,提出了一种基于线性子空间集成和Bayesian推理的过程监测方法,不仅有效地提高了监测效果,而且在很大程度上降低了算法的复杂度。
(3)针对过程的时变和多工况特性,并同时考虑到过程的复杂数据分布和非线性情况,提出了三种新的方法,即基于局部最小二乘回归(LSSVR)模型的方法、基于非线性外部分析的鲁棒方法以及基于二维线性子空间集成和Bayesian推理的方法。其中,基于局部模型的方法有效地改善了传统递归方法的不足,增强了时变过程监测的实时性;非线性外部分析鲁棒算法不仅有效地推广了外部分析方法在非线性过程中的应用,而且增强了该算法抗击噪声和离群点的能力;提出的二维监测方法不仅在很大程度上减弱了过程监测对知识和经验的依赖性,而且取得了很好的效果。
(4)同时考虑过程动态性和非高斯性的监测方法研究目前还比较少,本文在子空间模型辨识方法的基础上,通过引入统计局部技术,提出一种基于SMILA的动态非高斯过程监测方法,相比已有的动态非高斯过程监测方法,新的方法获得了更满意的监测性能。
(5)相比连续生产过程,间歇生产过程要复杂的多,由于目前对于多工况复杂间歇过程监测的研究还比较少,本文特别针对这种类型的间歇过程,提出一种基于Bayesian推理的监测方法,并将其推广至更为复杂的多阶段间歇生产过程中。
最后,在总结全文的基础上,对过程监测领域的未来工作进行了展望。
Process safety and product quality are two important issues that should be paid great attentions by modern industrial processes. As one of the key technologies in the process system engineering area, process monitoring can be employed to solve those two aspects. According to the wide use of the distribution control system in industrial processes, large amounts of data were collected, which have greatly accelerated the development of data-based process monitoring methods along the past decade. Particularly, the multivariate statistical process control (MSPC) method has gained greatly attentions both in industry and academy. and has become a hot spot in process monitoring area.
However, the traditional MSPC method is limited to Gaussian, linear, stationary and single mode processes. Based on the existing research works, this dissertation proposes several efficient monitoring methods for different complex processes, which are summarized as follows.
(1) According to the complex distribution of the process data, a two-step independent component analysis and principal component analysis (ICA-PCA) based information extraction strategy is proposed for process monitoring, which is sequently improved by support vector data description (SVDD) and factor analysis (FA). A new SVDD reconstruction based method is proposed to address the difficulty of non-Gausian fault diagnosis problem, which can be considered as a complement of reconstruction-based methods for fault diagnosis. Additionally, a mixed index is proposed for fault identification, which can also improve the performance of process monitoring.
(2) For nonlinear process monitoring, the statistical local approach (LA) is introduced upon the traditional kernel PCA modeling structure, which can effectively eliminate the restriction of Gaussian distribution for the process data. Due to the offline modeling and online implementation difficulties of the existing methods, a new viewpoint is proposed for nonlinear process monitoring, which is based on linear subspace integration and Bayesian inference. Compared to the existing nonlinear methods, the new method can both improve the monitoring performance and reduce the algorithm complexity.
(3) In order to improve the monitoring performance for time-varying and multimode processes, three new methods are proposed. A local least squares support vector regression (LSSVR) based method is proposed, which can greatly enhance the real-time performance of the method; A robust nonlinear external analyais is proposed, which extends the conventional linear external analysis to nonlinear processes, and simultaneously improves the robustness of the method to noises and outliers; A two-dimensional process monitoring method is also proposed, which greatly alleviates the lean of the monitoring method to process knowledge and experiences.
(4) Due to the research status on non-Gaussian dynamic processes, few works have been reported. In this dissertation, a new monitoring method is proposed for these special processes, which is based on subspace model identification (SMI) and local approach (LA). In contrast to other methods, the new proposed SMILA method is more efficient in monitoring non-Gaussian dynamic processes.
(5) The batch process is considered to be more complicated than continuous processes. Due to the lack of the monitoring research work on multimode batch process monitoring, a Bayesian inference based method is proposed for this special kind of processes, which is also extended for monitoring multiphase batch processes.
Finally, Conlcusions and future research studies of the process monitoring ares are illustrated.
引文
Alawi A., Choi S. W., Martin E. B., Morris A. J., Yue Z. H. (2007). Sensor fault identification using weighted combined contribution plots, in. Fault Detection,Supervision and Safety of Technical Processes 2006, Elsevier Science Ltd,Oxford, 908-913.
Alcala C, Qin S. J. (2008). Reconstruction-based contribution for process monitoring. Proceeding of the 17~(th) world congress, The International Federation of Automatic Control, Seoul, Korea, July 6-11.
Amari S. L., Cichocki A., Yang H. (1996). A New Learnig Algorithm for Blind Source Separation. Advances in Neural Information Processing Systems, 8:757-763.
Baffi G., Martin E. B., Morris A.J. (1999). Nonlinear projection to latent structure revisited: the quadratic PLS algorithm. Computers & Chemical Engineering,23(3): 395-411.
Baffi G., Martin E. B., Morris A.J. (2002). Non-linear model based predictive control through dynamic non-linear partial least squares. Chemical Engineering Research&Design, 80(1): 75-86.
Bajpai R. K., Reuss M. (1980). A mechanistic model for penicillin production. Journal of Chemical Technology and Biotechnology, 30: 332-344.
Bakshi B. R. (1998). Multiscale PCA with applications to multivariate statistical process monitoring. AIChE Journal, 44: 1596-1610.
Basseville M., Nikiforov I. (1993). Detection of abrupt changes-Theory and Application. Prentice Hall, N.J.
Basseville M. (1998). On-board component fault detection and isolation using the statistical local approach. Automatica, 34: 1391-1415.
Bell A. J., Sejnowski T. J. (1995). An Information-Maximisation Approach to Blind Separation and Blind Deconvolution. Neural Computation, 7(6): 10004-1034.
Bicego M., Figueiredo M. A. T. (2009). Soft clustering using weighted one-class support vector machines. Pattern recognition, 42: 27-32.
Birol G., Undey C., Cinar A. (2002). A modular simulation package for fed-batch fermentation: Penicillin production. Computers & Chemical Engineering, 26:1553-1561.
Bishop C. M. (2006). Pattern recognition and machine learning. Springle.Camacho J., Pico J. (2006). Online monitoring of batch processes using multiphase principal component analysis. Journal of Process Control, 10: 1021-1035.
Camacho J., Pico J., Ferrer A. (2007). Self-tuning run to run optimization of fed-batch processes using unfold-PLS. AIChE Journal, 53(7): 1789-1804.
Camacho J., Pico J., Ferrer A. (2008a). Bilinear modelling of batch processes. Part I: theoretical discussion. Journal of Chemometrics, 22: 299-308.
Camacho J., Pico J., Ferrer A. (2008b). Bilinear modelling of batch processes. Part II: a comparison of PLS soft-sensors. Journal of Chemometrics, 22: 533-547.
Camacho J., Pico J., Ferrer A. (2008c). Multi-phase analysis framework for handling batch process data. Journal of Chemometrics, 22: 632-643.
Chen J. H., Liu J. L. (1999). Mixture principal component analysis models for process monitoring. Industrial & Engineering Chemistry Research, 38:1478-1488.
Chen J. H., Liu J. L. (2000). Using mixture principal component analysis networks to extract fuzzy rules from data. Industrial & Engineering Chemistry Research, 39: 2355-2367.
Chen J. H., Liu K. C. (2002). On-line batch process monitoring using dynamic PCA and dynamic PLS models. Chemical Engineering Science, 57(1): 63-75.
Chen J. H., Chen H. H. (2006). On-line batch process monitoring using MHMT-based MPCA, Chemical Engineering Science 61: 3223-3239.
Chen Q., Wynne R. J., Goulding P., Sandoz, D. (2000). The application of principal component analysis and kernel density estimation to enhance process monitoring. Control Engineering Practice, 8(5): 531-543.
Chen Q., Kruger U., Leung A. T Y. (2004). Regularised kernel density estimation for clustered process data. Control Engineering Practice, 12: 267-274.
Chen W. C., Wang M. S. (2009). A fuzzy c-means clustering-based fragile watermarking scheme for image authentication. Expert Systems with Applications, 36: 1300-1307.
Cheng C., Chiu M.S. (2005). Nonlinear process monitoring using JITL-PCA.Chemometrics and Intelligent Laborator Syetems, 76: 1-13.
Cherry G. A., Qin S. J. (2006). Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis IEEE Transactions on Semiconductor Manufactory, 19: 159-172.
Chiang L. H., Braatz R. D. (2001). Fault detection and diagnosis in industrial systems. Great Britain: Springer-Verlag London limited.
Cho J.H., Lee J.M., Choi S.W., Lee D.W., Lee I.B. (2005). Fault identification for process monitoring using kernel principal component analysis. Chemical Engineering Science, 60: 279-288.
Choi S. W., Park J. H., Lee I. B. (2004). Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis.Computers & Chemical Engineering, 28: 1377-1387.
Choi S.W., Lee C.K., Lee J.M., Park J.H., Lee I.B. (2005). Fault detection and identification of nonlinear processes based on kernel PCA. Chemometrics and Intelligent Laborator Syetems, 75: 55-67.
Choi S. W., Morris A. J., Lee I. B. (2008a). Dynamic model-based batch process monitoring Chemical Engineering Science 63: 622-636.
Choi S. W., Morris A. J., Lee I. B. (2008b). Nonlinear multiscale modeling for fault detection and identification. Chemical Engineering Science, 63(8):2252-2266.
Conlin A. K., Martin E. B., Morris A. J. (2000). Confidence limits for contribution plots. Journal of Chemometrics, 14(5): 725-736.
Dayal B. S., MacGregor J. F. (1997). Improved PLS algorithms. Journal of Chemometrics, 11: 73-85.
Dong D., McAvoy T. J. (1996). Nonlinear principal component analysis based on principal curves and neural networks. Computers & Chemical Engineering,20: 65-78.
Downs J. J., Vogel E. F. (1993). A plant-wide industrial process control problem.Computers & Chemical Engineering, 17: 245-253.
Dunia R., Qin S. J., Edgar T. F., McAvoy T. J. (1996a). Use of principal component analysis for sensor fault identification. Computers & Chemical Engineering, 20: 713-718.
Dunia R., Qin S. J., Edgar T. F., McAvoy T. J. (1998b). Identification of faulty sensors using principal component analysis. AIChE Journal, 42(10):2797-2812.
Dunia R., Qin S. J. (1998a). A unified geometric approach to process and sensor fault identification: the unidimensional fault case. Computers & Chemical Engineering, 22: 927-943.
Dunia R., Qin S. J. (1998b). Subspace approach to multidimensional fault identification and reconstruction. AIChE Joural, 44(8): 1813-1831.
Ergon R. (2009). Informative score-loading-contribution plots for multi-response process monitoring. Chemometrics and Intelligent Laborator Syetems, 95(1):31-34.
Frank, P. M. (1990). Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy-A survey and some new results. Automatica,26(3): 459-474.
Frank P. M. (1996). Analytical and qualitative model-based fault diagnosis-A survey and some new results. European Journal of Control, 2: 6-23.
Ge Z. Q., Song Z. H. (2007). Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors.Industrial & Engineering Chemistry Research, 46: 2054-2063.
Ge Z. Q., Yang C. J., Song Z. H., Wang H. Q. (2008). Robust Online Monitoring for Multimode Processes Based on Nonlinear External Analysis. Industrial &Engineering Chemistry Research, 47: 4775-4783.
Gertler J. (1998). Fault detection and diagnosis in engineering systems. Marcel Dekker, New York.
Gertler J., Cao J. (2005). Design of optimal structured residuals from partial principal component models for fault diagnosis in linear systems. Journal of Process Control, 15(5): 585-603.
Gunther J. C., Baclaski J., Seborg D. E., Conner J. S. (2009). Pattern matching in batch bioprocesses-Comparisons across multiple products and operating conditions. Computers & Chemical Engineering, 33(1): 88-96.
Hastie T., Stuetzle W. (1989). Principal curves. Journal of America Statistical Association, 84: 502-516.
He Q. P., Wang J. (2007). Fault detection using the k-Nearest neighbor rule for semiconductor manufacturing processes. IEEE Transactions on Semiconductor Manufactory, 20: 345-354, 2007.
He X. B., Yang Y. P. (2008). Variable MWPCA for adaptive process monitoring.Industrial & Engineering Chemistry Research, 47:419-427.
Hiden H. G., Willis M. J., Tham M. T., Montague G. A. (1999). Non-linear principal component analysis using genetic programming. Chemometrics and Intelligent Laborator Syetems, 23: 413-425.
Hu K. L., Yuan J. Q. (2008a). Statistical monitoring of fed-batch process using dynamic multiway neighborhood preserving embedding. Chemometrics and Intelligent Laborator Syetems, 90(2): 195-203.
Hu K. L., Yuan J. Q. (2008b). Multivariate statistical process control based on multiway locality preserving projections. Journal of Process control 18:797-807.
Hwang D. H., Han C. (1999). Real-time monitoring for a process with multiple operating modes. Control Engineering Practice, 7: 891-902.
Hyv(?)rinen A. (1997). A fast fixed-point algorithm for independent component analysis. Neural Computation, 9: 1483-1492.
Hyv(?)rinen A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3):626-634.
Hyv(?)rinen A., Oja E. (2000). Independent component analysis: algorithms and applications. Neural Network, 13: 411-430.
Isermann R., Balle P. (1997). Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engineering Practice,5(5): 709-719.
Jackson J. E. (1991). A user's guide to principal component analysis. New York:Wiley Inter-science.
Jia F., Martin E. B., Morris A. J. (2001). Nonlinear principal components analysis with application to process fault detection. International Journal of Systems Science, 31: 1473-1487.
Jin H. D., Lee Y. H., Lee G., Han C. H. (2006). Robust recursive principal component analysis modeling for adaptive monitoring. Industrial & Engineering Chemistry Research, 45: 696-703.
Johannesmeyer M. C., Singhai A., Seborg D. E. (2002). Pattern matching in historical data. AIChE Journal, 48: 2022-2038.
Juricek B. C., Seborg D. E., Larimore W. E. (2001). Identification of the Tennessee Eastman challenge process with subspace methods. Control Engineering Practice, 9(12): 1337-1351.
Jutten C., Herault J. (1988). Independent component analysis versus PCA. Proceeding of European Signal Processing, 287-314.
Kampjarvi P., Sourander M. Komulainen T., Vatanski N., Nikus M., Jounela S. L.(2008). Fault detection and isolation of an on-line analyzer for an ethylene cracking process. Control Engineering Practice, 16(1): 1-13.
Kano M., Nagao K., Hasebe H., Hashimoto I., Ohno H., Strauss R., Bakshi B. R.(2002). Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem. Computers & Chemical Engineering, 26: 161-174.
Kano M., Tanaka S., Hasebe S., Hashimoto I., Ohno H. (2003). Monitoring independent components for fault detection. AIChE Journal, 49(4): 969-976.
Kano M., Hasebe S., Hashimoto I., Ohno H. (2004a). Evolution of multivariate statistical process control: application of independent component analysis and external analysis. Computers & Chemical Engineering, 28: 1157-1166.
Kano M., Tanaka S., Hasebe S., Hashimoto I., Ohno H. (2004b). Combined multivariate statistical process control. 1FAC Symposium on Advanced Control of Chemical Processes (ADCHEM), 303-308.
Kesavan P., Lee J. H. (1997). Diagnostic tools for multivariable model-based control systems. Industrial & Engineering Chemistry Research, 36(7):2725-2738.
Kim D., Lee I. B. (2003). Process monitoring based on probabilistic PCA.Chemometrics and Intelligent Laborator Syetems, 67: 109-123.
Kim D. S., Yoo C. K., Kim Y. I., Jung J. H., Lee I. B. (2005). Calibration,prediction and process monitoring model based on factor analysis for incomplete process data. Journal of Chemical Engineering of Japan. 38(12):1025-1034.
Kourti T. (2003). Multivariate dynamic data modeling for analysis and statistical process control of batch processes, startups and grade transitions. Journal of Chemometrics, 17(1): 93-109.
Kramer M. A. (1991). Nonlinear principal component analysis using autoassociative neural networks. AIChE Journal, 37(2): 233-243.
Krzanowski W. J. (1979). Between-groups comparison of principal components.Journal of America Statistical Association, 74: 703-707.
Kruger U., Zhou Y. Q., Irwin G. W. (2004). Improved principal component monitoring of large-scale processes. Journal of process control, 14: 879-888.
Kruger U., Antory D., Hahn J., Irwin G.W., McCullough G. (2005). Introduction of a nonlinearity measure for principal component models. Computers &Chemical Engineering, 29: 2355-2362.
Kruger U., Kumar S., Littler T. (2007). Improved principal component monitoring using the local approach. Automatica, 43: 1532-1542.
Ku W., Storer R.H., Georgakis C. (1995). Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems, 30: 179-196.
Kumar K. V., Negi A. (2008). SubXPCA and a generalized feature partitioning approach to principal component analysis. Pattern Recognition, 41:1398-1409.
Lane S., Martin E. B., Kooijmans R., Morris A. J. (2001). Performance monitoring of a multi-product semi-batch process. Journal of Process Control, 11: 1-11.
Larimore W. E. (1990). Canonical variate analysis in identification, filtering and adaptive control. In Proceedings of the 29~(th) Conference on Decision and Control, 596-604.
Larimore W. E. (1996). Statistical optimality and canonical variate analysis system identification. Signal Processing, 52: 131-144.
Lee D. S., Park J. M., Vanrolleghem P. A. (2005). Adaptive multiscale principal component analysis for on-line monitoring of a sequencing batch reactor.Journal of Biotechnology, 116(2): 195-210.
Lee D. S., Lee M. W., Woo S. H., Kim Y. J., Park J. M. (2006a). Multivariate online monitoring of a full-scale biological anaerobic filter process using kernel-based algorithms. Industrial & Engineering Chemistry Research,45(12): 4335-4344.
Lee D. S., Lee M. W., Woo S. H., Kim Y. J., Park J. M. (2006b). Nonlinear dynamic partial least squares modeling of a full-scale biological wastewater treatment plant. Process Biochemistry, 41(9): 2050-2057.
Lee J. M., Yoo C. K., Lee I. B. (2004a). Statistical process monitoring with independent component analysis. Journal of Process Control, 14(5): 467-485.
Lee J. M., Yoo C. K., Lee I. B. (2004b). Statistical monitoring of dynamic processes based on dynamic independent component analysis. Chemical Engineering Science, 59(14): 2995-3006.
Lee J. M., Yoo C.K., Choi S.W., Vanrolleghem P.A., Lee I. B. (2004c). Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science, 59: 223-234.
Lee J. M., Qin S. J., Lee I. B. (2006). Fault detection and diagnosis based on modified independent component analysis. AIChE Journal, 52: 3501-3514.
Lee J. M., Qin S. J., Lee 1. B.(2007). Fault detection of non-linear processes using kernel independent component analysis. Canadian Journal of Chemical Engineering, 85(4): 526-536.
Li R. F., Wang X. 2. (2002). Dimension reduction of process dynamic trends using independent component analysis. Computers & Chemical Engineering, 26:467-473.
Li R. Y., Rong G. (2006). Fault isolation by partial dynamic principal component analysis in dynamic process. Chinese Journal of Chemical Engineering, 14(4):486-493.
Li W., Yue H. H., Valle-Cervantes S., Qin S. J. (2000). Recursive PCA for adaptive process monitoring. Journal of Process Control, 10: 471-486.
Lieftucht D., Kruger U., Xie L., Littler T., Chen Q., Wang S. Q. (2006a). Statistical monitoring of dynamic multivariate processes-part 2. identifying fault magnitude and signature. Industrial & Engineering Chemistry Research, 45:1677-1688.
Lieftuche D., Kruger U., Invin G. W. (2006b). Improved reliability in diagnosing faults using multivariate statistics. Computers & Chemical Engineering, 30:901-912.
Liu J., Wong D. S. H. (2008). Fault detection and classification for a two-stage batch process. Journal of Chemometrics, 22: 385-398.
Liu X., Xie L., Kruger U., Littler T., Wang S. (2008). Statistical-based monitoring of multivariate non-Gaussian systems. AIChE Journal, 54: 2379-2391.
Liu X. Q., Xie L., Kruger U., Littler T., Wang S. Q. (2009). Moving window kernel PCA for adaptive monitoring of nonlinear processes. Chemometrics and Intelligent Laborator Syetems, doi: 10.1016/j.chemolab.2009.01.002.
Lu N. Y., Wang F. L., Gao F. R. (2004a). Combination method of principal component and wavelet analysis for multivariate process monitoring and fault diagnosis. Industrial & Engineering Chemistry Research, 42: 4198-4207.
Lu N. Y., Gao F. R., Wang F. L. (2004b). A sub-PCA modeling and online monitoring strategy for batch processes. AIChE Journal, 50: 255-259.
Lu N. Y., Yao Y., Gao F. R., Wang F. L. (2005). Two-dimensional dynamic PCA for batch process monitoring. AIChE Journal, 51(12): 3300-3304.
Lyman P. R., Georgakist C. (1995). Plant-wide Control of the Tennessee Eastman Problem. Computers & Chemical Engineering, 19: 321-331.
Malthouse E. C., Tamhane A. C., Mah R. S. H. (1997). Nonlinear partial least squares. Computers & Chemical Engineering, 21(8): 875-890.
Martin E. B., Morris A. J. (1996). Non-parametric confidence bounds for process performance monitoring charts. Journal of Process Control, 6: 349-358.
Martin E. B., Morris A. J. (2002). Enhanced bio-manufacturing through advanced multivariate statistical technology. Journal of Biotechnology, 99: 223-235.
Maulud A., Wang D., Romagnoli J. A. (2006). A multi-scale orthogonal nonlinear strategy for multi-variate statistical process monitoring. Journal of Process Control, 16: 671-683.
Mika S., Scholkopf B., Smola A., Muller K. R., Scholz M., Ratsch G. (1998).Kernel PCA and de-noising in feature spaces. MIT Press, Denver, Co.,536-542.
Miller P., Swanson R. E., Heckler C. F. (1993). Contribution plots: the missing link in multivariate quality control. Fall Conf. of ASQC and ASA, Milwaukee,WI.
Misra M., Yue H., Qin S. J., Ling C. (2002). Multivariate process monitoring and fault diagnosis by multi-scale PCA. Computers & Chemical Engineering,26(6): 1281-1293.
Negiz A., Cinar A. (1997a). Statistical monitoring of multivariable dynamic processes with state space model. AIChE Journal, 43: 2002-2020.
Nomikos P., MacGregor J. F. (1994). Monitoring batch processes using multiway principal component analysis. AIChE Journal, 44: 1361-1375.
Nomikos P., MacGregor J. F. (1995a). Multi-way partial least square in monitoring batch processes. Chemometrics and Intelligent Laborator Syetems, 30:97-108.
Nomikos P., MacGregor J. F. (1995b). Multivariate SPC charts for monitoring batch process. Technometrics, 37: 41-59.
Overschee P. V., Moor D. M. (1994). System identification for the identification of combined deterministic-stochastic systems. Automatica, 30: 75-93.
Overschee P. V., Moor D. M. (1996). Subspace identification for linear systemes.Kluwer Academic Publishers.
Patton R., Frank P. M., Clark R. (1989). Fault diagnosis in dynamic systems.Englewood Clifs: Prentice Hall.
Qin S. J., McAvoy T. J. (1992). Nonlinear PLS modeling using neural networks. Computers & Chemical Engineering, 16(4): 379-391.
Qin S. J. (1998). Recursive PLS algorithms for adaptive data monitoring.Computers & Chemical Engineering, 22: 503-514.
Qin S. J., Li W. (1999). Detection, identification and reconstruction of faulty sensors with maximized sensitivity. AIChE Journal, 45(9): 1963-1976.
Qin S. J., Li W. (2001). Detection and identification of faulty sensors in dynamic processes. AIChE Journal, 47(7): 1581-1593.
Qin S. J. (2003). Statistical process monitoring: basics and beyond. Journal of Chemometrics, 17: 480-502.
Rannar S., MacGregor J. F., Wold S. (1998). Adaptive batch monitoring using hierarchical PCA. Chemometrics and Intelligent Laborator Syetems, 41:73-81.
Raich A. C., Cinar A. (1995). Multivariate statistical methods for monitoring continuous processes: assessment of discrimination power of disturbance models and diagnosis of multiple disturbances. Chemometrics and Intelligent Laborator Syetems, 30: 37-48.
Raich A. C., Cinar A. (1997). Diagnosis of process disturbance by statistical distance and angle measures. Computers & Chemical Engineering, 21:661-676.
Reis M. S., Saraiva P. M., Bakshi B. R. (2008). Multiscale statistical process control using wavelet packets. AIChE Journal, 54(9): 2366-2378.
Ricker N. L. (1996). Decentralized control of the Tennessee Eastman challenge process. Journal of Process Control, 6: 205-221.
Runger G. C., Alt F. B., Montgomery D. C. (1996). Contributors to a multivariate statistical process control chart signal. Communications in Statistics-Theory and Methods, 25(10): 2203-2213.
Scholkopf B., Smola A. J., M(?)ller K. (1998). Nonlinear component analysis as a kernel eignvalue problem. Neural Computation, 10: 1000-1016.
Sebzalli Y. M., Wang X. Z. (2001). Knowledge discovery from process operational data using PCA and fuzzy clustering. Engineering Applications of Artificial Intelligence, 14:607-616.
Shao R., Jia F., Martin E. B. Morris A. J. (1999). Wavelets and non-linear principal component analysis for process monitoring. Control Engineering Practice, 7:865-879.
Shewhart W. A. (1931). Economic control of quality of manufactured product.New York: Van Nostrand.
Simoglou A., Martin E. B., Morris A. J. (2002). Statistical performance monitoring of dynamic multivariate processes using state space modeling. Computers & Chemical Engineering, 26(6): 909-920.
Singhai A., Seborg D. E. (2002a) Clustering of multivariate time-series data.Proceedings of the American Control Conference, 3931-3936.
Singhai A., Seborg D. E. (2002b). Pattern matching in multivariate time series databases using a window-moving approach. Industrial & Engineering Chemistry Research, 41: 3822-3838.
Singhai A., Seborg D. E. (2006). Evaluation of a pattern matching method for the Tennessee Eastman challenge process. Journal of Process Control, 16:601-613.
Stork C. L., Kowalski B. R. (1999). Weighting schemes for updating regression models-a theoretical approach. Chemometrics and Intelligent Laborator Syetems, 48(2), 151-166.
Stork C. L., Veltkamp D. J., Kowalski B. R. (1997). Identification of multiple sensor disturbances during process monitoring. Analytic Chemistry, 69:5031-5036.
Suykens J. A. K., Van Gestel T., De Brabanter J., De Moor B., Vandewalle J.(2002). Least Squares Support Vector Machines. Singapore: World Scientific.
Takahashi T., Kurita T. (2002). Robust de-noising by kernel PCA. Lecture Notes in Computer Science, 2415: 739-744
Tax D. M. J., Duin R. P. W. (1999). Support vector domain description. Pattern Recongnition Letters, 20: 1191 -1199.
Tax D. M. J., Duin R. P. W. (2004). Support vector domain description. Machine Learning, 54: 45-66.
Thissen U., Swierenga H., Weijer A. P. D., Wehrens R. Meissen W. J. Buydens M.C. (2005). Multivariate statistical process control using mixture modelling.Journal of Chemometrics, 19: 23-31
Treasure R. J., Kruger U., Cooper J. E. (2004). Dynamic multivariate statistical process control using subspace identification. Journal of process control, 14:279-292.
Tsung F. (2000). Statistical monitoring and diagnosis of automatic controlled process using dynamic PCA. International Journal of Production Research,38(3): 625-637.
(?)ndey C, Cinar A. (2002), Statistical monitoring of multistage, multiphase batch processes. IEEE Control System Magazine, 22: 40-52.
Valle-Cervantes S., Qin S. J., Piovoso M. (2001). Extracting fault subspaces for fault identification of a polyester film process. In Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineerings: New York, 4466-4471.
Vapnik V. N. (1995). The Nature of Statistical Learning Theory. New York:Springer.
Venkatasubramanian V., Rengaswamy R., Kavuri S. N., Yin K. (2003a). A review of process fault detection and diagnosis Part I: Quantitative model-based methods. Computers & Chemical Engineering, 27: 293-311.
Venkatasubramanian V., Rengaswamy R., Kavuri S. N., Yin K. (2003b). A review of process fault detection and diagnosis Part II: Qualitative models and search strategies. Computers & Chemical Engineering, 27: 313-326;
Venkatasubramanian V., Rengaswamy R., Kavuri S. N., Yin K. (2003c). A review of process fault detection and diagnosis Part III: Process history based methods. Computers & Chemical Engineering, 27: 327-346.
Wang H. Q., Song Z. H., Li P. (2002). Fault detection behavior and performance analysis of PCA-based process monitoring methods. Industrial & Engineering Chemistry Research, 41: 2455-2464.
Wang X., Kruger U., Irwin G. W. (2005). Process monitoring approach using fast moving window PCA. Industrial & Engineering Chemistry Research, 44(15):5691-5702.
Westerhuis J., Gurden S., Smilde A. (2000). Generalized contribution plots in multivariate statistical process monitoring. Chemometrics and Intelligent Laborator Syetems, 51: 95-114.
Wise B. M., Gallagher N. B., Butler S. W., White D. D., Barna G. G. (1999). A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process. Journal of Chemometrics, 13:379-396.
Wold S. (1992). Nonlinear partial least squares modeling II: Spline inner relation.Chemometrics and Intelligent Laboratory Systems, 14(1): 71-84.
Xie L., Kruger U., Lieftucht D., Littler T., Chen Q., Wang S. Q. (2006a). Statistical monitoring of dynamic multivariate processes-part1. modeling autocorrelation and cross-correlation. Industrial & Engineering Chemistry Research, 45: 1659-1676.
Xie L., Wu J. (2006b). Global optimal ICA and its application in MEG data analysis. Neurocomputing, 69: 2438-2442.
Yao Y., Gao F. R. (2007). Batch process monitoring in score space of two-dimensional dynamic principal component analysis (PCA). Industrial & Engineering Chemistry Research, 46(24): 8033-8043.
Yao Y., Gao F. R. (2008a). Subspace Identification for Two-Dimensional Dynamic Batch Process Statistical Monitoring. Chemical Engineering Science, 63:3411-3418.
Yao Y., Gao F. R. (2008b). Phase and transition based batch process modeling and online monitoring. Journal of Process Control, doi: 10.1016/j.jprocont.2008.11.001.
Yoo C. K., Lee J. M., Vanrolleghem P. A., Lee I. B. (2004). On-line monitoring of batch processes using multiway independent component analysis.Chemometrics and Intelligent Laborator Syetems 71: 151-163.
Yoo C. K., Lee I. B. (2006). Nonlinear multivariate filtering and bioprocess monitoring for supervising nonlinear biological processes. Process Biochemistry, 41(8): 1854-1863.
Yoo C. K., Villez K., Lee I. B., Rosen C., Vanrolleghem P. A. (2007). Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor,Biotechnology & Bioengineering. 96: 687-701.
Yoon S., MacGregor J. F. (2004). Principal component analysis of multiscale data for process monitoring and fault diagnosis. AIChE Journal, 50: 2891-2903.
Yu J., Qin S. J. (2008). Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AIChE Journal, 54:1811-1829.
Yue H. H., Qin S. J. (2001). Reconstruction-based fault identification using a combined index. Industrial & Engineering Chemistry Research, 40:4403-4414.
Zhao C. H., Wang F. L., Lu N. Y., Jia M. X. (2007). Stage-based soft-transition multiple PCA modeling and on-line monitoring strategy for batch processes.Journal of Process Control, 17(9): 728-741.
Zhao C. H., Wang F. L., Mao Z. H., Lu N. Y, Jia M. X. (2008a). Improved batch process monitoring and quality prediction based on multiphase statistical analysis.Industrial & Engineering Chemistry Research,47:835-849.
Zhao C.H.,Wang F.L.,Mao Z.H.,Lu N.Y.,Jia M.X.(2008b).Quality prediction based on phase-specific average trajectory for batch processes.AIChE Journal,54:693-705.
Zhao C.H.,Wang F.L.,Gao F.R.,Zhang Y.W.(2008c).Enhanced process comprehension and statistical analysis for slow-varying batch processes.Industrial & Engineering Chemistry Research,47(24):9996-10008.
Zhao C.H.,Wang F.L.,Gao F.R.(2009).Improved calibration investigation using phase-wise local and cumulative quality interpretation and prediction.Chemometrics and Intelligent Laborator Syetems,95(2):107-121.
Zhao S.J.,Zhang J.,Xu Y.M.(2004).Monitoring of processes with multiple operation modes through multiple principle component analysis models.Industrial & Engineering Chemistry Research,43:7025-7035.
Zhao S.J.,Xu Y.M.(2005).Multivariate statistical process monitoring using robust nonlinear principal component analysis.Tsinghua Science and Technology,10:582-586.
Zhao S.J.,Zhang J.,Xu Y.M.(2006).Performance monitoring of processes with multiple operating modes through multiple PLS models.Journal of Process Control,16(7):763-772.
Zhang Y.W.(2008).Fault detection and diagnosis of nonlinear processes using improved kernel independent component analysis(KICA) and support vector machine(SVM).Industrial & Engineering Chemistry Research,47(18):6961-6971.
陈国金,梁军,钱积新.(2003).独立元分析方法(ICA)及其在化工过程监控和故障诊断中的应用.化工学报,54(10):1474-1477.
陈国金,梁军,刘育明,钱积新.(2004).基于多元统计投影方法的过程监控技术研究.浙江大学学报(工学版),38(12):1561-1565.
陈耀,王文海,孙优贤.(2000).基于动态主元分析的统计过程监视.化工学报,51(5):666-670.
葛建华,孙优贤.(1994).容错控制系统的分析与综合.杭州:浙江大学出版社.
郭明.(2004).基于数据驱动的流程工业性能监控与故障诊断研究.杭州:浙江大学博士论文.
何守.(2004).基于ICA-PCA方法的流程工业过程监控与故障诊断研究.杭州:浙江大学博士论文.
蒋丽英.(2005).基于FDA/DPLS方法的流程工业故障诊断研究.杭州:浙江大学博士论文.
刘世成.(2008).面向间歇发酵过程的多元统计监测方法研究.杭州:浙江大学博士论文.
王海清.(2000).工业过程监控:基于小波和统计学的方法.杭州:浙江大学博士论文.
王海清,宋执环,王慧,詹宜巨.(2002).小波阈值密度估计器的设计与应用.仪器仪表学报,23:12-15.
谢磊.(2005).间歇过程统计性能监控研究.杭州:浙江大学博士论文.
谢磊,刘雪芹,张建明,王树青.(2009).基于NGPP-SVDD的非高斯过程监控及其应用研究.自动化学报,35(1):107-112.
张杰,阳宪惠.(2000).多变量统计过程控制.北京:化学工业出版社.
赵旭.(2006).基于统计学方法的过程监控与质量控制研究.上海:上海交通大学博士论文.
赵忠盖.(2007).因子分析及其在过程监控中的应用.化工学报,58(4):970-974.
周韶园.(2005).基于HMM的统计过程监控研究.杭州:浙江大学博士论文.
周东华,孙优贤.(1994).控制系统的故障检测与诊断技术.北京:清华大学出版社.
周东华,叶银忠.(2000).现代故障诊断与容错控制.北京:清华大学出版社.
Alcala C, Qin S. J. (2008). Reconstruction-based contribution for process monitoring. Proceeding of the 17~(th) world congress, The International Federation of Automatic Control, Seoul, Korea, July 6-11.
Amari S. L., Cichocki A., Yang H. (1996). A New Learnig Algorithm for Blind Source Separation. Advances in Neural Information Processing Systems, 8:757-763.
Baffi G., Martin E. B., Morris A.J. (1999). Nonlinear projection to latent structure revisited: the quadratic PLS algorithm. Computers & Chemical Engineering,23(3): 395-411.
Baffi G., Martin E. B., Morris A.J. (2002). Non-linear model based predictive control through dynamic non-linear partial least squares. Chemical Engineering Research&Design, 80(1): 75-86.
Bajpai R. K., Reuss M. (1980). A mechanistic model for penicillin production. Journal of Chemical Technology and Biotechnology, 30: 332-344.
Bakshi B. R. (1998). Multiscale PCA with applications to multivariate statistical process monitoring. AIChE Journal, 44: 1596-1610.
Basseville M., Nikiforov I. (1993). Detection of abrupt changes-Theory and Application. Prentice Hall, N.J.
Basseville M. (1998). On-board component fault detection and isolation using the statistical local approach. Automatica, 34: 1391-1415.
Bell A. J., Sejnowski T. J. (1995). An Information-Maximisation Approach to Blind Separation and Blind Deconvolution. Neural Computation, 7(6): 10004-1034.
Bicego M., Figueiredo M. A. T. (2009). Soft clustering using weighted one-class support vector machines. Pattern recognition, 42: 27-32.
Birol G., Undey C., Cinar A. (2002). A modular simulation package for fed-batch fermentation: Penicillin production. Computers & Chemical Engineering, 26:1553-1561.
Bishop C. M. (2006). Pattern recognition and machine learning. Springle.Camacho J., Pico J. (2006). Online monitoring of batch processes using multiphase principal component analysis. Journal of Process Control, 10: 1021-1035.
Camacho J., Pico J., Ferrer A. (2007). Self-tuning run to run optimization of fed-batch processes using unfold-PLS. AIChE Journal, 53(7): 1789-1804.
Camacho J., Pico J., Ferrer A. (2008a). Bilinear modelling of batch processes. Part I: theoretical discussion. Journal of Chemometrics, 22: 299-308.
Camacho J., Pico J., Ferrer A. (2008b). Bilinear modelling of batch processes. Part II: a comparison of PLS soft-sensors. Journal of Chemometrics, 22: 533-547.
Camacho J., Pico J., Ferrer A. (2008c). Multi-phase analysis framework for handling batch process data. Journal of Chemometrics, 22: 632-643.
Chen J. H., Liu J. L. (1999). Mixture principal component analysis models for process monitoring. Industrial & Engineering Chemistry Research, 38:1478-1488.
Chen J. H., Liu J. L. (2000). Using mixture principal component analysis networks to extract fuzzy rules from data. Industrial & Engineering Chemistry Research, 39: 2355-2367.
Chen J. H., Liu K. C. (2002). On-line batch process monitoring using dynamic PCA and dynamic PLS models. Chemical Engineering Science, 57(1): 63-75.
Chen J. H., Chen H. H. (2006). On-line batch process monitoring using MHMT-based MPCA, Chemical Engineering Science 61: 3223-3239.
Chen Q., Wynne R. J., Goulding P., Sandoz, D. (2000). The application of principal component analysis and kernel density estimation to enhance process monitoring. Control Engineering Practice, 8(5): 531-543.
Chen Q., Kruger U., Leung A. T Y. (2004). Regularised kernel density estimation for clustered process data. Control Engineering Practice, 12: 267-274.
Chen W. C., Wang M. S. (2009). A fuzzy c-means clustering-based fragile watermarking scheme for image authentication. Expert Systems with Applications, 36: 1300-1307.
Cheng C., Chiu M.S. (2005). Nonlinear process monitoring using JITL-PCA.Chemometrics and Intelligent Laborator Syetems, 76: 1-13.
Cherry G. A., Qin S. J. (2006). Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis IEEE Transactions on Semiconductor Manufactory, 19: 159-172.
Chiang L. H., Braatz R. D. (2001). Fault detection and diagnosis in industrial systems. Great Britain: Springer-Verlag London limited.
Cho J.H., Lee J.M., Choi S.W., Lee D.W., Lee I.B. (2005). Fault identification for process monitoring using kernel principal component analysis. Chemical Engineering Science, 60: 279-288.
Choi S. W., Park J. H., Lee I. B. (2004). Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis.Computers & Chemical Engineering, 28: 1377-1387.
Choi S.W., Lee C.K., Lee J.M., Park J.H., Lee I.B. (2005). Fault detection and identification of nonlinear processes based on kernel PCA. Chemometrics and Intelligent Laborator Syetems, 75: 55-67.
Choi S. W., Morris A. J., Lee I. B. (2008a). Dynamic model-based batch process monitoring Chemical Engineering Science 63: 622-636.
Choi S. W., Morris A. J., Lee I. B. (2008b). Nonlinear multiscale modeling for fault detection and identification. Chemical Engineering Science, 63(8):2252-2266.
Conlin A. K., Martin E. B., Morris A. J. (2000). Confidence limits for contribution plots. Journal of Chemometrics, 14(5): 725-736.
Dayal B. S., MacGregor J. F. (1997). Improved PLS algorithms. Journal of Chemometrics, 11: 73-85.
Dong D., McAvoy T. J. (1996). Nonlinear principal component analysis based on principal curves and neural networks. Computers & Chemical Engineering,20: 65-78.
Downs J. J., Vogel E. F. (1993). A plant-wide industrial process control problem.Computers & Chemical Engineering, 17: 245-253.
Dunia R., Qin S. J., Edgar T. F., McAvoy T. J. (1996a). Use of principal component analysis for sensor fault identification. Computers & Chemical Engineering, 20: 713-718.
Dunia R., Qin S. J., Edgar T. F., McAvoy T. J. (1998b). Identification of faulty sensors using principal component analysis. AIChE Journal, 42(10):2797-2812.
Dunia R., Qin S. J. (1998a). A unified geometric approach to process and sensor fault identification: the unidimensional fault case. Computers & Chemical Engineering, 22: 927-943.
Dunia R., Qin S. J. (1998b). Subspace approach to multidimensional fault identification and reconstruction. AIChE Joural, 44(8): 1813-1831.
Ergon R. (2009). Informative score-loading-contribution plots for multi-response process monitoring. Chemometrics and Intelligent Laborator Syetems, 95(1):31-34.
Frank, P. M. (1990). Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy-A survey and some new results. Automatica,26(3): 459-474.
Frank P. M. (1996). Analytical and qualitative model-based fault diagnosis-A survey and some new results. European Journal of Control, 2: 6-23.
Ge Z. Q., Song Z. H. (2007). Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors.Industrial & Engineering Chemistry Research, 46: 2054-2063.
Ge Z. Q., Yang C. J., Song Z. H., Wang H. Q. (2008). Robust Online Monitoring for Multimode Processes Based on Nonlinear External Analysis. Industrial &Engineering Chemistry Research, 47: 4775-4783.
Gertler J. (1998). Fault detection and diagnosis in engineering systems. Marcel Dekker, New York.
Gertler J., Cao J. (2005). Design of optimal structured residuals from partial principal component models for fault diagnosis in linear systems. Journal of Process Control, 15(5): 585-603.
Gunther J. C., Baclaski J., Seborg D. E., Conner J. S. (2009). Pattern matching in batch bioprocesses-Comparisons across multiple products and operating conditions. Computers & Chemical Engineering, 33(1): 88-96.
Hastie T., Stuetzle W. (1989). Principal curves. Journal of America Statistical Association, 84: 502-516.
He Q. P., Wang J. (2007). Fault detection using the k-Nearest neighbor rule for semiconductor manufacturing processes. IEEE Transactions on Semiconductor Manufactory, 20: 345-354, 2007.
He X. B., Yang Y. P. (2008). Variable MWPCA for adaptive process monitoring.Industrial & Engineering Chemistry Research, 47:419-427.
Hiden H. G., Willis M. J., Tham M. T., Montague G. A. (1999). Non-linear principal component analysis using genetic programming. Chemometrics and Intelligent Laborator Syetems, 23: 413-425.
Hu K. L., Yuan J. Q. (2008a). Statistical monitoring of fed-batch process using dynamic multiway neighborhood preserving embedding. Chemometrics and Intelligent Laborator Syetems, 90(2): 195-203.
Hu K. L., Yuan J. Q. (2008b). Multivariate statistical process control based on multiway locality preserving projections. Journal of Process control 18:797-807.
Hwang D. H., Han C. (1999). Real-time monitoring for a process with multiple operating modes. Control Engineering Practice, 7: 891-902.
Hyv(?)rinen A. (1997). A fast fixed-point algorithm for independent component analysis. Neural Computation, 9: 1483-1492.
Hyv(?)rinen A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3):626-634.
Hyv(?)rinen A., Oja E. (2000). Independent component analysis: algorithms and applications. Neural Network, 13: 411-430.
Isermann R., Balle P. (1997). Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engineering Practice,5(5): 709-719.
Jackson J. E. (1991). A user's guide to principal component analysis. New York:Wiley Inter-science.
Jia F., Martin E. B., Morris A. J. (2001). Nonlinear principal components analysis with application to process fault detection. International Journal of Systems Science, 31: 1473-1487.
Jin H. D., Lee Y. H., Lee G., Han C. H. (2006). Robust recursive principal component analysis modeling for adaptive monitoring. Industrial & Engineering Chemistry Research, 45: 696-703.
Johannesmeyer M. C., Singhai A., Seborg D. E. (2002). Pattern matching in historical data. AIChE Journal, 48: 2022-2038.
Juricek B. C., Seborg D. E., Larimore W. E. (2001). Identification of the Tennessee Eastman challenge process with subspace methods. Control Engineering Practice, 9(12): 1337-1351.
Jutten C., Herault J. (1988). Independent component analysis versus PCA. Proceeding of European Signal Processing, 287-314.
Kampjarvi P., Sourander M. Komulainen T., Vatanski N., Nikus M., Jounela S. L.(2008). Fault detection and isolation of an on-line analyzer for an ethylene cracking process. Control Engineering Practice, 16(1): 1-13.
Kano M., Nagao K., Hasebe H., Hashimoto I., Ohno H., Strauss R., Bakshi B. R.(2002). Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem. Computers & Chemical Engineering, 26: 161-174.
Kano M., Tanaka S., Hasebe S., Hashimoto I., Ohno H. (2003). Monitoring independent components for fault detection. AIChE Journal, 49(4): 969-976.
Kano M., Hasebe S., Hashimoto I., Ohno H. (2004a). Evolution of multivariate statistical process control: application of independent component analysis and external analysis. Computers & Chemical Engineering, 28: 1157-1166.
Kano M., Tanaka S., Hasebe S., Hashimoto I., Ohno H. (2004b). Combined multivariate statistical process control. 1FAC Symposium on Advanced Control of Chemical Processes (ADCHEM), 303-308.
Kesavan P., Lee J. H. (1997). Diagnostic tools for multivariable model-based control systems. Industrial & Engineering Chemistry Research, 36(7):2725-2738.
Kim D., Lee I. B. (2003). Process monitoring based on probabilistic PCA.Chemometrics and Intelligent Laborator Syetems, 67: 109-123.
Kim D. S., Yoo C. K., Kim Y. I., Jung J. H., Lee I. B. (2005). Calibration,prediction and process monitoring model based on factor analysis for incomplete process data. Journal of Chemical Engineering of Japan. 38(12):1025-1034.
Kourti T. (2003). Multivariate dynamic data modeling for analysis and statistical process control of batch processes, startups and grade transitions. Journal of Chemometrics, 17(1): 93-109.
Kramer M. A. (1991). Nonlinear principal component analysis using autoassociative neural networks. AIChE Journal, 37(2): 233-243.
Krzanowski W. J. (1979). Between-groups comparison of principal components.Journal of America Statistical Association, 74: 703-707.
Kruger U., Zhou Y. Q., Irwin G. W. (2004). Improved principal component monitoring of large-scale processes. Journal of process control, 14: 879-888.
Kruger U., Antory D., Hahn J., Irwin G.W., McCullough G. (2005). Introduction of a nonlinearity measure for principal component models. Computers &Chemical Engineering, 29: 2355-2362.
Kruger U., Kumar S., Littler T. (2007). Improved principal component monitoring using the local approach. Automatica, 43: 1532-1542.
Ku W., Storer R.H., Georgakis C. (1995). Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems, 30: 179-196.
Kumar K. V., Negi A. (2008). SubXPCA and a generalized feature partitioning approach to principal component analysis. Pattern Recognition, 41:1398-1409.
Lane S., Martin E. B., Kooijmans R., Morris A. J. (2001). Performance monitoring of a multi-product semi-batch process. Journal of Process Control, 11: 1-11.
Larimore W. E. (1990). Canonical variate analysis in identification, filtering and adaptive control. In Proceedings of the 29~(th) Conference on Decision and Control, 596-604.
Larimore W. E. (1996). Statistical optimality and canonical variate analysis system identification. Signal Processing, 52: 131-144.
Lee D. S., Park J. M., Vanrolleghem P. A. (2005). Adaptive multiscale principal component analysis for on-line monitoring of a sequencing batch reactor.Journal of Biotechnology, 116(2): 195-210.
Lee D. S., Lee M. W., Woo S. H., Kim Y. J., Park J. M. (2006a). Multivariate online monitoring of a full-scale biological anaerobic filter process using kernel-based algorithms. Industrial & Engineering Chemistry Research,45(12): 4335-4344.
Lee D. S., Lee M. W., Woo S. H., Kim Y. J., Park J. M. (2006b). Nonlinear dynamic partial least squares modeling of a full-scale biological wastewater treatment plant. Process Biochemistry, 41(9): 2050-2057.
Lee J. M., Yoo C. K., Lee I. B. (2004a). Statistical process monitoring with independent component analysis. Journal of Process Control, 14(5): 467-485.
Lee J. M., Yoo C. K., Lee I. B. (2004b). Statistical monitoring of dynamic processes based on dynamic independent component analysis. Chemical Engineering Science, 59(14): 2995-3006.
Lee J. M., Yoo C.K., Choi S.W., Vanrolleghem P.A., Lee I. B. (2004c). Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science, 59: 223-234.
Lee J. M., Qin S. J., Lee I. B. (2006). Fault detection and diagnosis based on modified independent component analysis. AIChE Journal, 52: 3501-3514.
Lee J. M., Qin S. J., Lee 1. B.(2007). Fault detection of non-linear processes using kernel independent component analysis. Canadian Journal of Chemical Engineering, 85(4): 526-536.
Li R. F., Wang X. 2. (2002). Dimension reduction of process dynamic trends using independent component analysis. Computers & Chemical Engineering, 26:467-473.
Li R. Y., Rong G. (2006). Fault isolation by partial dynamic principal component analysis in dynamic process. Chinese Journal of Chemical Engineering, 14(4):486-493.
Li W., Yue H. H., Valle-Cervantes S., Qin S. J. (2000). Recursive PCA for adaptive process monitoring. Journal of Process Control, 10: 471-486.
Lieftucht D., Kruger U., Xie L., Littler T., Chen Q., Wang S. Q. (2006a). Statistical monitoring of dynamic multivariate processes-part 2. identifying fault magnitude and signature. Industrial & Engineering Chemistry Research, 45:1677-1688.
Lieftuche D., Kruger U., Invin G. W. (2006b). Improved reliability in diagnosing faults using multivariate statistics. Computers & Chemical Engineering, 30:901-912.
Liu J., Wong D. S. H. (2008). Fault detection and classification for a two-stage batch process. Journal of Chemometrics, 22: 385-398.
Liu X., Xie L., Kruger U., Littler T., Wang S. (2008). Statistical-based monitoring of multivariate non-Gaussian systems. AIChE Journal, 54: 2379-2391.
Liu X. Q., Xie L., Kruger U., Littler T., Wang S. Q. (2009). Moving window kernel PCA for adaptive monitoring of nonlinear processes. Chemometrics and Intelligent Laborator Syetems, doi: 10.1016/j.chemolab.2009.01.002.
Lu N. Y., Wang F. L., Gao F. R. (2004a). Combination method of principal component and wavelet analysis for multivariate process monitoring and fault diagnosis. Industrial & Engineering Chemistry Research, 42: 4198-4207.
Lu N. Y., Gao F. R., Wang F. L. (2004b). A sub-PCA modeling and online monitoring strategy for batch processes. AIChE Journal, 50: 255-259.
Lu N. Y., Yao Y., Gao F. R., Wang F. L. (2005). Two-dimensional dynamic PCA for batch process monitoring. AIChE Journal, 51(12): 3300-3304.
Lyman P. R., Georgakist C. (1995). Plant-wide Control of the Tennessee Eastman Problem. Computers & Chemical Engineering, 19: 321-331.
Malthouse E. C., Tamhane A. C., Mah R. S. H. (1997). Nonlinear partial least squares. Computers & Chemical Engineering, 21(8): 875-890.
Martin E. B., Morris A. J. (1996). Non-parametric confidence bounds for process performance monitoring charts. Journal of Process Control, 6: 349-358.
Martin E. B., Morris A. J. (2002). Enhanced bio-manufacturing through advanced multivariate statistical technology. Journal of Biotechnology, 99: 223-235.
Maulud A., Wang D., Romagnoli J. A. (2006). A multi-scale orthogonal nonlinear strategy for multi-variate statistical process monitoring. Journal of Process Control, 16: 671-683.
Mika S., Scholkopf B., Smola A., Muller K. R., Scholz M., Ratsch G. (1998).Kernel PCA and de-noising in feature spaces. MIT Press, Denver, Co.,536-542.
Miller P., Swanson R. E., Heckler C. F. (1993). Contribution plots: the missing link in multivariate quality control. Fall Conf. of ASQC and ASA, Milwaukee,WI.
Misra M., Yue H., Qin S. J., Ling C. (2002). Multivariate process monitoring and fault diagnosis by multi-scale PCA. Computers & Chemical Engineering,26(6): 1281-1293.
Negiz A., Cinar A. (1997a). Statistical monitoring of multivariable dynamic processes with state space model. AIChE Journal, 43: 2002-2020.
Nomikos P., MacGregor J. F. (1994). Monitoring batch processes using multiway principal component analysis. AIChE Journal, 44: 1361-1375.
Nomikos P., MacGregor J. F. (1995a). Multi-way partial least square in monitoring batch processes. Chemometrics and Intelligent Laborator Syetems, 30:97-108.
Nomikos P., MacGregor J. F. (1995b). Multivariate SPC charts for monitoring batch process. Technometrics, 37: 41-59.
Overschee P. V., Moor D. M. (1994). System identification for the identification of combined deterministic-stochastic systems. Automatica, 30: 75-93.
Overschee P. V., Moor D. M. (1996). Subspace identification for linear systemes.Kluwer Academic Publishers.
Patton R., Frank P. M., Clark R. (1989). Fault diagnosis in dynamic systems.Englewood Clifs: Prentice Hall.
Qin S. J., McAvoy T. J. (1992). Nonlinear PLS modeling using neural networks. Computers & Chemical Engineering, 16(4): 379-391.
Qin S. J. (1998). Recursive PLS algorithms for adaptive data monitoring.Computers & Chemical Engineering, 22: 503-514.
Qin S. J., Li W. (1999). Detection, identification and reconstruction of faulty sensors with maximized sensitivity. AIChE Journal, 45(9): 1963-1976.
Qin S. J., Li W. (2001). Detection and identification of faulty sensors in dynamic processes. AIChE Journal, 47(7): 1581-1593.
Qin S. J. (2003). Statistical process monitoring: basics and beyond. Journal of Chemometrics, 17: 480-502.
Rannar S., MacGregor J. F., Wold S. (1998). Adaptive batch monitoring using hierarchical PCA. Chemometrics and Intelligent Laborator Syetems, 41:73-81.
Raich A. C., Cinar A. (1995). Multivariate statistical methods for monitoring continuous processes: assessment of discrimination power of disturbance models and diagnosis of multiple disturbances. Chemometrics and Intelligent Laborator Syetems, 30: 37-48.
Raich A. C., Cinar A. (1997). Diagnosis of process disturbance by statistical distance and angle measures. Computers & Chemical Engineering, 21:661-676.
Reis M. S., Saraiva P. M., Bakshi B. R. (2008). Multiscale statistical process control using wavelet packets. AIChE Journal, 54(9): 2366-2378.
Ricker N. L. (1996). Decentralized control of the Tennessee Eastman challenge process. Journal of Process Control, 6: 205-221.
Runger G. C., Alt F. B., Montgomery D. C. (1996). Contributors to a multivariate statistical process control chart signal. Communications in Statistics-Theory and Methods, 25(10): 2203-2213.
Scholkopf B., Smola A. J., M(?)ller K. (1998). Nonlinear component analysis as a kernel eignvalue problem. Neural Computation, 10: 1000-1016.
Sebzalli Y. M., Wang X. Z. (2001). Knowledge discovery from process operational data using PCA and fuzzy clustering. Engineering Applications of Artificial Intelligence, 14:607-616.
Shao R., Jia F., Martin E. B. Morris A. J. (1999). Wavelets and non-linear principal component analysis for process monitoring. Control Engineering Practice, 7:865-879.
Shewhart W. A. (1931). Economic control of quality of manufactured product.New York: Van Nostrand.
Simoglou A., Martin E. B., Morris A. J. (2002). Statistical performance monitoring of dynamic multivariate processes using state space modeling. Computers & Chemical Engineering, 26(6): 909-920.
Singhai A., Seborg D. E. (2002a) Clustering of multivariate time-series data.Proceedings of the American Control Conference, 3931-3936.
Singhai A., Seborg D. E. (2002b). Pattern matching in multivariate time series databases using a window-moving approach. Industrial & Engineering Chemistry Research, 41: 3822-3838.
Singhai A., Seborg D. E. (2006). Evaluation of a pattern matching method for the Tennessee Eastman challenge process. Journal of Process Control, 16:601-613.
Stork C. L., Kowalski B. R. (1999). Weighting schemes for updating regression models-a theoretical approach. Chemometrics and Intelligent Laborator Syetems, 48(2), 151-166.
Stork C. L., Veltkamp D. J., Kowalski B. R. (1997). Identification of multiple sensor disturbances during process monitoring. Analytic Chemistry, 69:5031-5036.
Suykens J. A. K., Van Gestel T., De Brabanter J., De Moor B., Vandewalle J.(2002). Least Squares Support Vector Machines. Singapore: World Scientific.
Takahashi T., Kurita T. (2002). Robust de-noising by kernel PCA. Lecture Notes in Computer Science, 2415: 739-744
Tax D. M. J., Duin R. P. W. (1999). Support vector domain description. Pattern Recongnition Letters, 20: 1191 -1199.
Tax D. M. J., Duin R. P. W. (2004). Support vector domain description. Machine Learning, 54: 45-66.
Thissen U., Swierenga H., Weijer A. P. D., Wehrens R. Meissen W. J. Buydens M.C. (2005). Multivariate statistical process control using mixture modelling.Journal of Chemometrics, 19: 23-31
Treasure R. J., Kruger U., Cooper J. E. (2004). Dynamic multivariate statistical process control using subspace identification. Journal of process control, 14:279-292.
Tsung F. (2000). Statistical monitoring and diagnosis of automatic controlled process using dynamic PCA. International Journal of Production Research,38(3): 625-637.
(?)ndey C, Cinar A. (2002), Statistical monitoring of multistage, multiphase batch processes. IEEE Control System Magazine, 22: 40-52.
Valle-Cervantes S., Qin S. J., Piovoso M. (2001). Extracting fault subspaces for fault identification of a polyester film process. In Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineerings: New York, 4466-4471.
Vapnik V. N. (1995). The Nature of Statistical Learning Theory. New York:Springer.
Venkatasubramanian V., Rengaswamy R., Kavuri S. N., Yin K. (2003a). A review of process fault detection and diagnosis Part I: Quantitative model-based methods. Computers & Chemical Engineering, 27: 293-311.
Venkatasubramanian V., Rengaswamy R., Kavuri S. N., Yin K. (2003b). A review of process fault detection and diagnosis Part II: Qualitative models and search strategies. Computers & Chemical Engineering, 27: 313-326;
Venkatasubramanian V., Rengaswamy R., Kavuri S. N., Yin K. (2003c). A review of process fault detection and diagnosis Part III: Process history based methods. Computers & Chemical Engineering, 27: 327-346.
Wang H. Q., Song Z. H., Li P. (2002). Fault detection behavior and performance analysis of PCA-based process monitoring methods. Industrial & Engineering Chemistry Research, 41: 2455-2464.
Wang X., Kruger U., Irwin G. W. (2005). Process monitoring approach using fast moving window PCA. Industrial & Engineering Chemistry Research, 44(15):5691-5702.
Westerhuis J., Gurden S., Smilde A. (2000). Generalized contribution plots in multivariate statistical process monitoring. Chemometrics and Intelligent Laborator Syetems, 51: 95-114.
Wise B. M., Gallagher N. B., Butler S. W., White D. D., Barna G. G. (1999). A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process. Journal of Chemometrics, 13:379-396.
Wold S. (1992). Nonlinear partial least squares modeling II: Spline inner relation.Chemometrics and Intelligent Laboratory Systems, 14(1): 71-84.
Xie L., Kruger U., Lieftucht D., Littler T., Chen Q., Wang S. Q. (2006a). Statistical monitoring of dynamic multivariate processes-part1. modeling autocorrelation and cross-correlation. Industrial & Engineering Chemistry Research, 45: 1659-1676.
Xie L., Wu J. (2006b). Global optimal ICA and its application in MEG data analysis. Neurocomputing, 69: 2438-2442.
Yao Y., Gao F. R. (2007). Batch process monitoring in score space of two-dimensional dynamic principal component analysis (PCA). Industrial & Engineering Chemistry Research, 46(24): 8033-8043.
Yao Y., Gao F. R. (2008a). Subspace Identification for Two-Dimensional Dynamic Batch Process Statistical Monitoring. Chemical Engineering Science, 63:3411-3418.
Yao Y., Gao F. R. (2008b). Phase and transition based batch process modeling and online monitoring. Journal of Process Control, doi: 10.1016/j.jprocont.2008.11.001.
Yoo C. K., Lee J. M., Vanrolleghem P. A., Lee I. B. (2004). On-line monitoring of batch processes using multiway independent component analysis.Chemometrics and Intelligent Laborator Syetems 71: 151-163.
Yoo C. K., Lee I. B. (2006). Nonlinear multivariate filtering and bioprocess monitoring for supervising nonlinear biological processes. Process Biochemistry, 41(8): 1854-1863.
Yoo C. K., Villez K., Lee I. B., Rosen C., Vanrolleghem P. A. (2007). Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor,Biotechnology & Bioengineering. 96: 687-701.
Yoon S., MacGregor J. F. (2004). Principal component analysis of multiscale data for process monitoring and fault diagnosis. AIChE Journal, 50: 2891-2903.
Yu J., Qin S. J. (2008). Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AIChE Journal, 54:1811-1829.
Yue H. H., Qin S. J. (2001). Reconstruction-based fault identification using a combined index. Industrial & Engineering Chemistry Research, 40:4403-4414.
Zhao C. H., Wang F. L., Lu N. Y., Jia M. X. (2007). Stage-based soft-transition multiple PCA modeling and on-line monitoring strategy for batch processes.Journal of Process Control, 17(9): 728-741.
Zhao C. H., Wang F. L., Mao Z. H., Lu N. Y, Jia M. X. (2008a). Improved batch process monitoring and quality prediction based on multiphase statistical analysis.Industrial & Engineering Chemistry Research,47:835-849.
Zhao C.H.,Wang F.L.,Mao Z.H.,Lu N.Y.,Jia M.X.(2008b).Quality prediction based on phase-specific average trajectory for batch processes.AIChE Journal,54:693-705.
Zhao C.H.,Wang F.L.,Gao F.R.,Zhang Y.W.(2008c).Enhanced process comprehension and statistical analysis for slow-varying batch processes.Industrial & Engineering Chemistry Research,47(24):9996-10008.
Zhao C.H.,Wang F.L.,Gao F.R.(2009).Improved calibration investigation using phase-wise local and cumulative quality interpretation and prediction.Chemometrics and Intelligent Laborator Syetems,95(2):107-121.
Zhao S.J.,Zhang J.,Xu Y.M.(2004).Monitoring of processes with multiple operation modes through multiple principle component analysis models.Industrial & Engineering Chemistry Research,43:7025-7035.
Zhao S.J.,Xu Y.M.(2005).Multivariate statistical process monitoring using robust nonlinear principal component analysis.Tsinghua Science and Technology,10:582-586.
Zhao S.J.,Zhang J.,Xu Y.M.(2006).Performance monitoring of processes with multiple operating modes through multiple PLS models.Journal of Process Control,16(7):763-772.
Zhang Y.W.(2008).Fault detection and diagnosis of nonlinear processes using improved kernel independent component analysis(KICA) and support vector machine(SVM).Industrial & Engineering Chemistry Research,47(18):6961-6971.
陈国金,梁军,钱积新.(2003).独立元分析方法(ICA)及其在化工过程监控和故障诊断中的应用.化工学报,54(10):1474-1477.
陈国金,梁军,刘育明,钱积新.(2004).基于多元统计投影方法的过程监控技术研究.浙江大学学报(工学版),38(12):1561-1565.
陈耀,王文海,孙优贤.(2000).基于动态主元分析的统计过程监视.化工学报,51(5):666-670.
葛建华,孙优贤.(1994).容错控制系统的分析与综合.杭州:浙江大学出版社.
郭明.(2004).基于数据驱动的流程工业性能监控与故障诊断研究.杭州:浙江大学博士论文.
何守.(2004).基于ICA-PCA方法的流程工业过程监控与故障诊断研究.杭州:浙江大学博士论文.
蒋丽英.(2005).基于FDA/DPLS方法的流程工业故障诊断研究.杭州:浙江大学博士论文.
刘世成.(2008).面向间歇发酵过程的多元统计监测方法研究.杭州:浙江大学博士论文.
王海清.(2000).工业过程监控:基于小波和统计学的方法.杭州:浙江大学博士论文.
王海清,宋执环,王慧,詹宜巨.(2002).小波阈值密度估计器的设计与应用.仪器仪表学报,23:12-15.
谢磊.(2005).间歇过程统计性能监控研究.杭州:浙江大学博士论文.
谢磊,刘雪芹,张建明,王树青.(2009).基于NGPP-SVDD的非高斯过程监控及其应用研究.自动化学报,35(1):107-112.
张杰,阳宪惠.(2000).多变量统计过程控制.北京:化学工业出版社.
赵旭.(2006).基于统计学方法的过程监控与质量控制研究.上海:上海交通大学博士论文.
赵忠盖.(2007).因子分析及其在过程监控中的应用.化工学报,58(4):970-974.
周韶园.(2005).基于HMM的统计过程监控研究.杭州:浙江大学博士论文.
周东华,孙优贤.(1994).控制系统的故障检测与诊断技术.北京:清华大学出版社.
周东华,叶银忠.(2000).现代故障诊断与容错控制.北京:清华大学出版社.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |