基于统计理论的工业过程综合性能监控、诊断及质量预测方法研究
|
摘要
随着工业过程规模的不断扩大和复杂性的日益提高,有效的性能监控、故障诊断和质量预测是保证生产安全、提高产品质量和经济效益的关键。对于复杂的工业过程来说,准确详细的数学模型往往很难得到,即使能够得到,这些理论上的等式也只能描述系统中一部分能量及物料平衡关系,这就限制了基于模型的性能监控方法的应用。另外,随着计算机技术的迅速发展,工业过程中能够测量和处理的变量越来越多,如何从海量数据中挖掘出隐藏的有用信息,从而提高过程运行的安全性和可靠性,已经成为越来越迫切需要解决的问题。统计性能监控就是在这种背景下发展起来的,并且受到了广泛关注。
统计性能监控是一种基于多元统计理论的方法。它通过对测量数据的分析和解释,判断过程所处的运行状态,在线监测和识别过程中出现的异常工况,从而指导生产、减小过程故障所造成的损失和提高生产效率。
另外,在现代许多企业中,由于技术或经济条件的限制,生产过程中许多与产品质量密切相关的重要参数很难通过传感器在线测量。随着市场竞争的不断加剧和生产工艺的复杂化,这一问题已成为制约生产与产品质量进一步提高的关键性因素。质量预测和估计(软测量技术)就是为了解决这一问题而产生的,质量预测通过一些可以测量的过程变量和其他一些参数,能够在线估计这些无法直接测量的参数和变量,从而为过程控制及管理决策提供支持,为生产过程的综合自动化奠定基础。
本文在传统统计性能监控和质量估计方法的基础上,做了不同程度的改进并提出了一些新的监控和质量估计算法。
本文的主要工作和贡献有以下几个方面:
1.利用核主元分析非线性性能监控的优势,并将相似度分析引入故障诊断领域,提出了基于核主元分析和模式匹配技术相结合的性能监控和故障诊断方法。针对PCA相似度分析存在的问题,对该方法进行了改进。首先利用KPCA方法计算数据的非线性主元,然后计算不同数据集之间的非线性主元相似度;并将主元相似度、非线性主元相似度和基于距离的相似度赋予不同的权值构成综合相似度指标来进行模式匹配。TE过程仿真试验验证了该方法在非线性性能监控和故障诊断中的有效性。
2.针对复杂的工业过程,综合核主元分析处理非线性数据的优点和ICA方法提取高维特征空间信息的能力,提出了基于KICA方法的非线性性能监控方法。该方法首先将数据通过非线性核函数映射到高维特征空间,然后在特征空间中进行独立主元分析和计算。通过在特征空间中构建监控统计量和控制置信限,来实现复杂化工和生物过程的监控。非线性数值仿真实例和流化催化裂化(FCCU)过程仿真研究验证了该方法的有效性。
3.针对间歇过程监控的特点,将核方法引入到Fisher判别分析中,提出了基于核Fisher判别分析的间歇过程性能监控与故障诊断法。所提出的方法仅利用已获得的数据测量值对过程进行监控,避免了传统MPCA方法对未来测量值的估计,从而提高了间歇过程监控的性能。在线性能监控通过比较核Fisher特征向量之间的欧氏距离来实现,而最优核Fisher判别向量用来鉴别故障类型。与KPCA方法相比,KFDA方法不仅简化了运算,避免了对核主元个数的确定,而且可以通过求解最优核Fisher判别向量来实现故障诊断。青霉素发酵过程仿真应用表明,核Fisher方法比传统的MPCA方法能更及时地监测出过程异常情况,更准确地判断异常发生的原因。
4.利用核偏最小二乘(Kernel Partial Least Squares, KPLS)非线性回归的优势,提出了基于KPLS的非线性质量估计和预测方法。首先通过非线性映射将过程数据从低维输入空间映射到高维特征空间,实现变量之间非线性相关关系的线性转化。然后在高维特征空间中利用PLS回归方法进行质量估计和预测。数值仿真实例和实际工业过程数据应用表明KPLS方法能更有效地捕捉变量之间的非线性关系,回归和质量预测效果明显优于线性PLS方法。
5.针对质量估计和预测过程中由于仪表错误或过程泄漏等原因造成的过失误差及故障问题,将模式识别中的Fisher判别分析法引入过程显著误差侦破和故障监测,提出了基于Fisher判别分析和核回归的质量监控和估计方法。首先利用Fisher方法对输入数据进行在线监测,若系统运行正常,则用核主元回归方法对质量数据进行预测和估计;否则将存在故障和过失误差的数据剔除并利用贡献图法确定故障原因。实际工业过程数据仿真研究验证了该方法进行故障监测和质量估计的有效性。
6.通过分析上海焦化甲醇精馏过程的特点和工艺流程,开发出了一套统计性能监控和质量估计软件,并将其应用到实际生产过程中,取得了良好效果,从而为上海焦化综合信息化平台和先进控制的成功实施奠定了坚实的基础。
In real industrial processes, effective performance monitoring and quality prediction are the key to ensure safety, enhance product quality and economy benefit. For the complex industrial process, it is difficult to achieve the accurate mathematical model. Even if it could be achieved, the equations which are predigested can only describe part relationships of energy and mass. These limit the application of methods based on rigorous mathematical model. On the other hand, with the rapid development of computer technology, a large amount of process data have been sampled and collected. How to transform these collected data into valuable information, and mine it deep-level to improve the monitoring performance becomes a challenge issue. It is one of the most active research areas in the field of process control.
Statistical performance monitoring is a method based on statistical theory for online fault detection and diagnosis via analysis to the collected data. The information extracted from process data could reflect the operating status at any time, reduce the losses caused by faults and enhance product quality.
Because of the limitation of technology and cost, many key variables are difficult to measure via sensors in industrial process. With the market competition become more and more furious, it has become an important factor holding back the product quality further improvement. Quality prediction and estimation (soft sensor) technology has been proposed to solve this problem. It can estimate the variables that are difficult to measure directly. The results can be used for quality control and decision support. It lays a foundation for the integrated automation.
In view of the characteristics of continuous and batch industry processes, some improvements of the tradition monitoring and prediction methods have been made, and new statistical monitoring algorithms are also proposed in this thesis.
The main results and contributions of this dissertation are stated as follows:
1. Using the advantage of kernel component analysis (KPCA) for nonlinear monitoring and introducing the similarity analysis for fault diagnosis, a new performance monitoring and fault diagnosis method based on KPCA and pattern matching is proposed. Aiming at the existing problem in traditional PCA similarity analysis, the method is improved. Nonlinear principal component similarities are firstly calculated. Then the integrated similarity index is proposed through endowing with different weights to PCA similarity, KPCA similarity and distance similarity. Fault diagnosis is performed through pattern matching of different faults. Effectiveness of the proposed method is verified through TE process.
2. In view of the complex industrial processes, a nonlinear process monitoring method based on KICA is proposed through integrating the merit of KPCA to deal with nonlinear data and ICA to extract the high-dimensional information. The data are firstly mapped into high-dimensional feature subspace. Then the ICA algorithm is performed. Performance monitoring is implemented through constructing the statistical index and control limit in the feature space. Application results to the FCCU process indicate that the proposed method can effectively capture nonlinear relationship among variables. Its performance significantly outperforms monitoring method based on ICA or KPCA.
3. In view of the characteristics of batch process and using the advantage of kernel theory, a novel batch performance monitoring and fault identification strategy based on kernel fisher discriminant analysis (KFDA) is proposed. The approach only uses present data and overcomes pre-estimating the unknown part of process variable trajectory in multi-way PCA (MPCA). The key to the proposed approach is to calculate the distance of block data which are projected to the optimal kernel Fisher discriminant vector between new batch and reference batch. Through comparing distance with the predefined threshold, it can be considered whether the batch is normal or abnormal. Similar degree between the present discriminant vector and the optimal discriminant vector of fault in historical data set is used to perform fault diagnosis. Simulation results on a penicillin fermentation process demonstrate that, in comparison to the MPCA method, the proposed method is more accurate and efficient to detect and diagnose the malfunctions.
4. Using the advantage of kernel partial least squares (KPLS) in nonlinear regression, a new quality estimation and prediction method based on KPLS is proposed. The basic idea of the method is to first map data in the original space into high-dimensional feature space via nonlinear kernel and then performs quality estimation and prediction. Application results to a simple example and real data in an industrial oil refinery factory show that the proposed method can effectively capture nonlinear relationship among variables and have better estimation performance than PLS and other linear approaches.
5. In view of the gross error caused by sensor faults or process leakage, a novel performance monitoring and quality estimation approach based on fisher discriminant analysis (FDA) and kernel regression is proposed. FDA is first used for quality monitoring.
If the process is under normal condition, then kernel regression is further used for quality prediction and estimation. Otherwise, if faults have occurred, contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can effectively detect the happening fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.
6. The model of performance monitoring and quality prediction is built and a software package is developed through analysis to the characteristic and techniques of methanol process. They are successfully applied to the monitoring of methanol process in Shanghai Coking and Chemical Corporation (SCCC). What we have done lay the foundations for the performance of integrated automation and advanced control in SCCC.
统计性能监控是一种基于多元统计理论的方法。它通过对测量数据的分析和解释,判断过程所处的运行状态,在线监测和识别过程中出现的异常工况,从而指导生产、减小过程故障所造成的损失和提高生产效率。
另外,在现代许多企业中,由于技术或经济条件的限制,生产过程中许多与产品质量密切相关的重要参数很难通过传感器在线测量。随着市场竞争的不断加剧和生产工艺的复杂化,这一问题已成为制约生产与产品质量进一步提高的关键性因素。质量预测和估计(软测量技术)就是为了解决这一问题而产生的,质量预测通过一些可以测量的过程变量和其他一些参数,能够在线估计这些无法直接测量的参数和变量,从而为过程控制及管理决策提供支持,为生产过程的综合自动化奠定基础。
本文在传统统计性能监控和质量估计方法的基础上,做了不同程度的改进并提出了一些新的监控和质量估计算法。
本文的主要工作和贡献有以下几个方面:
1.利用核主元分析非线性性能监控的优势,并将相似度分析引入故障诊断领域,提出了基于核主元分析和模式匹配技术相结合的性能监控和故障诊断方法。针对PCA相似度分析存在的问题,对该方法进行了改进。首先利用KPCA方法计算数据的非线性主元,然后计算不同数据集之间的非线性主元相似度;并将主元相似度、非线性主元相似度和基于距离的相似度赋予不同的权值构成综合相似度指标来进行模式匹配。TE过程仿真试验验证了该方法在非线性性能监控和故障诊断中的有效性。
2.针对复杂的工业过程,综合核主元分析处理非线性数据的优点和ICA方法提取高维特征空间信息的能力,提出了基于KICA方法的非线性性能监控方法。该方法首先将数据通过非线性核函数映射到高维特征空间,然后在特征空间中进行独立主元分析和计算。通过在特征空间中构建监控统计量和控制置信限,来实现复杂化工和生物过程的监控。非线性数值仿真实例和流化催化裂化(FCCU)过程仿真研究验证了该方法的有效性。
3.针对间歇过程监控的特点,将核方法引入到Fisher判别分析中,提出了基于核Fisher判别分析的间歇过程性能监控与故障诊断法。所提出的方法仅利用已获得的数据测量值对过程进行监控,避免了传统MPCA方法对未来测量值的估计,从而提高了间歇过程监控的性能。在线性能监控通过比较核Fisher特征向量之间的欧氏距离来实现,而最优核Fisher判别向量用来鉴别故障类型。与KPCA方法相比,KFDA方法不仅简化了运算,避免了对核主元个数的确定,而且可以通过求解最优核Fisher判别向量来实现故障诊断。青霉素发酵过程仿真应用表明,核Fisher方法比传统的MPCA方法能更及时地监测出过程异常情况,更准确地判断异常发生的原因。
4.利用核偏最小二乘(Kernel Partial Least Squares, KPLS)非线性回归的优势,提出了基于KPLS的非线性质量估计和预测方法。首先通过非线性映射将过程数据从低维输入空间映射到高维特征空间,实现变量之间非线性相关关系的线性转化。然后在高维特征空间中利用PLS回归方法进行质量估计和预测。数值仿真实例和实际工业过程数据应用表明KPLS方法能更有效地捕捉变量之间的非线性关系,回归和质量预测效果明显优于线性PLS方法。
5.针对质量估计和预测过程中由于仪表错误或过程泄漏等原因造成的过失误差及故障问题,将模式识别中的Fisher判别分析法引入过程显著误差侦破和故障监测,提出了基于Fisher判别分析和核回归的质量监控和估计方法。首先利用Fisher方法对输入数据进行在线监测,若系统运行正常,则用核主元回归方法对质量数据进行预测和估计;否则将存在故障和过失误差的数据剔除并利用贡献图法确定故障原因。实际工业过程数据仿真研究验证了该方法进行故障监测和质量估计的有效性。
6.通过分析上海焦化甲醇精馏过程的特点和工艺流程,开发出了一套统计性能监控和质量估计软件,并将其应用到实际生产过程中,取得了良好效果,从而为上海焦化综合信息化平台和先进控制的成功实施奠定了坚实的基础。
In real industrial processes, effective performance monitoring and quality prediction are the key to ensure safety, enhance product quality and economy benefit. For the complex industrial process, it is difficult to achieve the accurate mathematical model. Even if it could be achieved, the equations which are predigested can only describe part relationships of energy and mass. These limit the application of methods based on rigorous mathematical model. On the other hand, with the rapid development of computer technology, a large amount of process data have been sampled and collected. How to transform these collected data into valuable information, and mine it deep-level to improve the monitoring performance becomes a challenge issue. It is one of the most active research areas in the field of process control.
Statistical performance monitoring is a method based on statistical theory for online fault detection and diagnosis via analysis to the collected data. The information extracted from process data could reflect the operating status at any time, reduce the losses caused by faults and enhance product quality.
Because of the limitation of technology and cost, many key variables are difficult to measure via sensors in industrial process. With the market competition become more and more furious, it has become an important factor holding back the product quality further improvement. Quality prediction and estimation (soft sensor) technology has been proposed to solve this problem. It can estimate the variables that are difficult to measure directly. The results can be used for quality control and decision support. It lays a foundation for the integrated automation.
In view of the characteristics of continuous and batch industry processes, some improvements of the tradition monitoring and prediction methods have been made, and new statistical monitoring algorithms are also proposed in this thesis.
The main results and contributions of this dissertation are stated as follows:
1. Using the advantage of kernel component analysis (KPCA) for nonlinear monitoring and introducing the similarity analysis for fault diagnosis, a new performance monitoring and fault diagnosis method based on KPCA and pattern matching is proposed. Aiming at the existing problem in traditional PCA similarity analysis, the method is improved. Nonlinear principal component similarities are firstly calculated. Then the integrated similarity index is proposed through endowing with different weights to PCA similarity, KPCA similarity and distance similarity. Fault diagnosis is performed through pattern matching of different faults. Effectiveness of the proposed method is verified through TE process.
2. In view of the complex industrial processes, a nonlinear process monitoring method based on KICA is proposed through integrating the merit of KPCA to deal with nonlinear data and ICA to extract the high-dimensional information. The data are firstly mapped into high-dimensional feature subspace. Then the ICA algorithm is performed. Performance monitoring is implemented through constructing the statistical index and control limit in the feature space. Application results to the FCCU process indicate that the proposed method can effectively capture nonlinear relationship among variables. Its performance significantly outperforms monitoring method based on ICA or KPCA.
3. In view of the characteristics of batch process and using the advantage of kernel theory, a novel batch performance monitoring and fault identification strategy based on kernel fisher discriminant analysis (KFDA) is proposed. The approach only uses present data and overcomes pre-estimating the unknown part of process variable trajectory in multi-way PCA (MPCA). The key to the proposed approach is to calculate the distance of block data which are projected to the optimal kernel Fisher discriminant vector between new batch and reference batch. Through comparing distance with the predefined threshold, it can be considered whether the batch is normal or abnormal. Similar degree between the present discriminant vector and the optimal discriminant vector of fault in historical data set is used to perform fault diagnosis. Simulation results on a penicillin fermentation process demonstrate that, in comparison to the MPCA method, the proposed method is more accurate and efficient to detect and diagnose the malfunctions.
4. Using the advantage of kernel partial least squares (KPLS) in nonlinear regression, a new quality estimation and prediction method based on KPLS is proposed. The basic idea of the method is to first map data in the original space into high-dimensional feature space via nonlinear kernel and then performs quality estimation and prediction. Application results to a simple example and real data in an industrial oil refinery factory show that the proposed method can effectively capture nonlinear relationship among variables and have better estimation performance than PLS and other linear approaches.
5. In view of the gross error caused by sensor faults or process leakage, a novel performance monitoring and quality estimation approach based on fisher discriminant analysis (FDA) and kernel regression is proposed. FDA is first used for quality monitoring.
If the process is under normal condition, then kernel regression is further used for quality prediction and estimation. Otherwise, if faults have occurred, contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can effectively detect the happening fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.
6. The model of performance monitoring and quality prediction is built and a software package is developed through analysis to the characteristic and techniques of methanol process. They are successfully applied to the monitoring of methanol process in Shanghai Coking and Chemical Corporation (SCCC). What we have done lay the foundations for the performance of integrated automation and advanced control in SCCC.
引文
[1] Calandranis, J., Stephanopoulos, G., Nunokawa S. Online performance monitoring and diagnosis. Chemical Engineering Progress, 1990, 86(1): 60-68.
[2]周韶圆.基于HMM的统计过程监控研究[博士学位论文].杭州:浙江大学,2005.
[3]赵旭.基于统计学方法的过程监控与质量控制研究[博士学位论文].上海:上海交通大学,2007.
[4] Chiang, L.H., Russell, E.L. Braatz, R.D. Fault Detection and Diagnosis in Industrial Systems. Hong Kong, Springer, 2001.
[5] Basseville, M. Detecting changes in signals and systems– a survey. Automatica, 1988, 24(3): 309-326.
[6] Frank, P.M. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy-a survey and some new results. Automatica, 1990, 26(3): 459-474.
[7] Huang, Y., Gertler, J., Mcavoy, T.J. Sensor and actuator fault isolation by structured partial PCA with nonlinear extensions. Journal of Process Control, 2000, 10(5): 459-469.
[8] Zhang, J., Roberts, P.D. Process fault diagnosis with diagnostic rules based structural decomposition. Journal of Process Control, 1991, 1(5): 259-269.
[9] Zhang, J., Martin, E., Morris, A.J. Fault detection and classification through multivariate statistical techniques. Proceedings of the American Control Conference. Piscataway, New Jersey, 1995, 169-174.
[10] Wang, X.Z., McGreavy, C. Automatic classification for mining process operational data. Industrial &Engineering Chemistry Research, 1998, 37(6): 2215-2222.
[11]张杰,阳宪惠.多变量统计过程控制.北京:化学工业出版社, 2000.
[12] Shewhart, W.A. Statistical method from the viewpoint of quality control. New York: Dover, 1986.
[13] Page, E.S. Continuous inspection schemes. Biometrika, 1954, 41: 100-114.
[14] Roberts, S.W. Control chart tests based on geometric moving average. Technometrics, 1959, 1: 239-250.
[15] Page, E.S. Cumulative sum control charts. Technometrics, 1961, 3:1-9.
[16] Thompson, J.R., Koronacki, J. Statistical process control for quality improvement. New York: Chapman and Hall, 1993.
[17] Bissel, D. Statistical methods for SPC and TQM. London: Chapman and Hall, 1994.
[18] Wold, S. Cross-validatory estimation of the number of components in Factor and principal components models. Technometrics, 1978, 20(4): 397-405.
[19] Himes, D.M., Storer, R.H., Georgakis, C. Determination of the number of principal components for disturbance detection and isolation. Proceedings of the American Control Conference. Baltimore, USA, 1994, 1279-1283.
[20] Qin, S.J., Dunia, R. Determining the number of principal components for best reconstruction. Journal of Process Control, 2000, 10(2-3): 245-250.
[21] Geladi, P., Kowalshi, B.R. Partial least squares regression: A tutorial. Analytica Chimica Acta, 1986, 185(1): 1-17.
[22]王惠文.偏最小二乘回归方法及应用.北京:国防工业出版社, 1999.
[23] Dayal, B.S., MacGregor, J.F. Improved PLS algorithms. Journal of Chemometrics, 1997, 11(1): 73-85.
[24] R?nnar, S., MarGregor, J.F., Wold, S. Adaptive batch monitoring using hierarchical PCA. Chemometrics and Intelligent Laboratory Systems, 1998, 41(1): 73-81.
[25] Tsung, F. Statistical monitoring and diagnosis of automatic controlled process using dynamic PCA. International Journal of Production Research, 2000, 38(3): 625-637.
[26] Chen, J.H., Liao, C.M. Dynamic process fault monitoring based on neural network and PCA. Journal of Process Control, 2002, 12(2): 277-289.
[27] Kaspar,M.H., Ray, W.H. Dynamic PLS modeling for process control. Chemical Engineering Science, 1993, 48(9): 3447-3461.
[28] Lakshminarayanan, S., Shah, S.L., Nandakumar, K. Modeling and control of multivariable processes: Dynamic PLS approach. AIChE Journal, 1997, 43(9): 2307-2322.
[29] Ku, W., Storer, R.H., Georgakis, C. Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems, 1995, 30(1): 179-196.
[30]陈耀,王海文,孙优贤.基于动态主元分析的统计过程监视.化工学报, 2000, 51(5): 666-670.
[31] Negiz, A., Cinar, A. Statistical monitoring of multivariate dynamic processes with state-space models, AIChE Journal, 1997, 43(8): 2002-2020.
[32] Simoglou, A., Martin, E.B., Morris, A.J. Statistical performance monitoring of dynamic multivariate processes using state space modeling. Computers and Chemical Engineering, 2002, 26(6): 909-920.
[33] Simoglou, A., Martin, E.B., Morris, A.J. A comparison of canonical variate analysis and partial least squares for the identification of dynamic processes. Proceedings of the American Control Conference, San Diego, California, USA, 1999, 832-837.
[34] Kruger, U., Zhou, Y., George, W.I. Improved principal component monitoring of large-scale processes. Journal of Process Control, 2004, 14(4): 879-888.
[35] Kosanovich, K.A., Piovoso, M.J., Dahl, K.S. Multi-way PCA applied to an industrial batch process. Proceeding of American Control Conference, 1994, 1294-1298.
[36]陆宁云.间歇工业过程的统计建模、在线监测和质量预测[博士学位论文].沈阳:东北大学, 2004.
[37] MacGregor, J.F., Jackle,C., Kiparissides, C. Process monitoring and diagnosis by multiblock PLS methods. AIChE Journal, 1994, 40(5): 826-838.
[38] Wold, S., Kettaneh, N., Tjessem, K. Hierarchical multiblock PLS and PC models for easier interpretation and as an alternative to variable selection. Journal of Chemometrics, 1996, 10(5-6): 463-482.
[39] Wangen, L.E., Kowalski, B.R. A multiblock partial least squares algorithm for investigating complex chemical systems. Journal of Chemometrics, 1988, 3(1): 3-20.
[40] Westerhuis, J.A., Kourti, T., MacGregor, J.F. Analysis of multiblock and hierarchical PCA and PLS models. Journal of Chemometrics, 1998, 12(5): 301-321.
[41] Martin, E.B., Morris, A.J. Non-parametric confidence bounds for process performancemonitoring charts. Journal of Process Control, 1996, 6(6): 349-358.
[42] Chen, Q., Wynne, R.J., Goulding, P. The application of principal component analysis and kernel density estimation to enhance process monitoring. Control Engineering Practice, 2000, 8(5): 531-543.
[43]陈希孺,方兆本,李国英,等.非参数统计.上海:上海科学技术出版社, 1989.
[44]王海清,宋执环,王慧,等.小波阈值密度估计器的设计与应用.仪器仪表学报, 2002, 23(1): 12-15.
[45] Hyv?rinen, A., Oja, E. Independent component analysis: algorithms and applications. Neural Networks, 2000, 13(4-5): 411-430.
[46] Li, R.F., Wang, X.Z. Dimension reduction of process dynamic trends using independent component analysis. Computers and Chemical Engineering, 2002, 26(3): 467-473.
[47] Kano, M., Tannaka, S., Hasebe, S. Monitoring independent components for fault detection. AIChE Journal, 2003, 49(4): 969-976.
[48] Kano, M., Hasebe, S., Hashimoto, I. Evolution of multivariate statistical process control: application of independent component analysis and external analysis. Computers and Chemical Engineering, 2004, 28(6-7): 1157-1166.
[49]陈国金,梁军,钱积新.独立元分析方法及在化工过程监控和故障诊断中的应用.化工学报, 2003, 54(10): 1474-1477.
[50] Lee, J.M., Yoo, C.K., Lee, I.B. Statistical process monitoring with independent component analysis. Journal of Process Control, 2004, 14(5): 467-485.
[51]何宁,郭明,谢磊,等.独立主元在动态多变量过程中的故障检测与诊断.化工学报, 2005, 56(4): 646-652.
[52] Kramer, M.A., Nonlinear principal component analysis using autoassociative neural networks. AIChE Journal, 1991, 37(2): 233-243.
[53] Dong, D., McAvoy, T.J. Batch tracking via nonlinear principal component analysis. AIChE Journal, 1996, 42(8): 2199-2208.
[54] Qin, S.J., McAvoy, T.J. Nonlinear PLS modeling using neural networks. Computers and Chemical Engineering, 1992, 16(4): 379-391.
[55] Malthouse, E.C., Tamhane, A.C., Mah, R.S.H. Nonlinear partial least squares. Computers and Chemical Engineering, 1997, 21(8): 875-890.
[56] Baffi, G., Martin, E.B. Morris, A.J. Non-linear projection to latent structures revisited: the quadratic PLS algorithm. Computers and Chemical Engineering, 1999, 23(3): 395-411.
[57] Zhang, J., Martin, E.B., Morris, A.J. Process monitoring using non-linear statistical techniques. Chemical Engineering Journal, 1997, 67(3): 181-189.
[58] Lin, W., Qian, Y., Li, X. Non-linear dynamic principal component analysis for the online process monitoring and diagnosis. Computers and Chemical Engineering, 2000, 24(2-7): 423-429.
[59] Hastie,T., Stuetzle, W. Principal curves. Journal of American Statistical Association, 1989, 84(406): 502-516.
[60] Dong, D., McAvoy, T.J. Nonlinear principal component analysis-based on principal curves and neural networks. Computers and Chemical Engineering, 1996, 20(1):65-78.
[61] Sch?lkoph, B., Smola, A. Learning with kernels. Cambridge: MIT Press, 2002.
[62] Gnanadesikian, R. Methods for statistical data analysis of multivariate observation. New York:Wiley, 1977
[63] Chiang, L.H., Kotanchek, M.E., Kordon, A.K. Fault diagnosis based on Fisher discriminant analysis and support vector machines. Computers and Chemical Engineering, 2004, 28(8): 1389-1401.
[64] Lee, J.M., Lee, I.B. Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science, 2004, 59(1): 223-234.
[65] Nomikos, P. MacGregor, J.F. Monitoring batch processes using multiway principal component analysis. AIChE Journal, 1994, 40(8): 1361-1375.
[66] Nomikos, P., MacGregor, J.F. Mutiway partial least squares in monitoring batch process. Chemometrics and Intelligent Laboratory Systems, 1995, 30(1): 97-108.
[67] Lennox, B., Hiden, H.G., Montague, G.A. Application of multivariate statistical process control to batch operations. Computers and Chemical Engineering, 2000, 24(2-7): 291-296.
[68] Cho, H.W., Kim, K.J. A method for predicting future observations in the monitoring of a batch process. Journal of Quality Technology, 2003, 35(1): 59-69.
[69] He, N., Wang, S.Q., Xie, L. An improved adaptive multi-way principal component analysis for monitoring streptomycin fermentation. Chinese Journal of Chemical Engineering. 2004, 12(1): 96-101.
[70] Chen, J., Liu, J. Process monitoring using principal component analysis in different operating time processes. IFAC 14th Triennial World Congress, Beijing, P.R. China, 1999.
[71] Dahl, K.S., Piovoso, M.J., Kosanovich, K.A. Translating third-order data analysis methods to chemical batch processes. Chemometrics and Intelligent Laboratory Systems, 1999, 46(2): 161-180.
[72] Bro, R., Smilde, A.K., Jong, S. On the difference between low-rank and subspace approximation: improved model for multi-linear PLS regression. Chemometrics and Intelligent Laboratory Systems, 2001, 58(1): 3-13.
[73] Kassidas, A., MacGregor, J.K., Taylor, P.A. Synchronization of batch trajectories using dynamic time warping. AIChE Journal, 1998, 44(4): 864-875.
[74] Lu, N.Y., Gao, F.R., Yang, Y. PCA-Based Modeling and On-line Monitoring Strategy for Uneven-Length Batch Processes. Industrial &Engineering Chemistry Research, 2004, 43(13): 3343-3352.
[75] Xie, L., Zhang, J.M., Wang, S.Q. A robust statistical batch process monitoring framework and its application. Chinese Journal of Chemical Engineering, 2004, 12(5): 682-687.
[76] Yoo, C., Lee, J.M., Vanrolleghem, P.A., et al. On-line monitoring of batch processes using multi-way independent component analysis. Chemometrics and Intelligent Laboratory Systems, 2004, 71(2): 151-163.
[77] Lee, J.M., Yoo, C.K., Lee, I.B. Fault detection of batch processes using multi-way kernel principal component analysis. Computers and Chemical Engineering, 2004, 28(9): 1837-1847.
[78] Ramaker, H.J., Van Sprang, E.N.M, Gurden, S.P. Improved monitoring of batch processes by incorporating external information. Journal of Process Control, 2004, 12(4): 569-576.
[79] Martin, E.B., Morris, A.J. Enhanced bio-manufacturing through advanced multivariate statistical technology. Journal of Biotechnology, 2002, 99(3): 223-235.
[80] Hodge, D., Simon, L., Karim, M.N. Data driven approaches to modeling and analysis of bioprocesses: some industrial examples. Proceedings of American Control Conference, 2003,2062-2076.
[81] Skoglund, A., Brundin, A., Mandenius, C.F. Monitoring a paperboard machine using multivariate statistical process control. Chemometrics and Intelligent Laboratory Systems, 2004, 73(1 SPEC. ISS.): 3-6.
[82] Mishra, B.V., Mayer, E., Raisch, J. Short-term scheduling of batch processes. A comparative study of different approaches. Industrial&Engineering Chemical Research, 2005, 44(11): 4022- 4034.
[83]郭明.基于数据驱动的流程工业性能监控与故障诊断研究[博士学位论文],杭州:浙江大学, 2004.
[84] Bakshi, J.F. Multiscale PCA with application to multivariate statistical process monitoring. AIChE Journal, 1998, 44(7): 1596-1610.
[85]谢磊.间歇过程统计性能监控研究[博士学位论文].杭州:浙江大学, 2005.
[86] Miller, P., Swanson, R.E., Heckler, C.E. Contribution plot: A missing link in multivariate quality control. Application Mathematics and Computer Science, 1992, 8(4): 775-792.
[87] Gertler, J., Li, W., Huang, Y., McAvoy, T. Islation enhanced principal component analysis. AIChE Journal, 1999, 45(2): 323-334.
[88]王海清.工业过程监控:基于小波和统计学的方法[博士学位论文].杭州:浙江大学, 2000.
[89] Dunia, R., Qin, S.J. Subspace approach to multidimensional fault identification and reconstruction. AIChE Journal, 1998, 44(8): 1813-1831.
[90] Yue, H.H., Qin, S.J. Reconstruction-based fault identification using a combined index. Industrial and Engineering Chemical Research, 2001, 40(20): 4403-4414.
[91]王海清,宋执环,王慧. PCA过程检测方法的故障检测行为分析.化工学报, 2002, 53(3): 297-301.
[92] Raich, A., Cinar, A. Diagnosis of process disturbances by statistical distance and angle measures. Computers and Chemical Engineering, 1997, 21(6): 661-673.
[93] Zhang, J., Martin, E.B., Morris, A.J. Fault detection and diagnosis using multivariate statistical techniques. Chemical Engineering Research and Design, 1996, 74(1): 89-96.
[94] Johnson, J.E., Wichern, D.W. Applied multivariate statistical analysis. 3rded. New Jersey: Prentice- hall, 1992.
[95] Kano, M., Hasebe, S., Hashimoto, I. A new multivariate statistical process monitoring method using principal component analysis. Computers and Chemical Engineering, 2001, 25(7-8): 1103-1113.
[96]郭明,王树青.基于特征子空间的系统性能监控与工况识别.化工学报, 2004, 55(1): 151-154.
[97] Guo, M., Xie, L., Wang, S.Q. Research on an integrated ICA-SVM based framework for fault diagnosis. IEEE International Conference on Systems, Man & Cybernetics, Washington, USA, 2003, 2710-2715.
[98] Chu, Y., Qin, S.J., Han, C. Fault detection and operation mode identification based on pattern classification with variable selection. Industrial&Engineering Chemical Research, 2004, 43(7): 1701-1710.
[99] Joseph, B., Brosilow, C.B. Inferential control of process. AIChE Journal, 1978, 24(3): 485-509.
[100] Sarkar, P., Gupta, S.K. Steady state simulation of continuous flow stirred tank slurry propylenepolymerization reactors. Polymer Engineering and Science, 1992, 32(11): 732-742.
[101] Sarkar, P., Gupta, S.K. Dynamic simulation of propylene polymerization in continuous flow stirred tank reactors. Polymer Engineering & Science, 1993, 33(6): 368-374.
[102] McAuley, K.B., MacGregor, J.F. Online inference of polymer properties in an industrial polyethylene reactor. AIChE Journal, 1991, 37(6): 825-835.
[103] Sato, C., Ohtani, T., Nishitani, H. Modeling simulation and nonlinear control of a gas-phase polymerization process. Computers and Chemical Engineering, 2000, 24(2-7): 945-951.
[104] Bettoni, A., Bravi, M., Chianese, A. Inferential control of a side stream distillation column. Computers and Chemical Engineering, 2000, 23(11):1737-1744.
[105] Amirthalingam, R., Lee, J.H. Subspace identification based on inferential control applied to a continuous pulp digester. Journal of Process Control, 1999, 9(5): 397-406.
[106] Lang, L., Gillis, E.D. Nonlinear observers for distillation columns. Computers and Chemical Engineering, 1990, 14(11): 1297-1301.
[107] Gudi, R.D., Shah, S., Gray, M. Adaptive multivariate state and parameter estimation strategies with application to a bioreactor. AIChE Journal, 1995, 41(11): 2451-2464.
[108] Tham, M.T., Montague, G. A., Morris, et al. Soft sensors for process monitoring and inferential control. Journal of Process Control, 1991, 1(1): 3-14.
[109] Kreisselmeier, G. Adaptive observers with exponential convergence. IEEE Transactions on Automatic Control, 1977, 22(1): 2-8.
[110] Guilandoust, M.T., Morris, A.J., Tham, M.T. Adaptive inferential control. IEE Proceedings D, 1987, 134(3): 171-179.
[111] Quintero-Marnol, E., Luyben, W. L., Georgakis,C. Application of extended Luenberger observer to the control of multicomonent batch distillation. Industrial&Engineering Chemical Research, 1991, 30(8): 1870-1880.
[112] Weber, R., Brosilow, C.B. The use of secondary measurement to improve control. AIChE Journal, 1972, 18(3): 614-623.
[113] Guilandoust, M.T., Morris, A.J., Tham, M.T. An adaptive estimation algorithm for inferential control. Industrial&Engineering Chemical Research, 1988, 27(9): 1658-1664.
[114] Tham M.J., Morris, A.J., Montague, G.A. Soft-sensing a solution to the problem of measurement delays. Chemical Engineering Research and Design, 1989, 67(6): 547-554.
[115]梁旻,熊智华,周强,等. MTBE反应器软测量系统与控制.化工自动化及仪表,1999,26(6): 23-27.
[116] Kresta, J.V., Marlin, T. E., MacGregor, J. F. Development of inferential process models using PLS. Computers and Chemical Engineering, 1994, 18(7): 597–611.
[117] Wold, S., Kettaneh-wold, N., Skagerberg, B. Nonlinear PLS modeling. Chemometrics and Intelligent Laboratory Systems, 1989, 7(1-2): 53-65.
[118] Mejdell, T., Skogestad, S. Estimation of distillation composition from multiple temperature measurements using partial-least-squares regression. Industrial &Engineering Chemical Research, 1991, 30(12): 2543-2555.
[119] Mejdell, T., Skogestad, S. Output estiamtion using multiple secondary measurements. High- Purity Distillation. AIChE Journal, 1993, 39(10): 1641-1653.
[120] Qin, S.J., Yue, H.Y., Dunis, R. Self-validating inferential sensors with application to air emission monitoring. Industrial&Engineering Chemical Research, 1997, 36(5):1675-1685.
[121] Skageberg, B., MacGrego,r J.F., Kiparissides, C. Multivariate data analysis applied to low- density polyethylene reactors. Chemometrics and Intelligent Laboratory Systems, 1992, 14(1- 3): 341-356.
[122] Baffi, F.G., Martin, E.B., Morris, A.J. Nonlinear dynamic projection to latent structures modeling. Chemometrics and Intelligent Laboratory Systems, 2000, 52(1): 5-22.
[123]罗建旭.软测量若干关键技术的研究及其在石油化学工业过程中的应用[博士学位论文].上海:上海交通大学, 2004.
[124] Yan, W.W., Shao, H.H., Wang, X.F. Soft sensing modeling based on support vector machine and Bayesian model selection. Computers and Chemical Engineering, 2004, 28(8): 1489-1498.
[125] Nomikos, P., MacGregor, J.F. Multivariate SPC charts monitoring batch processes. Technometrics, 1995, 37(1): 41-59.
[126] Wise, B.M., Gallagher, N.B. The process chemometrics approach to process monitoring and fault detection. Journal of Process Control, 1996, 6(6): 329-348.
[127] Malthouse, E.C., Mah, R.S.H., Tamhane, A.C. Some theoretical results on nonlinear principal component analysis. American Control Conference, 1995, Seattle, USA, 744-748.
[128] Jia, F., Martin, E.B., Morris, A.J. Nonlinear principal component analysis for process fault detection. Computers and Chemical Engineering (Suppl.), 1988, 22: S581- S854.
[129] Xie, L, Zhang, Q.L., Wang, S.H.. Linear neural networks pruning techniques and their applications in process monitoring. IEEE International Conference on Systems, Man &Cybernetics, Washington, USA, 2003.
[130] Sch?lkoph, B., Smola, A., Müller, K.-R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1998, 10(5): 1299-1399.
[131] Sorsa, T., Koivo, H.N. Application of artificial neural networks in process fault diagnosis. Automatica, 1993, 29(4): 843-849.
[132]郭明,谢磊,何宁,等.一种基于ICA-SVM的故障诊断方法.中南工业大学学报(自然科学版), 2003, 34(4): 447-450.
[133] Zhao, X, Wen X.-J, Shao, H.H. Combination method of principal component analysis and support vector machine for on-line process monitoring and fault diagnosis. Journal of Donghua University. 2006, 23(1): 53-58.
[134] Krzanowski, W. J. Between-groups comparison of principal components. Journal of American Statistical Association, 1979, 74(367), 703-707.
[135] Johannesmeyer, M.C., Singhai, A., Seborg, D.E. Pattern matching in historical data. AIChE Journal, 2002, 48(9): 2022-2038.
[136] Singhai, A., Seborg, D. E. Clustering of multivariate time-series data. Proceedings of American Control Conference, 2002, 3931-3936.
[137] Singhai, A., Seborg, D. E. Pattern matching in multivariate time series databases using a window-moving approach. Industrial&Engineering Chemistry Research, 2002, 41(16): 3822- 3838.
[138] Singhai, A., Seborg, D.E. Data compression issues with pattern matching in historical data. Proceedings of American Control Conference, 2003, 3696-3701.
[139] Ge, Z.Q., Song, Z.H. Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors. Industrial&Engineering Chemistry Research, 2007, 46(7): 2054-2063.
[140]李巍华.基于核方法的机械故障特征提取与分类技术研究[博士学位论文].武汉:华中科技大学, 2003.
[141] Vapnik, V. An overview of statistical learning theory. IEEE Transactions on Neural Network, 1999, 10(5): 988-999.
[142] Genton, M.G. Classes of Kernels for Machine Learning: a statistical perspective. Journal of Machine Learning Research, 2001, 2(12): 299-312.
[143]阎威武.支持向量机理论、方法和应用研究[博士学位论文].上海:上海交通大学, 2004.
[144]王华忠,俞金寿.基于混合核函数PCR方法的工业过程软测量建模.化工自动化及仪表, 2005, 32(2): 23-25.
[145] Jackson, J.E., Mudhalkar, G.S. Control procedures for residuals associated with principal conponent analysis. Technometrics, 1979, 21(3): 341-349.
[146] Mardia, K.V., Kent, J.T., Bibby, J.M. Multivariate Analysis. London: Academic, 1979.
[147] Downs, J.H., Vogel, E.F. A plant-wide industrial process control problem. Computers Chemical Engineering, 1993, 17(3): 245-255.
[148] Ricker, N.L. Tennessee Eastman challenge archieve. Available at http://depts.washington.edu /control/LARRY/TE/download.html, 1999.
[149]杨竹青,李勇,胡德文.独立成分分析方法综述.自动化学报, 2002, 28(5): 762-772.
[150] Jutten, C, Herault, J. Independent component analysis. Proceedings of European Signal Processing Conference, 1988, 287-314.
[151]何宁.基于ICA-PCA方法的流程工业过程监控与故障诊断研究[博士学位论文].杭州:浙江大学, 2004.
[152] Yang, H.H, Amari, S.-I. Adaptive online learning algorithms for blind separation: maximum entropy and minimum mutual information. Neural Networks, 1997, 9(67): 1457-1481.
[153]杨福生,洪波.独立分量分离的原理与应用.清华大学出版社,北京,2006.
[154] Cardoso, J.-F. Blind signal processing: statistical principles. Proceeds of IEEE, 1998, 86(10): 2009-2025.
[155] Bell, A.J., Sejnowski, A.J. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 1995, 7(6): 1129-1159.
[156] Obradovic, D., Deco, G. Information maximization and independent component analysis: Is there a difference? Neural Computation, 1998, 10(8): 2085-2101.
[157] Lee, T.-W., Girolami, M., Bell, A.J., et al. A unifying information-theoretic framework for independent component analysis. International Journal of Computer and Mathematics with Application, 2000, 39(11): 1-12.
[158] Cardoso, J.-F. Higher order contrasts for independent component analysis. Neural Computation, 1999, 11(1): 157-192.
[159] Cardoso, J.-F. Information and maximum likelihood for blind source separation. IEEE Transactions on Signal Processing Letters, 1997, 4(4): 112-114.
[160] Yang, J., Gao, X., Zhang, D., et al. Kernel ICA: An alternative formulation and its application to face recognition. Pattern Recognition, 2005, 38(10):1784-1787.
[161] Bach, F.R., Jordan, M. I. Kernel independent component analysis. Journal of Machine Learning, 2002, 3(1):1-48.
[162] Lee, J.M., Qin, S.J., Lee, I.B. Fault detection of non-linear processes using kernel independentcomponent analysis. Canadian Journal of Chemical Engineering, 2007, 85(4): 526-536.
[163] Zhang, Y., Qin, S.J. Fault detection of nonlinear processes using multiway kernel independent component analysis. Industrial & Engineering Chemistry Research, 46(23): 7780-7787.
[164] Amari, S. L., Cichocki, A., Yang, H. A new learning algorithm for blind source separation. Advances in Neural Information Processing Systems, 1996, 8: 757-763.
[165] Hyv?rinen, A., Oja, E. A fast fixed-point algorithm for independent component analysis. Neural Computation, 1997, 9(7): 1483-1492.
[166] Tracy, N.D., Young, J.C., Mason, R.L. Multivariate control charts for individual observations. Journal of Quality Technology, 1992, 24: 88-95.
[167] Wand, M.P., Jones, M.C. Kernel smoothing. UK: Chapman & Hall, 1995.
[168] McFarlane, R.C., Reineman, R.C., Bartee, J.F., et al. Dynamic simulator for a model IV fluid catalytic cracking unit. Computers and Chemical Engineering, 1993, 17(3): 275-300
[169] Lu, N.Y., Gao, F.R., Wang, F.L. Sub-PCA modeling and on-line monitoring strategy for batch processes. AIChE Journal, 2004, 50(1): 255-259.
[170] Lee, J.M., Yoo, C.K., Lee, I.B. On-line batch process monitoring using a consecutively updated multiway principal component analysis model. Computers and Chemical Engineering, 2003, 27(12): 1903-1912.
[171]赵旭,阎威武,邵惠鹤.基于核Fisher判别分析方法的非线性统计过程监控与故障诊断.化工学报, 2007, 58(4): 951-956.
[172] Mika, S., R?tsch, G., Weston, J. Fisher discriminant analysis with kernels. Nerual Networks for Signal Processing IX, 1999, 41-48.
[173] Sch?lkopf, B., Mika, S., Burges, C. Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks, 1999, 10 (5): 1000-1016.
[174] Sch?lkopf, B., Platt, J.C., Smola, A.J. Kernel Method for Percentile Feature Extraction. Technical report, MSR-TR-2000-22. Microsoft Research, 2000.
[175] Vapnik, V.N. Statistical Learning Theory. New York: John Wiley&Sons, 1998.
[176] Smola, A., Mangasarian, O. L., Sch?lkopf, B. Sparse Kernel Feature Analysis. Technical Report 99-04 .University of Wisconsin Madison, 1999.
[177] Birol, G., Undey, C., Cinar, A. A modular simulation package for fed-batch fermentation: penicillin production. Computer and Chemical Engineering, 2002, 26(11): 1553-1565.
[178] Walczak, B, Massart, D.L. Dealing with missing data: PartΙ. Chemometrics and Intelligent Laboratory System, 2001, 58(1): 15-27.
[179] Walczak, B, Massart, D.L. Dealing with missing data: Part II. Chemometrics and Intelligent Laboratory System, 2001, 58(1): 29-42.
[180] Kim, D.S., Yoo, C.K., Kim,Y.I., et al. Calibration, prediction and process monitoring model based on factor analysis for incomplete process data. Journal of Chemical Engineering of Japan, 2005, 38(12):1025-1034.
[181] Ignova, M., Glassey, J., Ward, A.C., et al. Multivariate statistical methods in bioprocess fault detection and performance forecasting. Transactions of the Institute of Measurement and Control 1999; 19(5): 271-279.
[182] Ignova, M., Montague, G.A., Ward, A.C., et al. Fermentation seed quality analysis with self- organising neural networks. Biotechnology and Bioengineering 1999, 64(1): 82-91.
[183] Piovoso, M.J., Kosanovch, K.A. Application of multivariate statistical methods to processmonitoring and controller design. International Journal of Control, 1994, 59(3): 743-756.
[184]李春富.基于数据的软测量建模方法及其应用研究[博士学位论文].北京:清华大学, 2004.
[185] Zhao, Z., Xia, X., Wang, J., et al. Nonlinear dynamic matrix control based on multiple operating models. Journal of Process Control, 2003, 13(1): 41-56.
[186] Helland, K., Berntsen, H.E, Borgen, O.S., et al. Recursive algorithm for partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 1992, 14(1-3): 129-137.
[187] Qin, S.J. Recursive PLS algorithms for adaptive data modeling. Computers and Chemical Engineering, 22(4-5): 503-514.
[188] Dayal, B.S., MacGregor, J.F. Recursive exponentially weighted PLS and its application to adaptive control and prediction. Journal of Process Control, 1997, 7(3): 169-179.
[189] Frank, I. E. A nonlinear PLS model. Chemometrics and Intelligent Laboratory Systems, 1990, 8(2): 109-119.
[190] Wold, S. Non-linear partial least squares modeling II spline inner function. Chemometrics and Intelligent Laboratory Systems, 1992, 14(1-3): 71-84.
[191] Li, T., Mei, H., Cong, P. Combining nonlinear PLS with the numeric generic algorithm for QSAR. Chemometrics and Intelligent Laboratory Systems, 1999, 45(1-2): 177-184.
[192] Rosipal, R., Trejo, L.J. Kernel partial least squares regression in reproducing kernel Hilbert space. Journal of Machine Learning Research, 2001, 2(6): 97-123.
[193] Haykin, S. Neural Network. Prentice Hall: Englewood Cliffs, NJ, 1999.
[194] Lee, D.S., Lee, M.W., Woo, S.H., et al. Multivariate online monitoring of a full-scale biological anaerobic filter process using kernel-based algorithms. Industrial&Engineering Chemistry Research, 2006, 45(12): 4335-4344.
[195] Rosipal, R., Trejo, L.J., Matthews, B. Kernel PLS-SVC for linear and nonlinear classification. Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC, 2003, 640-647.
[196] Kim, K., Lee, J.M., Lee, I.B. A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction. Chemonetrics and Intelligent Laboratory Systems, 2005, 79(1-2): 22-30.
[197]王华忠.核函数方法及其在过程建模与故障诊断中的应用研究[博士学位论文].上海:华东理工大学, 2004.
[198] Wold, H. Nonlinear estimation by iterative least squares procedures. Research Papers in Statistics, 1966, Wiley, New York.
[199]宋凯.基于PLS的统计质量监控研究与应用[博士学位论文].杭州:浙江大学, 2005.
[200] Hoskuldsson, A. PLS regression methods. Journal of Chemometrics, 1988, 2(3): 211-228.
[201]袁永根,李华生.过程系统测量数据校正技术,中国石化出版社, 1996.
[202] Wisnewski, P.A, Doyle, F.J., Primus, C.J. Measurement selection issues for the model predictive control of a Kamyr digester, Control Systems '96, Halifax, Canada, 1996, 153-156.
[203]翁玉凤,邵惠鹤,李宏阳,等.应用离散Karhunen-Loeve法选择丙烯浓度的辅助变量.上海交通大学学报, 1999, 33(4): 439-441.
[204] Wilson, D.J.H., Irwin, G.W., Lightdoby, G. RBF principal manifolds for process monitoring. IEEE Transactions on Neural Networks, 1999, 10(6): 1424-1434.
[205] Zhao, S.J., Zhang, J., Xu, Y.M., et al. Nonlinear projection to latent structures method and itsapplications. Industrial&Engineering Chemistry Research, 2006, 45(11): 3843-3852.
[206] Mejdell, T., Skogestad, S. Composition estimator in a pilot-plant distillation column using multiple temperature, Industrial & Engineering Chemistry Research, 1991, 30(12): 2555-2564.
[207] Park, S., Han, C. A nonlinear soft sensor based on multivariate smoothing procedure for quality estimation in distillation columns. Computers and Chemical Engineering, 2000, 24 (2-7): 871- 877.
[208] Zhang, X., Yan, W., Shao, H. Nonlinear multivariate quality estimation and predication based on kernel partial least squares. Industrial &Engineering Chemistry Research, 2008, 47(4): 1120 -1131.
[209] Mah, R.S.H., Tamhane, A.C. Detection of gross errors in process data. AIChE Journal, 1982, 28(5): 828-830.
[210] Tamhane, A.C., Mah, R.S.H. Data reconciliation and gross error detection in chemical process networks. Technometrics, 1985, 27(4): 409-422.
[211] Narasimhan, S., Mah, R.S.H. Generalized likelihood ratios for gross error identification. AIChE Journal, 1987, 33(9): 1514-1521.
[212]杨友麟,滕荣波.过程工业测量数据中过失误差的侦破与校正,化工学报, 1996, 23(6): 248-253.
[213] Cheng, Y.Q., Zhuang, Y.M., Yang, J.Y. Optimal Fisher discriminant analysis using the rank decomposition. Pattern Recognition, 1992, 25(1): 101-111.
[214] Gibass, M., Mackay, D.J.C. Efficient implementation of Gaussian process. Technical report, Department of Physics, Cavendish Laboratory, Cambridge University, UK, 1997.
[215] He, Q.P., Qin, S.J. A new fault diagnosis method using fault directions in fisher discriminant analysis. AIChE Journal, 2005, 51(2): 555-571.
[2]周韶圆.基于HMM的统计过程监控研究[博士学位论文].杭州:浙江大学,2005.
[3]赵旭.基于统计学方法的过程监控与质量控制研究[博士学位论文].上海:上海交通大学,2007.
[4] Chiang, L.H., Russell, E.L. Braatz, R.D. Fault Detection and Diagnosis in Industrial Systems. Hong Kong, Springer, 2001.
[5] Basseville, M. Detecting changes in signals and systems– a survey. Automatica, 1988, 24(3): 309-326.
[6] Frank, P.M. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy-a survey and some new results. Automatica, 1990, 26(3): 459-474.
[7] Huang, Y., Gertler, J., Mcavoy, T.J. Sensor and actuator fault isolation by structured partial PCA with nonlinear extensions. Journal of Process Control, 2000, 10(5): 459-469.
[8] Zhang, J., Roberts, P.D. Process fault diagnosis with diagnostic rules based structural decomposition. Journal of Process Control, 1991, 1(5): 259-269.
[9] Zhang, J., Martin, E., Morris, A.J. Fault detection and classification through multivariate statistical techniques. Proceedings of the American Control Conference. Piscataway, New Jersey, 1995, 169-174.
[10] Wang, X.Z., McGreavy, C. Automatic classification for mining process operational data. Industrial &Engineering Chemistry Research, 1998, 37(6): 2215-2222.
[11]张杰,阳宪惠.多变量统计过程控制.北京:化学工业出版社, 2000.
[12] Shewhart, W.A. Statistical method from the viewpoint of quality control. New York: Dover, 1986.
[13] Page, E.S. Continuous inspection schemes. Biometrika, 1954, 41: 100-114.
[14] Roberts, S.W. Control chart tests based on geometric moving average. Technometrics, 1959, 1: 239-250.
[15] Page, E.S. Cumulative sum control charts. Technometrics, 1961, 3:1-9.
[16] Thompson, J.R., Koronacki, J. Statistical process control for quality improvement. New York: Chapman and Hall, 1993.
[17] Bissel, D. Statistical methods for SPC and TQM. London: Chapman and Hall, 1994.
[18] Wold, S. Cross-validatory estimation of the number of components in Factor and principal components models. Technometrics, 1978, 20(4): 397-405.
[19] Himes, D.M., Storer, R.H., Georgakis, C. Determination of the number of principal components for disturbance detection and isolation. Proceedings of the American Control Conference. Baltimore, USA, 1994, 1279-1283.
[20] Qin, S.J., Dunia, R. Determining the number of principal components for best reconstruction. Journal of Process Control, 2000, 10(2-3): 245-250.
[21] Geladi, P., Kowalshi, B.R. Partial least squares regression: A tutorial. Analytica Chimica Acta, 1986, 185(1): 1-17.
[22]王惠文.偏最小二乘回归方法及应用.北京:国防工业出版社, 1999.
[23] Dayal, B.S., MacGregor, J.F. Improved PLS algorithms. Journal of Chemometrics, 1997, 11(1): 73-85.
[24] R?nnar, S., MarGregor, J.F., Wold, S. Adaptive batch monitoring using hierarchical PCA. Chemometrics and Intelligent Laboratory Systems, 1998, 41(1): 73-81.
[25] Tsung, F. Statistical monitoring and diagnosis of automatic controlled process using dynamic PCA. International Journal of Production Research, 2000, 38(3): 625-637.
[26] Chen, J.H., Liao, C.M. Dynamic process fault monitoring based on neural network and PCA. Journal of Process Control, 2002, 12(2): 277-289.
[27] Kaspar,M.H., Ray, W.H. Dynamic PLS modeling for process control. Chemical Engineering Science, 1993, 48(9): 3447-3461.
[28] Lakshminarayanan, S., Shah, S.L., Nandakumar, K. Modeling and control of multivariable processes: Dynamic PLS approach. AIChE Journal, 1997, 43(9): 2307-2322.
[29] Ku, W., Storer, R.H., Georgakis, C. Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems, 1995, 30(1): 179-196.
[30]陈耀,王海文,孙优贤.基于动态主元分析的统计过程监视.化工学报, 2000, 51(5): 666-670.
[31] Negiz, A., Cinar, A. Statistical monitoring of multivariate dynamic processes with state-space models, AIChE Journal, 1997, 43(8): 2002-2020.
[32] Simoglou, A., Martin, E.B., Morris, A.J. Statistical performance monitoring of dynamic multivariate processes using state space modeling. Computers and Chemical Engineering, 2002, 26(6): 909-920.
[33] Simoglou, A., Martin, E.B., Morris, A.J. A comparison of canonical variate analysis and partial least squares for the identification of dynamic processes. Proceedings of the American Control Conference, San Diego, California, USA, 1999, 832-837.
[34] Kruger, U., Zhou, Y., George, W.I. Improved principal component monitoring of large-scale processes. Journal of Process Control, 2004, 14(4): 879-888.
[35] Kosanovich, K.A., Piovoso, M.J., Dahl, K.S. Multi-way PCA applied to an industrial batch process. Proceeding of American Control Conference, 1994, 1294-1298.
[36]陆宁云.间歇工业过程的统计建模、在线监测和质量预测[博士学位论文].沈阳:东北大学, 2004.
[37] MacGregor, J.F., Jackle,C., Kiparissides, C. Process monitoring and diagnosis by multiblock PLS methods. AIChE Journal, 1994, 40(5): 826-838.
[38] Wold, S., Kettaneh, N., Tjessem, K. Hierarchical multiblock PLS and PC models for easier interpretation and as an alternative to variable selection. Journal of Chemometrics, 1996, 10(5-6): 463-482.
[39] Wangen, L.E., Kowalski, B.R. A multiblock partial least squares algorithm for investigating complex chemical systems. Journal of Chemometrics, 1988, 3(1): 3-20.
[40] Westerhuis, J.A., Kourti, T., MacGregor, J.F. Analysis of multiblock and hierarchical PCA and PLS models. Journal of Chemometrics, 1998, 12(5): 301-321.
[41] Martin, E.B., Morris, A.J. Non-parametric confidence bounds for process performancemonitoring charts. Journal of Process Control, 1996, 6(6): 349-358.
[42] Chen, Q., Wynne, R.J., Goulding, P. The application of principal component analysis and kernel density estimation to enhance process monitoring. Control Engineering Practice, 2000, 8(5): 531-543.
[43]陈希孺,方兆本,李国英,等.非参数统计.上海:上海科学技术出版社, 1989.
[44]王海清,宋执环,王慧,等.小波阈值密度估计器的设计与应用.仪器仪表学报, 2002, 23(1): 12-15.
[45] Hyv?rinen, A., Oja, E. Independent component analysis: algorithms and applications. Neural Networks, 2000, 13(4-5): 411-430.
[46] Li, R.F., Wang, X.Z. Dimension reduction of process dynamic trends using independent component analysis. Computers and Chemical Engineering, 2002, 26(3): 467-473.
[47] Kano, M., Tannaka, S., Hasebe, S. Monitoring independent components for fault detection. AIChE Journal, 2003, 49(4): 969-976.
[48] Kano, M., Hasebe, S., Hashimoto, I. Evolution of multivariate statistical process control: application of independent component analysis and external analysis. Computers and Chemical Engineering, 2004, 28(6-7): 1157-1166.
[49]陈国金,梁军,钱积新.独立元分析方法及在化工过程监控和故障诊断中的应用.化工学报, 2003, 54(10): 1474-1477.
[50] Lee, J.M., Yoo, C.K., Lee, I.B. Statistical process monitoring with independent component analysis. Journal of Process Control, 2004, 14(5): 467-485.
[51]何宁,郭明,谢磊,等.独立主元在动态多变量过程中的故障检测与诊断.化工学报, 2005, 56(4): 646-652.
[52] Kramer, M.A., Nonlinear principal component analysis using autoassociative neural networks. AIChE Journal, 1991, 37(2): 233-243.
[53] Dong, D., McAvoy, T.J. Batch tracking via nonlinear principal component analysis. AIChE Journal, 1996, 42(8): 2199-2208.
[54] Qin, S.J., McAvoy, T.J. Nonlinear PLS modeling using neural networks. Computers and Chemical Engineering, 1992, 16(4): 379-391.
[55] Malthouse, E.C., Tamhane, A.C., Mah, R.S.H. Nonlinear partial least squares. Computers and Chemical Engineering, 1997, 21(8): 875-890.
[56] Baffi, G., Martin, E.B. Morris, A.J. Non-linear projection to latent structures revisited: the quadratic PLS algorithm. Computers and Chemical Engineering, 1999, 23(3): 395-411.
[57] Zhang, J., Martin, E.B., Morris, A.J. Process monitoring using non-linear statistical techniques. Chemical Engineering Journal, 1997, 67(3): 181-189.
[58] Lin, W., Qian, Y., Li, X. Non-linear dynamic principal component analysis for the online process monitoring and diagnosis. Computers and Chemical Engineering, 2000, 24(2-7): 423-429.
[59] Hastie,T., Stuetzle, W. Principal curves. Journal of American Statistical Association, 1989, 84(406): 502-516.
[60] Dong, D., McAvoy, T.J. Nonlinear principal component analysis-based on principal curves and neural networks. Computers and Chemical Engineering, 1996, 20(1):65-78.
[61] Sch?lkoph, B., Smola, A. Learning with kernels. Cambridge: MIT Press, 2002.
[62] Gnanadesikian, R. Methods for statistical data analysis of multivariate observation. New York:Wiley, 1977
[63] Chiang, L.H., Kotanchek, M.E., Kordon, A.K. Fault diagnosis based on Fisher discriminant analysis and support vector machines. Computers and Chemical Engineering, 2004, 28(8): 1389-1401.
[64] Lee, J.M., Lee, I.B. Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science, 2004, 59(1): 223-234.
[65] Nomikos, P. MacGregor, J.F. Monitoring batch processes using multiway principal component analysis. AIChE Journal, 1994, 40(8): 1361-1375.
[66] Nomikos, P., MacGregor, J.F. Mutiway partial least squares in monitoring batch process. Chemometrics and Intelligent Laboratory Systems, 1995, 30(1): 97-108.
[67] Lennox, B., Hiden, H.G., Montague, G.A. Application of multivariate statistical process control to batch operations. Computers and Chemical Engineering, 2000, 24(2-7): 291-296.
[68] Cho, H.W., Kim, K.J. A method for predicting future observations in the monitoring of a batch process. Journal of Quality Technology, 2003, 35(1): 59-69.
[69] He, N., Wang, S.Q., Xie, L. An improved adaptive multi-way principal component analysis for monitoring streptomycin fermentation. Chinese Journal of Chemical Engineering. 2004, 12(1): 96-101.
[70] Chen, J., Liu, J. Process monitoring using principal component analysis in different operating time processes. IFAC 14th Triennial World Congress, Beijing, P.R. China, 1999.
[71] Dahl, K.S., Piovoso, M.J., Kosanovich, K.A. Translating third-order data analysis methods to chemical batch processes. Chemometrics and Intelligent Laboratory Systems, 1999, 46(2): 161-180.
[72] Bro, R., Smilde, A.K., Jong, S. On the difference between low-rank and subspace approximation: improved model for multi-linear PLS regression. Chemometrics and Intelligent Laboratory Systems, 2001, 58(1): 3-13.
[73] Kassidas, A., MacGregor, J.K., Taylor, P.A. Synchronization of batch trajectories using dynamic time warping. AIChE Journal, 1998, 44(4): 864-875.
[74] Lu, N.Y., Gao, F.R., Yang, Y. PCA-Based Modeling and On-line Monitoring Strategy for Uneven-Length Batch Processes. Industrial &Engineering Chemistry Research, 2004, 43(13): 3343-3352.
[75] Xie, L., Zhang, J.M., Wang, S.Q. A robust statistical batch process monitoring framework and its application. Chinese Journal of Chemical Engineering, 2004, 12(5): 682-687.
[76] Yoo, C., Lee, J.M., Vanrolleghem, P.A., et al. On-line monitoring of batch processes using multi-way independent component analysis. Chemometrics and Intelligent Laboratory Systems, 2004, 71(2): 151-163.
[77] Lee, J.M., Yoo, C.K., Lee, I.B. Fault detection of batch processes using multi-way kernel principal component analysis. Computers and Chemical Engineering, 2004, 28(9): 1837-1847.
[78] Ramaker, H.J., Van Sprang, E.N.M, Gurden, S.P. Improved monitoring of batch processes by incorporating external information. Journal of Process Control, 2004, 12(4): 569-576.
[79] Martin, E.B., Morris, A.J. Enhanced bio-manufacturing through advanced multivariate statistical technology. Journal of Biotechnology, 2002, 99(3): 223-235.
[80] Hodge, D., Simon, L., Karim, M.N. Data driven approaches to modeling and analysis of bioprocesses: some industrial examples. Proceedings of American Control Conference, 2003,2062-2076.
[81] Skoglund, A., Brundin, A., Mandenius, C.F. Monitoring a paperboard machine using multivariate statistical process control. Chemometrics and Intelligent Laboratory Systems, 2004, 73(1 SPEC. ISS.): 3-6.
[82] Mishra, B.V., Mayer, E., Raisch, J. Short-term scheduling of batch processes. A comparative study of different approaches. Industrial&Engineering Chemical Research, 2005, 44(11): 4022- 4034.
[83]郭明.基于数据驱动的流程工业性能监控与故障诊断研究[博士学位论文],杭州:浙江大学, 2004.
[84] Bakshi, J.F. Multiscale PCA with application to multivariate statistical process monitoring. AIChE Journal, 1998, 44(7): 1596-1610.
[85]谢磊.间歇过程统计性能监控研究[博士学位论文].杭州:浙江大学, 2005.
[86] Miller, P., Swanson, R.E., Heckler, C.E. Contribution plot: A missing link in multivariate quality control. Application Mathematics and Computer Science, 1992, 8(4): 775-792.
[87] Gertler, J., Li, W., Huang, Y., McAvoy, T. Islation enhanced principal component analysis. AIChE Journal, 1999, 45(2): 323-334.
[88]王海清.工业过程监控:基于小波和统计学的方法[博士学位论文].杭州:浙江大学, 2000.
[89] Dunia, R., Qin, S.J. Subspace approach to multidimensional fault identification and reconstruction. AIChE Journal, 1998, 44(8): 1813-1831.
[90] Yue, H.H., Qin, S.J. Reconstruction-based fault identification using a combined index. Industrial and Engineering Chemical Research, 2001, 40(20): 4403-4414.
[91]王海清,宋执环,王慧. PCA过程检测方法的故障检测行为分析.化工学报, 2002, 53(3): 297-301.
[92] Raich, A., Cinar, A. Diagnosis of process disturbances by statistical distance and angle measures. Computers and Chemical Engineering, 1997, 21(6): 661-673.
[93] Zhang, J., Martin, E.B., Morris, A.J. Fault detection and diagnosis using multivariate statistical techniques. Chemical Engineering Research and Design, 1996, 74(1): 89-96.
[94] Johnson, J.E., Wichern, D.W. Applied multivariate statistical analysis. 3rded. New Jersey: Prentice- hall, 1992.
[95] Kano, M., Hasebe, S., Hashimoto, I. A new multivariate statistical process monitoring method using principal component analysis. Computers and Chemical Engineering, 2001, 25(7-8): 1103-1113.
[96]郭明,王树青.基于特征子空间的系统性能监控与工况识别.化工学报, 2004, 55(1): 151-154.
[97] Guo, M., Xie, L., Wang, S.Q. Research on an integrated ICA-SVM based framework for fault diagnosis. IEEE International Conference on Systems, Man & Cybernetics, Washington, USA, 2003, 2710-2715.
[98] Chu, Y., Qin, S.J., Han, C. Fault detection and operation mode identification based on pattern classification with variable selection. Industrial&Engineering Chemical Research, 2004, 43(7): 1701-1710.
[99] Joseph, B., Brosilow, C.B. Inferential control of process. AIChE Journal, 1978, 24(3): 485-509.
[100] Sarkar, P., Gupta, S.K. Steady state simulation of continuous flow stirred tank slurry propylenepolymerization reactors. Polymer Engineering and Science, 1992, 32(11): 732-742.
[101] Sarkar, P., Gupta, S.K. Dynamic simulation of propylene polymerization in continuous flow stirred tank reactors. Polymer Engineering & Science, 1993, 33(6): 368-374.
[102] McAuley, K.B., MacGregor, J.F. Online inference of polymer properties in an industrial polyethylene reactor. AIChE Journal, 1991, 37(6): 825-835.
[103] Sato, C., Ohtani, T., Nishitani, H. Modeling simulation and nonlinear control of a gas-phase polymerization process. Computers and Chemical Engineering, 2000, 24(2-7): 945-951.
[104] Bettoni, A., Bravi, M., Chianese, A. Inferential control of a side stream distillation column. Computers and Chemical Engineering, 2000, 23(11):1737-1744.
[105] Amirthalingam, R., Lee, J.H. Subspace identification based on inferential control applied to a continuous pulp digester. Journal of Process Control, 1999, 9(5): 397-406.
[106] Lang, L., Gillis, E.D. Nonlinear observers for distillation columns. Computers and Chemical Engineering, 1990, 14(11): 1297-1301.
[107] Gudi, R.D., Shah, S., Gray, M. Adaptive multivariate state and parameter estimation strategies with application to a bioreactor. AIChE Journal, 1995, 41(11): 2451-2464.
[108] Tham, M.T., Montague, G. A., Morris, et al. Soft sensors for process monitoring and inferential control. Journal of Process Control, 1991, 1(1): 3-14.
[109] Kreisselmeier, G. Adaptive observers with exponential convergence. IEEE Transactions on Automatic Control, 1977, 22(1): 2-8.
[110] Guilandoust, M.T., Morris, A.J., Tham, M.T. Adaptive inferential control. IEE Proceedings D, 1987, 134(3): 171-179.
[111] Quintero-Marnol, E., Luyben, W. L., Georgakis,C. Application of extended Luenberger observer to the control of multicomonent batch distillation. Industrial&Engineering Chemical Research, 1991, 30(8): 1870-1880.
[112] Weber, R., Brosilow, C.B. The use of secondary measurement to improve control. AIChE Journal, 1972, 18(3): 614-623.
[113] Guilandoust, M.T., Morris, A.J., Tham, M.T. An adaptive estimation algorithm for inferential control. Industrial&Engineering Chemical Research, 1988, 27(9): 1658-1664.
[114] Tham M.J., Morris, A.J., Montague, G.A. Soft-sensing a solution to the problem of measurement delays. Chemical Engineering Research and Design, 1989, 67(6): 547-554.
[115]梁旻,熊智华,周强,等. MTBE反应器软测量系统与控制.化工自动化及仪表,1999,26(6): 23-27.
[116] Kresta, J.V., Marlin, T. E., MacGregor, J. F. Development of inferential process models using PLS. Computers and Chemical Engineering, 1994, 18(7): 597–611.
[117] Wold, S., Kettaneh-wold, N., Skagerberg, B. Nonlinear PLS modeling. Chemometrics and Intelligent Laboratory Systems, 1989, 7(1-2): 53-65.
[118] Mejdell, T., Skogestad, S. Estimation of distillation composition from multiple temperature measurements using partial-least-squares regression. Industrial &Engineering Chemical Research, 1991, 30(12): 2543-2555.
[119] Mejdell, T., Skogestad, S. Output estiamtion using multiple secondary measurements. High- Purity Distillation. AIChE Journal, 1993, 39(10): 1641-1653.
[120] Qin, S.J., Yue, H.Y., Dunis, R. Self-validating inferential sensors with application to air emission monitoring. Industrial&Engineering Chemical Research, 1997, 36(5):1675-1685.
[121] Skageberg, B., MacGrego,r J.F., Kiparissides, C. Multivariate data analysis applied to low- density polyethylene reactors. Chemometrics and Intelligent Laboratory Systems, 1992, 14(1- 3): 341-356.
[122] Baffi, F.G., Martin, E.B., Morris, A.J. Nonlinear dynamic projection to latent structures modeling. Chemometrics and Intelligent Laboratory Systems, 2000, 52(1): 5-22.
[123]罗建旭.软测量若干关键技术的研究及其在石油化学工业过程中的应用[博士学位论文].上海:上海交通大学, 2004.
[124] Yan, W.W., Shao, H.H., Wang, X.F. Soft sensing modeling based on support vector machine and Bayesian model selection. Computers and Chemical Engineering, 2004, 28(8): 1489-1498.
[125] Nomikos, P., MacGregor, J.F. Multivariate SPC charts monitoring batch processes. Technometrics, 1995, 37(1): 41-59.
[126] Wise, B.M., Gallagher, N.B. The process chemometrics approach to process monitoring and fault detection. Journal of Process Control, 1996, 6(6): 329-348.
[127] Malthouse, E.C., Mah, R.S.H., Tamhane, A.C. Some theoretical results on nonlinear principal component analysis. American Control Conference, 1995, Seattle, USA, 744-748.
[128] Jia, F., Martin, E.B., Morris, A.J. Nonlinear principal component analysis for process fault detection. Computers and Chemical Engineering (Suppl.), 1988, 22: S581- S854.
[129] Xie, L, Zhang, Q.L., Wang, S.H.. Linear neural networks pruning techniques and their applications in process monitoring. IEEE International Conference on Systems, Man &Cybernetics, Washington, USA, 2003.
[130] Sch?lkoph, B., Smola, A., Müller, K.-R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1998, 10(5): 1299-1399.
[131] Sorsa, T., Koivo, H.N. Application of artificial neural networks in process fault diagnosis. Automatica, 1993, 29(4): 843-849.
[132]郭明,谢磊,何宁,等.一种基于ICA-SVM的故障诊断方法.中南工业大学学报(自然科学版), 2003, 34(4): 447-450.
[133] Zhao, X, Wen X.-J, Shao, H.H. Combination method of principal component analysis and support vector machine for on-line process monitoring and fault diagnosis. Journal of Donghua University. 2006, 23(1): 53-58.
[134] Krzanowski, W. J. Between-groups comparison of principal components. Journal of American Statistical Association, 1979, 74(367), 703-707.
[135] Johannesmeyer, M.C., Singhai, A., Seborg, D.E. Pattern matching in historical data. AIChE Journal, 2002, 48(9): 2022-2038.
[136] Singhai, A., Seborg, D. E. Clustering of multivariate time-series data. Proceedings of American Control Conference, 2002, 3931-3936.
[137] Singhai, A., Seborg, D. E. Pattern matching in multivariate time series databases using a window-moving approach. Industrial&Engineering Chemistry Research, 2002, 41(16): 3822- 3838.
[138] Singhai, A., Seborg, D.E. Data compression issues with pattern matching in historical data. Proceedings of American Control Conference, 2003, 3696-3701.
[139] Ge, Z.Q., Song, Z.H. Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors. Industrial&Engineering Chemistry Research, 2007, 46(7): 2054-2063.
[140]李巍华.基于核方法的机械故障特征提取与分类技术研究[博士学位论文].武汉:华中科技大学, 2003.
[141] Vapnik, V. An overview of statistical learning theory. IEEE Transactions on Neural Network, 1999, 10(5): 988-999.
[142] Genton, M.G. Classes of Kernels for Machine Learning: a statistical perspective. Journal of Machine Learning Research, 2001, 2(12): 299-312.
[143]阎威武.支持向量机理论、方法和应用研究[博士学位论文].上海:上海交通大学, 2004.
[144]王华忠,俞金寿.基于混合核函数PCR方法的工业过程软测量建模.化工自动化及仪表, 2005, 32(2): 23-25.
[145] Jackson, J.E., Mudhalkar, G.S. Control procedures for residuals associated with principal conponent analysis. Technometrics, 1979, 21(3): 341-349.
[146] Mardia, K.V., Kent, J.T., Bibby, J.M. Multivariate Analysis. London: Academic, 1979.
[147] Downs, J.H., Vogel, E.F. A plant-wide industrial process control problem. Computers Chemical Engineering, 1993, 17(3): 245-255.
[148] Ricker, N.L. Tennessee Eastman challenge archieve. Available at http://depts.washington.edu /control/LARRY/TE/download.html, 1999.
[149]杨竹青,李勇,胡德文.独立成分分析方法综述.自动化学报, 2002, 28(5): 762-772.
[150] Jutten, C, Herault, J. Independent component analysis. Proceedings of European Signal Processing Conference, 1988, 287-314.
[151]何宁.基于ICA-PCA方法的流程工业过程监控与故障诊断研究[博士学位论文].杭州:浙江大学, 2004.
[152] Yang, H.H, Amari, S.-I. Adaptive online learning algorithms for blind separation: maximum entropy and minimum mutual information. Neural Networks, 1997, 9(67): 1457-1481.
[153]杨福生,洪波.独立分量分离的原理与应用.清华大学出版社,北京,2006.
[154] Cardoso, J.-F. Blind signal processing: statistical principles. Proceeds of IEEE, 1998, 86(10): 2009-2025.
[155] Bell, A.J., Sejnowski, A.J. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 1995, 7(6): 1129-1159.
[156] Obradovic, D., Deco, G. Information maximization and independent component analysis: Is there a difference? Neural Computation, 1998, 10(8): 2085-2101.
[157] Lee, T.-W., Girolami, M., Bell, A.J., et al. A unifying information-theoretic framework for independent component analysis. International Journal of Computer and Mathematics with Application, 2000, 39(11): 1-12.
[158] Cardoso, J.-F. Higher order contrasts for independent component analysis. Neural Computation, 1999, 11(1): 157-192.
[159] Cardoso, J.-F. Information and maximum likelihood for blind source separation. IEEE Transactions on Signal Processing Letters, 1997, 4(4): 112-114.
[160] Yang, J., Gao, X., Zhang, D., et al. Kernel ICA: An alternative formulation and its application to face recognition. Pattern Recognition, 2005, 38(10):1784-1787.
[161] Bach, F.R., Jordan, M. I. Kernel independent component analysis. Journal of Machine Learning, 2002, 3(1):1-48.
[162] Lee, J.M., Qin, S.J., Lee, I.B. Fault detection of non-linear processes using kernel independentcomponent analysis. Canadian Journal of Chemical Engineering, 2007, 85(4): 526-536.
[163] Zhang, Y., Qin, S.J. Fault detection of nonlinear processes using multiway kernel independent component analysis. Industrial & Engineering Chemistry Research, 46(23): 7780-7787.
[164] Amari, S. L., Cichocki, A., Yang, H. A new learning algorithm for blind source separation. Advances in Neural Information Processing Systems, 1996, 8: 757-763.
[165] Hyv?rinen, A., Oja, E. A fast fixed-point algorithm for independent component analysis. Neural Computation, 1997, 9(7): 1483-1492.
[166] Tracy, N.D., Young, J.C., Mason, R.L. Multivariate control charts for individual observations. Journal of Quality Technology, 1992, 24: 88-95.
[167] Wand, M.P., Jones, M.C. Kernel smoothing. UK: Chapman & Hall, 1995.
[168] McFarlane, R.C., Reineman, R.C., Bartee, J.F., et al. Dynamic simulator for a model IV fluid catalytic cracking unit. Computers and Chemical Engineering, 1993, 17(3): 275-300
[169] Lu, N.Y., Gao, F.R., Wang, F.L. Sub-PCA modeling and on-line monitoring strategy for batch processes. AIChE Journal, 2004, 50(1): 255-259.
[170] Lee, J.M., Yoo, C.K., Lee, I.B. On-line batch process monitoring using a consecutively updated multiway principal component analysis model. Computers and Chemical Engineering, 2003, 27(12): 1903-1912.
[171]赵旭,阎威武,邵惠鹤.基于核Fisher判别分析方法的非线性统计过程监控与故障诊断.化工学报, 2007, 58(4): 951-956.
[172] Mika, S., R?tsch, G., Weston, J. Fisher discriminant analysis with kernels. Nerual Networks for Signal Processing IX, 1999, 41-48.
[173] Sch?lkopf, B., Mika, S., Burges, C. Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks, 1999, 10 (5): 1000-1016.
[174] Sch?lkopf, B., Platt, J.C., Smola, A.J. Kernel Method for Percentile Feature Extraction. Technical report, MSR-TR-2000-22. Microsoft Research, 2000.
[175] Vapnik, V.N. Statistical Learning Theory. New York: John Wiley&Sons, 1998.
[176] Smola, A., Mangasarian, O. L., Sch?lkopf, B. Sparse Kernel Feature Analysis. Technical Report 99-04 .University of Wisconsin Madison, 1999.
[177] Birol, G., Undey, C., Cinar, A. A modular simulation package for fed-batch fermentation: penicillin production. Computer and Chemical Engineering, 2002, 26(11): 1553-1565.
[178] Walczak, B, Massart, D.L. Dealing with missing data: PartΙ. Chemometrics and Intelligent Laboratory System, 2001, 58(1): 15-27.
[179] Walczak, B, Massart, D.L. Dealing with missing data: Part II. Chemometrics and Intelligent Laboratory System, 2001, 58(1): 29-42.
[180] Kim, D.S., Yoo, C.K., Kim,Y.I., et al. Calibration, prediction and process monitoring model based on factor analysis for incomplete process data. Journal of Chemical Engineering of Japan, 2005, 38(12):1025-1034.
[181] Ignova, M., Glassey, J., Ward, A.C., et al. Multivariate statistical methods in bioprocess fault detection and performance forecasting. Transactions of the Institute of Measurement and Control 1999; 19(5): 271-279.
[182] Ignova, M., Montague, G.A., Ward, A.C., et al. Fermentation seed quality analysis with self- organising neural networks. Biotechnology and Bioengineering 1999, 64(1): 82-91.
[183] Piovoso, M.J., Kosanovch, K.A. Application of multivariate statistical methods to processmonitoring and controller design. International Journal of Control, 1994, 59(3): 743-756.
[184]李春富.基于数据的软测量建模方法及其应用研究[博士学位论文].北京:清华大学, 2004.
[185] Zhao, Z., Xia, X., Wang, J., et al. Nonlinear dynamic matrix control based on multiple operating models. Journal of Process Control, 2003, 13(1): 41-56.
[186] Helland, K., Berntsen, H.E, Borgen, O.S., et al. Recursive algorithm for partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 1992, 14(1-3): 129-137.
[187] Qin, S.J. Recursive PLS algorithms for adaptive data modeling. Computers and Chemical Engineering, 22(4-5): 503-514.
[188] Dayal, B.S., MacGregor, J.F. Recursive exponentially weighted PLS and its application to adaptive control and prediction. Journal of Process Control, 1997, 7(3): 169-179.
[189] Frank, I. E. A nonlinear PLS model. Chemometrics and Intelligent Laboratory Systems, 1990, 8(2): 109-119.
[190] Wold, S. Non-linear partial least squares modeling II spline inner function. Chemometrics and Intelligent Laboratory Systems, 1992, 14(1-3): 71-84.
[191] Li, T., Mei, H., Cong, P. Combining nonlinear PLS with the numeric generic algorithm for QSAR. Chemometrics and Intelligent Laboratory Systems, 1999, 45(1-2): 177-184.
[192] Rosipal, R., Trejo, L.J. Kernel partial least squares regression in reproducing kernel Hilbert space. Journal of Machine Learning Research, 2001, 2(6): 97-123.
[193] Haykin, S. Neural Network. Prentice Hall: Englewood Cliffs, NJ, 1999.
[194] Lee, D.S., Lee, M.W., Woo, S.H., et al. Multivariate online monitoring of a full-scale biological anaerobic filter process using kernel-based algorithms. Industrial&Engineering Chemistry Research, 2006, 45(12): 4335-4344.
[195] Rosipal, R., Trejo, L.J., Matthews, B. Kernel PLS-SVC for linear and nonlinear classification. Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC, 2003, 640-647.
[196] Kim, K., Lee, J.M., Lee, I.B. A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction. Chemonetrics and Intelligent Laboratory Systems, 2005, 79(1-2): 22-30.
[197]王华忠.核函数方法及其在过程建模与故障诊断中的应用研究[博士学位论文].上海:华东理工大学, 2004.
[198] Wold, H. Nonlinear estimation by iterative least squares procedures. Research Papers in Statistics, 1966, Wiley, New York.
[199]宋凯.基于PLS的统计质量监控研究与应用[博士学位论文].杭州:浙江大学, 2005.
[200] Hoskuldsson, A. PLS regression methods. Journal of Chemometrics, 1988, 2(3): 211-228.
[201]袁永根,李华生.过程系统测量数据校正技术,中国石化出版社, 1996.
[202] Wisnewski, P.A, Doyle, F.J., Primus, C.J. Measurement selection issues for the model predictive control of a Kamyr digester, Control Systems '96, Halifax, Canada, 1996, 153-156.
[203]翁玉凤,邵惠鹤,李宏阳,等.应用离散Karhunen-Loeve法选择丙烯浓度的辅助变量.上海交通大学学报, 1999, 33(4): 439-441.
[204] Wilson, D.J.H., Irwin, G.W., Lightdoby, G. RBF principal manifolds for process monitoring. IEEE Transactions on Neural Networks, 1999, 10(6): 1424-1434.
[205] Zhao, S.J., Zhang, J., Xu, Y.M., et al. Nonlinear projection to latent structures method and itsapplications. Industrial&Engineering Chemistry Research, 2006, 45(11): 3843-3852.
[206] Mejdell, T., Skogestad, S. Composition estimator in a pilot-plant distillation column using multiple temperature, Industrial & Engineering Chemistry Research, 1991, 30(12): 2555-2564.
[207] Park, S., Han, C. A nonlinear soft sensor based on multivariate smoothing procedure for quality estimation in distillation columns. Computers and Chemical Engineering, 2000, 24 (2-7): 871- 877.
[208] Zhang, X., Yan, W., Shao, H. Nonlinear multivariate quality estimation and predication based on kernel partial least squares. Industrial &Engineering Chemistry Research, 2008, 47(4): 1120 -1131.
[209] Mah, R.S.H., Tamhane, A.C. Detection of gross errors in process data. AIChE Journal, 1982, 28(5): 828-830.
[210] Tamhane, A.C., Mah, R.S.H. Data reconciliation and gross error detection in chemical process networks. Technometrics, 1985, 27(4): 409-422.
[211] Narasimhan, S., Mah, R.S.H. Generalized likelihood ratios for gross error identification. AIChE Journal, 1987, 33(9): 1514-1521.
[212]杨友麟,滕荣波.过程工业测量数据中过失误差的侦破与校正,化工学报, 1996, 23(6): 248-253.
[213] Cheng, Y.Q., Zhuang, Y.M., Yang, J.Y. Optimal Fisher discriminant analysis using the rank decomposition. Pattern Recognition, 1992, 25(1): 101-111.
[214] Gibass, M., Mackay, D.J.C. Efficient implementation of Gaussian process. Technical report, Department of Physics, Cavendish Laboratory, Cambridge University, UK, 1997.
[215] He, Q.P., Qin, S.J. A new fault diagnosis method using fault directions in fisher discriminant analysis. AIChE Journal, 2005, 51(2): 555-571.