智能多故障识别方法在过程监控中的应用研究
摘要
随着科技的发展,工业过程的复杂性日益提高,为了保证生产过程的安全性、可靠性,提高产品质量,减少生产成本,各种设计方案、控制策略、优化算法层出不穷。过程监控是个系统工程,需要紧密结合自动控制、人工智能、计算机科学、模式识别以及系统工程的相关学科知识,从而提供高控制系统的可靠性和安全性。本文主要针对流程工业中的过程监控问题,提出了几种智能多故障识别诊断方法。主要工作有如下几点。
主元分析方法通常与Hotelling T2和平方误差预测(SPE)统计量方法相结合,来解决线性、类别较少的故障检测和分类问题。本文首先运用三类鸢尾花数据作为实验样本,将选取某类作为基准样本,训练出主元模型,运用Hotelling T2和Q统计量方法对其余两类进行检测,从而判断是否是基准样本的同类。同时将其他两类样本投影到基准样本的得分空间,从二维主元分布图就可以明显看出每类样本的分布,从而达到识别的效果。本文第三章提出了一种PCA_SVM的多故障分类方法,运用传统PCA方法对故障数据进行降维,并将所有故障数据投影到正常工况样本的PCA主元空间中,由正常数据样本计算出T2统计量的阈值,根据Hotelling T2统计原理,对所有故障数据进行检测,将检测到的故障样本通过SVM的多分类方法进行故障分类。通过TE过程仿真平台的实验表明,PCA_SVM方法与PCA_KNN、C_SVM方法相比较,算法简单,容易实现,计算速度较快,同时可以达到较高的多分类准确率。此外,本文还采用了流形学习方法中的局部切空间整合算法(LTSA),将其作为多类故障数据的降维工具,并提出一种集成的支持向量机分类(CSVM)思想,在多分类过程中,对每个2分类器的核函数进行选择,择优选取分类效果较好的核函数,从而建立一个集成分类器,能够对新进的样本进行准确分类。将LTSA和集成SVM方法进行结合,运用LTSA对数据进行降维处理,然后把数据输入到SVM的不同核函数的二分类器中进行训练,并通过测试样本进行实验测试,实验表明,LTSA CSVM跟其他的支持向量机分类方法比较具有一定的优越性。
As the development of science and technology, industry process is becoming more and more complicated. In order to ensure process safety and reliability, decrease the production costs and improve products' quality, related researchers have developed many kinds of design program, control strategy and optimization algorithm. Process monitoring is a systematic engineering, it is integrated with automation control, artificial intelligent, computer science, pattern recognition and system engineering knowledge. This paper is focused on industry process monitoring, especially about multi-class faults recognition. Main research work is presented as follows:
Principle component analysis (PCA) is usually combines with Hotelling T2 and SPE statistical methods to solve fault detection and classification problem with linear and little sample label. Fisher data was used in the second section. Train a PCA model with one class of the sample data; calculate the T2 and SPE control limits of the train samples, all the test samples' T2 and SPE value would be compared with control limits to decide whether it is abnormal data or not. Meanwhile, the other two class data was projected to the principle space. It can be obviously discovered from the 2-dimension distribution plot that different class is scattered in different space.
In the third part of this paper, PCA_SVM is presented as a multi-label recognition method. Traditional PCA is used as a dimension decrease tool, then all fault data is projected into the normal PCA space, detect the new coming data with Hotelling T2. All the detected data would be transport to the SVM classifier to judge which class the sample belong to. From the experiment based on the TE data, it can be concluded that PCA_SVM method is simple, fast computation and higher classification accuracy.
Manifold learning method is introduced in the last section. Local tangent space alignment algorithm (LTSA) which is one of the manifold learning method, imported as a dimension reduction method. A combined support vector machine (CSVM) classification is created in this part. During the multi-labels SVM classification process, each lvsl classifier will choose an appropriate kernel function Instead of just one kernel function for all the classifiers. After experiment test, the LTSA_CSVM shows some advantages in the multi-label classification.
主元分析方法通常与Hotelling T2和平方误差预测(SPE)统计量方法相结合,来解决线性、类别较少的故障检测和分类问题。本文首先运用三类鸢尾花数据作为实验样本,将选取某类作为基准样本,训练出主元模型,运用Hotelling T2和Q统计量方法对其余两类进行检测,从而判断是否是基准样本的同类。同时将其他两类样本投影到基准样本的得分空间,从二维主元分布图就可以明显看出每类样本的分布,从而达到识别的效果。本文第三章提出了一种PCA_SVM的多故障分类方法,运用传统PCA方法对故障数据进行降维,并将所有故障数据投影到正常工况样本的PCA主元空间中,由正常数据样本计算出T2统计量的阈值,根据Hotelling T2统计原理,对所有故障数据进行检测,将检测到的故障样本通过SVM的多分类方法进行故障分类。通过TE过程仿真平台的实验表明,PCA_SVM方法与PCA_KNN、C_SVM方法相比较,算法简单,容易实现,计算速度较快,同时可以达到较高的多分类准确率。此外,本文还采用了流形学习方法中的局部切空间整合算法(LTSA),将其作为多类故障数据的降维工具,并提出一种集成的支持向量机分类(CSVM)思想,在多分类过程中,对每个2分类器的核函数进行选择,择优选取分类效果较好的核函数,从而建立一个集成分类器,能够对新进的样本进行准确分类。将LTSA和集成SVM方法进行结合,运用LTSA对数据进行降维处理,然后把数据输入到SVM的不同核函数的二分类器中进行训练,并通过测试样本进行实验测试,实验表明,LTSA CSVM跟其他的支持向量机分类方法比较具有一定的优越性。
As the development of science and technology, industry process is becoming more and more complicated. In order to ensure process safety and reliability, decrease the production costs and improve products' quality, related researchers have developed many kinds of design program, control strategy and optimization algorithm. Process monitoring is a systematic engineering, it is integrated with automation control, artificial intelligent, computer science, pattern recognition and system engineering knowledge. This paper is focused on industry process monitoring, especially about multi-class faults recognition. Main research work is presented as follows:
Principle component analysis (PCA) is usually combines with Hotelling T2 and SPE statistical methods to solve fault detection and classification problem with linear and little sample label. Fisher data was used in the second section. Train a PCA model with one class of the sample data; calculate the T2 and SPE control limits of the train samples, all the test samples' T2 and SPE value would be compared with control limits to decide whether it is abnormal data or not. Meanwhile, the other two class data was projected to the principle space. It can be obviously discovered from the 2-dimension distribution plot that different class is scattered in different space.
In the third part of this paper, PCA_SVM is presented as a multi-label recognition method. Traditional PCA is used as a dimension decrease tool, then all fault data is projected into the normal PCA space, detect the new coming data with Hotelling T2. All the detected data would be transport to the SVM classifier to judge which class the sample belong to. From the experiment based on the TE data, it can be concluded that PCA_SVM method is simple, fast computation and higher classification accuracy.
Manifold learning method is introduced in the last section. Local tangent space alignment algorithm (LTSA) which is one of the manifold learning method, imported as a dimension reduction method. A combined support vector machine (CSVM) classification is created in this part. During the multi-labels SVM classification process, each lvsl classifier will choose an appropriate kernel function Instead of just one kernel function for all the classifiers. After experiment test, the LTSA_CSVM shows some advantages in the multi-label classification.
引文
[1]T. T. Shannon. "Improving monitoring and diagnosis for process control using independent component analysis".2007:2-5.
[2]W. L. Luyben, B.D, Tyreus, and M.L. Luyben. "Plant wide process control" McGraw-Hill.1998.
[3]W. L. Luyben. "Process modeling, simulation and control for chemical engineers" McGraw-Hill.1989.
[4]王海清.”工业过程监测：基于小波和统计学的方法”.2000.
[5]V. Venkatasubramanian, Rengaswamy, R., Kavuri, S.N., Yin, K.,. "A review of process fault detection and diagnosis (Part III)". Computer Chemistry and Engineering. 2003,27:327-346.
[6]J. Calandranis, Stephanopoulos, G, Nunokawa, S. "On-line performance monitoring and diagnosis". Chemical Engineering P rogress.1990,86:60-68.
[7]P. A. T. Kassidas A., J.F.MacGregor. "Off-line diagnosis of teterministic fault in continuous dynamic multivariable processes using speech recognition methods". Process Control.1998,8 (5-6):381-393.
[8]M. A. K. Leonard J.A. "Diagnosing dynamic fault using modular neural nets". IEEE Experts.1993,(4):44-53.
[9]A. D. Y. Lu M., H.Li, T.Wada. "Application driven approach for the development of a data model standard for process plant operation". Computer and Chemical Engineering. 2000,20 (2-7):463-470.
[10]张洪钱,闻新,周露.”控制系统的故障诊断和容错控制”.机械工业出版社.1998.
[11]K. P. L. J. H. "Diagnostic tools for multivariable model-based control systems [J]". Ind Eng Chem Res.1997,36:2725-2738.
[12]蒋浩天等(著).“工业系统的故障检测与诊断”.机械工业出版社.2003.9.
[13]A. C. Anne Raich. "Statistical process monitoring and disturbance diagnosis in multivariable continuous processes". AIChE Journal.1996,42 (4):995-1009.
[14]胡峰,孙国基.“过程监控技术及其应阁[M]”.北京：国防工业出版社.2001.
[15]J. J. Gertler. "Fault detection and diagnosis in engineering systems". Marcel Dekker,New York.1998.
[16]I. R. " Fault-detection and fault-diagnosis method An introduction [J]". Control Englishing Practice.1997,5:639-652.
[17]马君,原忠虎,马琳伟.”流程工业过程监测的现状及未来发展趋势”.沈阳大学学报.2007,19：36-41.
[18]H. S. Xiang Xie, and Wen Yang. "Review of Multivariate Statistical Process Monitoring". Proceedings of the 8th World Congress on Intelligent Control and Automation 2010, China.
[19]赵旭,阎威武,邵惠鹤.“基于多变量统计方法的产品质量控制”.上海交通大学学报.2007,41(1)：126-130.
[20]王海清,宋执环,王慧.“PCA过程监测方法的故障检测行为分析”.化工学报.2001,53(297-301).
[21]O.A. "PLS regression methods[J]". Journal of Chemometrics.1988,2:211-228. 22] G. P. E. K. "The start and early history of chemometrics:Selected interviews. Part 1 [J]" Journal of Chemometrics.1990,4:337-354. [23] G. P. K. B. R. "Partial least squares regression:A tutorial [J]". Analytica Chimica Acta. 1986,185 (1):1-17.
[24]唐凯,刘瑞兰等.“基于多元统计过程控制的故障识别方法[J]”.浙江大学学报：工学版.2005,5：663-667.
[25]P. Nomikos, MacGregor, John F. "Monitoring batch processes using multiway principal component analysis". AIChE Journal 1994,40:1361-1373.
[26]M. A. Kramer. "Autoassociative neural networks". Computers & Chemical Engineering. 1992,16 (4):313-328.
[27]D. a. T. J. M. Dong. "Nonlinear principal component analysis-Based on principal curves and neural networks". Computers & Chemical Engineering.1996,20 (1):65-78.
[28]S. G. Kulkarni. "Modeling and monitoring of batch processes using principal component analysis (PCA) assisted generalized regression neural networks (GRNN)". Biochemical Engineering Journal.2004,18 (3):193-210.
[29]B. Scholkopf. "Input space versus feature space in kernel-based methods". IEEE transactions on neural networks.1999,10 (5):1000-1017.
[30]J.-M. Lee, et al. "Fault detection and diagnosis based on modified independent component analysis". AIChE Journal.2006,52 (10):3501-3514.
[31]J. M. Lee, S.J. Qin and I.B. Lee. "Fault detection of non-linear processes using kernel independent component analysis". Canadian journal of chemical engineering.2007,85 (5):526-536.
[32]C. K. Yoo. "On-line monitoring of batch processes using multiway independent component analysis". Chemometrics and Intelligent Laboratory Systems.2004,71 (2):151-163.
[33]X. Q. Liu, et al. "2008Statistical-based monitoring of multivariate non-Gaussian systems". AIChE Journal.2008,54 (9):2379-2391.
[34]X. M. Tian, et al. "Multiway kernel independent component analysis based on feature samples for batch process monitoring". Neurocomputing.2009,72 (7-9):1584-1596.
[35]R. I. a. P. Ball. "Trends in the application of model based fault detection and diagnosis of technical processes". Process of the 13th IFAC World Congress.1996,N (1-12):IEEE Press, Piscataway.
[36]P. R. J. Chen J. "Robust Model-Based Fault Diagnosis for Dynamic System". Boston: Kluwer Academic Publishers.1999.
[37]D. S. Wang H. "Actuator fault diagnosis:an adaptive observer based approach". IEEE Transactions on Automatic Control.1996,41 (7):1073-1078.
[38]H. Z. J. Wang H, Daley S. "On the use of adaptive updating rules for actuators and sensor fault diagnosis". Automatica.1997,32 (2):217-225.
[39]D. J.-L. Chai Tian-You, Wang Hong, Su Chun-Yi. "Hybrid intelligent optimal control method for operation of complex industrial processes". Acta Automatica Sinica. 2008,34 (5):505-515.
[40]e. a. B. C. Juricek. "Process control applications of subspace and regression-based identification and monitoring methods". American Control Conference. Proceedings of the 2005.2005,4:2341-2346.
[41]I.T.Jolliffe. "Principal Component Analysis". Springer Series in Statistics.2002.
[42]C. S. Arnold. "Computer-based process monitoring/fault detection using principal component analysis".2001:15-22.
[43]M. Kano, Hasebe S, Hashimoto I, Ohno H. "A new multivariate statistical process monitoring method using principlal component analysis". Computer and Chemical Engineering.2001,25 (7-8):1103-1113.
[44]M. J. Martin E, Lane S. "Monitoring process manufacturing performance". IEEE Control Systems Magazine.2002,22 (5):26-29.
[45]R. W. H. Harris TJ. "Statistical process control procedures for correlated observations[J]". The Canadian Journal of Chemical Engineering.1991,69:48-57.
[46]R. H. S. Ku Wenfu, Christos Gerogakis. "Disturbance detection and isilation by dynamic principal component analysis[J]". Chemometrics and Intelligent Laboratory Systems. 1995,30:179-196.
[47]L. K. Chen J. "On-line batch process monitoring using dynamic PCA and dynamic PLS models[j]". Chemical Engineering Science.2002,57:63-75.
[48]K. U. Treasure R J, Cooper J E. "Dynamic multivariate statistical process control using subspace idetification[J]". Journal of Process Control.2004,14:279-292.
[49]Z. j. Xie Lei, Wang Shuqing,. "Investigation of Dynamic Multivariate Chemical Process Monitoring[J]". Chinese J,Chem.Eng.2006,559-568 (279-292).
[50]C. Y. Cheng, et al. "Adaptive Kernel Principal Component Analysis (KPCA) for Monitoring Small Disturbances of Nonlinear Processes". Industrial & Engineering Chemistry Research.2010,49 (5):2254-2262.
[51]L. Haiping, et al. "MPCA:Multilinear Principal Component Analysis of Tensor Objects". Neural Networks, IEEE Transactions on.2008,19 (1):18-39.
[52]X. Jie, et al. "Combining KPCA with Sparse SVM for Nonlinear Process Monitoring", in Power and Energy Engineering Conference (APPEEC),2010 Asia-Pacific,2010, pp. 1-4.
[53]B. Libo and W. Xiangdong. "Fault Detection and Diagnosis of Continuous Process Based on Multiblock Principal Component Analysis", in Computer Engineering and Technology,2009. ICCET'09. International Conference on,2009, pp.200-204.
[54]N. A. A. Majid, et al. "Real-time for monitoring aluminium reduction cells using multi-way PCA (MPCA) and dynamic Euclidean distances", in Control and Automation, 2009. ICCA 2009. IEEE International Conference on,2009, pp.454-458.
[55]何菲,杜文莉,钱锋.”基于PCA_SVM的多故障分类方法及其在TE过程中的应用".计算机与应用化学.2010,27(10).
[56]夏睿.”工业过程在线监测技术研究”.2006.
[57]E. L. R. a. R. D. B. L.H. Chiang. "Fault detection and diagnosis in industrial systems". Springer, London.2001.
[58]S. J. Qin. "Statistical process monitoring:basics and beyond". J.Chemometrics. 2003,17:480-502.
[59]R. A. Fisher. "The use of multiple measurements in taxonomic problems". Ann. Eugenics.1936,7:179-188.
[60]Y. M. Z. a. J. Y. Y. Y. Q. Cheng. "Optimal Fisher discriminant analysis using the rank decomposition". Pattern Recognition.25 (101-111).
[61]G.-h. Gao, et al. "Hybrid Support Vector Machines-Based Multi-fault Classification". Journal of China University of Mining and Technology.2007,17 (2):246-250.
[62]H. Xiaobin, et al. "Class-incremental fisher discriminant analysis with principal component analysis for process monitoring". in Asian Control Conference,2009. ASCC 2009.7th,2009, pp.830-834.
[63]G. Zhiqiang, et al. "Bayesian method for multimode non-Gaussian process monitoring" in Decision and Control,2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on,2009, pp. 4927-4932.
[64]R. Polikar, et al. "An ensemble based data fusion approach for early diagnosis of Alzheimer's disease". Information Fusion.2008,9 (1):83-95.
[65]C. Sumana, et al. "Dynamic kernel scatter-difference-based discriminant analysis for diagnosis of Tennessee Eastman process". in American Control Conference,2009. ACC '09.,2009, pp.3417-3422.
[66]王晓哗.“K-最近邻分类技术的改进算法”.电子与信息学报.2005,27(3).
[67]边肇祺,张学工.”模式识别(第二版)”.2000：292-302.
[68]B. Waske, et al. "Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data". IEEE Transactions on Geoscience and Remote Sensing.2010,48 (7):2880-2889.
[69]I. Monroy, et al. "A semi-supervised approach to fault diagnosis for chemical processes". Computers & Chemical Engineering.2010,34 (5):631-642.
[70]R. Bai and J. Liao. "A Hybrid Documents Classification Based on SVM and Rough Sets", in Advanced Science and Technology,2009. AST'09. International e-Conference on,2009, pp.18-23.
[71]H. Chun-Chin, et al. "Integrating independent component analysis and support vector machine for multivariate process monitoring". Computers & Industrial Engineering. 2010,59 (1):145-156.
[72]E. Gumus, et al. "Evaluation of face recognition techniques using PCA, wavelets and SVM". Expert Systems with Applications.2010,In Press, Corrected Proof.
[73]Z. Hao, A. C.Maire, M.Malik, J., "SVM-KNN:Discriminative Nearest Neighbor Classification for Visual Category Recognition," in Computer Vision and Pattern Recognition,2006 IEEE Computer Society Conference on vol.2, ed,2006, pp.2126-2136.
[74]Z. Min-Ling and Z. Zhi-Hua. "A k-nearest neighbor based algorithm for multi-label classification", in Granular Computing,2005 IEEE International Conference on,2005, pp.718-721 Vol.2.
[75]R. Palaniappan and K. V. R. Ravi. "Improving visual evoked potential feature classification for person recognition using PCA and normalization". Pattern Recognition Letters.2006,27 (7):726-733.
[76]D. R. Vinson, et al. "Studies in plant-wide controllability using the Tennessee Eastman Challenge problem, the case for multivariable control", in American Control Conference, 1995. Proceedings of the,1995, pp.250-254 vol.1.
[77]J. J. Downs and E. F. Vogel. "A plant-wide industrial process control problem". Computers & Chemical Engineering.1993,17 (3):245-255.
[78]T. J. McAvoy and N. Ye. "Base control for the Tennessee Eastman problem". Computers and Chemical Engineering.1994,18 (Compendex):383-413.
[79]E. Tafazzoli and M. Saif. "Application of combined support vector machines in process fault diagnosis". in American Control Conference,2009. ACC'09.,2009, pp.3429-3433.
[80]L. H. Chiang, et al. "Fault diagnosis based on Fisher discriminant analysis and support vector machines". Computers & Chemical Engineering.2004,28 (8):1389-1401.
[81]F. Westad and M. Kermit. "Independent Component Analysis". in Comprehensive Chemometrics, D. B. Stephen, et al., Eds., ed Oxford:Elsevier,2009, pp.227-248.
[82]徐蓉.”流形学习概述”.智能系统学报.2006,1(1).
[83]韦佳.”流形学习与基于流形假设的半监督学习研究”.2009.
[84]蒋全胜.”基于流形学习的机械故障诊断理论与方法研究”.2009.
[85]S. L. ROWEIS S. "Nolinear dimensionality reduction by locally linear embedding[J]". Science.2000,290 (5000):2323-2326.
[86]N. P. BELKIN M. "Laplacian eigenmaps for dimensionality reduction and data representation[J]". Neural Coputation.2003,15 (6):1373-1396.
[87]王守觉.”仿生模式识别(拓扑模式识别)——一种模式识别新模型的理论与应用[J]”.电子学报.2002,30(10)：1417-1420.
[88]Z. H. Y. ZHANG Z Y. "Principal manifolds and nonlinear dimensionality reduction via tangent space alignment[J]". SIAM Journal of Science Computing.2005,26 (1):313-338.
[89]王自强.”流形学习算法综述”.计算机工程与应用.2008,44(35).
[90]W. Min. "Locality pursuit embedding". Pattern Recognition.2004,37 (4).
[91]张少捷,王振雷.”基于LTSA的FS-SVDD方法及其在化工过程监控中的应用”.化工学报.2010,61(8).
[92]L. I. Kuncheva. "Combining Pattern Classifiers:Methods and Algorithms (Kuncheva, L.I.; 2004) [book review]". Neural Networks, IEEE Transactions on.2007,18 (3):964-964.
[93]M. C. Lee, et al. "Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction". Artificial Intelligence in Medicine.2010,50 (1):43-53.
[94]G. Ming, et al. "Research on an integrated ICA-SVM based framework for fault diagnosis". in Systems, Man and Cybernetics,2003. IEEE International Conference on, 2003, pp.2710-2715 vol.3.
[2]W. L. Luyben, B.D, Tyreus, and M.L. Luyben. "Plant wide process control" McGraw-Hill.1998.
[3]W. L. Luyben. "Process modeling, simulation and control for chemical engineers" McGraw-Hill.1989.
[4]王海清.”工业过程监测：基于小波和统计学的方法”.2000.
[5]V. Venkatasubramanian, Rengaswamy, R., Kavuri, S.N., Yin, K.,. "A review of process fault detection and diagnosis (Part III)". Computer Chemistry and Engineering. 2003,27:327-346.
[6]J. Calandranis, Stephanopoulos, G, Nunokawa, S. "On-line performance monitoring and diagnosis". Chemical Engineering P rogress.1990,86:60-68.
[7]P. A. T. Kassidas A., J.F.MacGregor. "Off-line diagnosis of teterministic fault in continuous dynamic multivariable processes using speech recognition methods". Process Control.1998,8 (5-6):381-393.
[8]M. A. K. Leonard J.A. "Diagnosing dynamic fault using modular neural nets". IEEE Experts.1993,(4):44-53.
[9]A. D. Y. Lu M., H.Li, T.Wada. "Application driven approach for the development of a data model standard for process plant operation". Computer and Chemical Engineering. 2000,20 (2-7):463-470.
[10]张洪钱,闻新,周露.”控制系统的故障诊断和容错控制”.机械工业出版社.1998.
[11]K. P. L. J. H. "Diagnostic tools for multivariable model-based control systems [J]". Ind Eng Chem Res.1997,36:2725-2738.
[12]蒋浩天等(著).“工业系统的故障检测与诊断”.机械工业出版社.2003.9.
[13]A. C. Anne Raich. "Statistical process monitoring and disturbance diagnosis in multivariable continuous processes". AIChE Journal.1996,42 (4):995-1009.
[14]胡峰,孙国基.“过程监控技术及其应阁[M]”.北京：国防工业出版社.2001.
[15]J. J. Gertler. "Fault detection and diagnosis in engineering systems". Marcel Dekker,New York.1998.
[16]I. R. " Fault-detection and fault-diagnosis method An introduction [J]". Control Englishing Practice.1997,5:639-652.
[17]马君,原忠虎,马琳伟.”流程工业过程监测的现状及未来发展趋势”.沈阳大学学报.2007,19：36-41.
[18]H. S. Xiang Xie, and Wen Yang. "Review of Multivariate Statistical Process Monitoring". Proceedings of the 8th World Congress on Intelligent Control and Automation 2010, China.
[19]赵旭,阎威武,邵惠鹤.“基于多变量统计方法的产品质量控制”.上海交通大学学报.2007,41(1)：126-130.
[20]王海清,宋执环,王慧.“PCA过程监测方法的故障检测行为分析”.化工学报.2001,53(297-301).
[21]O.A. "PLS regression methods[J]". Journal of Chemometrics.1988,2:211-228. 22] G. P. E. K. "The start and early history of chemometrics:Selected interviews. Part 1 [J]" Journal of Chemometrics.1990,4:337-354. [23] G. P. K. B. R. "Partial least squares regression:A tutorial [J]". Analytica Chimica Acta. 1986,185 (1):1-17.
[24]唐凯,刘瑞兰等.“基于多元统计过程控制的故障识别方法[J]”.浙江大学学报：工学版.2005,5：663-667.
[25]P. Nomikos, MacGregor, John F. "Monitoring batch processes using multiway principal component analysis". AIChE Journal 1994,40:1361-1373.
[26]M. A. Kramer. "Autoassociative neural networks". Computers & Chemical Engineering. 1992,16 (4):313-328.
[27]D. a. T. J. M. Dong. "Nonlinear principal component analysis-Based on principal curves and neural networks". Computers & Chemical Engineering.1996,20 (1):65-78.
[28]S. G. Kulkarni. "Modeling and monitoring of batch processes using principal component analysis (PCA) assisted generalized regression neural networks (GRNN)". Biochemical Engineering Journal.2004,18 (3):193-210.
[29]B. Scholkopf. "Input space versus feature space in kernel-based methods". IEEE transactions on neural networks.1999,10 (5):1000-1017.
[30]J.-M. Lee, et al. "Fault detection and diagnosis based on modified independent component analysis". AIChE Journal.2006,52 (10):3501-3514.
[31]J. M. Lee, S.J. Qin and I.B. Lee. "Fault detection of non-linear processes using kernel independent component analysis". Canadian journal of chemical engineering.2007,85 (5):526-536.
[32]C. K. Yoo. "On-line monitoring of batch processes using multiway independent component analysis". Chemometrics and Intelligent Laboratory Systems.2004,71 (2):151-163.
[33]X. Q. Liu, et al. "2008Statistical-based monitoring of multivariate non-Gaussian systems". AIChE Journal.2008,54 (9):2379-2391.
[34]X. M. Tian, et al. "Multiway kernel independent component analysis based on feature samples for batch process monitoring". Neurocomputing.2009,72 (7-9):1584-1596.
[35]R. I. a. P. Ball. "Trends in the application of model based fault detection and diagnosis of technical processes". Process of the 13th IFAC World Congress.1996,N (1-12):IEEE Press, Piscataway.
[36]P. R. J. Chen J. "Robust Model-Based Fault Diagnosis for Dynamic System". Boston: Kluwer Academic Publishers.1999.
[37]D. S. Wang H. "Actuator fault diagnosis:an adaptive observer based approach". IEEE Transactions on Automatic Control.1996,41 (7):1073-1078.
[38]H. Z. J. Wang H, Daley S. "On the use of adaptive updating rules for actuators and sensor fault diagnosis". Automatica.1997,32 (2):217-225.
[39]D. J.-L. Chai Tian-You, Wang Hong, Su Chun-Yi. "Hybrid intelligent optimal control method for operation of complex industrial processes". Acta Automatica Sinica. 2008,34 (5):505-515.
[40]e. a. B. C. Juricek. "Process control applications of subspace and regression-based identification and monitoring methods". American Control Conference. Proceedings of the 2005.2005,4:2341-2346.
[41]I.T.Jolliffe. "Principal Component Analysis". Springer Series in Statistics.2002.
[42]C. S. Arnold. "Computer-based process monitoring/fault detection using principal component analysis".2001:15-22.
[43]M. Kano, Hasebe S, Hashimoto I, Ohno H. "A new multivariate statistical process monitoring method using principlal component analysis". Computer and Chemical Engineering.2001,25 (7-8):1103-1113.
[44]M. J. Martin E, Lane S. "Monitoring process manufacturing performance". IEEE Control Systems Magazine.2002,22 (5):26-29.
[45]R. W. H. Harris TJ. "Statistical process control procedures for correlated observations[J]". The Canadian Journal of Chemical Engineering.1991,69:48-57.
[46]R. H. S. Ku Wenfu, Christos Gerogakis. "Disturbance detection and isilation by dynamic principal component analysis[J]". Chemometrics and Intelligent Laboratory Systems. 1995,30:179-196.
[47]L. K. Chen J. "On-line batch process monitoring using dynamic PCA and dynamic PLS models[j]". Chemical Engineering Science.2002,57:63-75.
[48]K. U. Treasure R J, Cooper J E. "Dynamic multivariate statistical process control using subspace idetification[J]". Journal of Process Control.2004,14:279-292.
[49]Z. j. Xie Lei, Wang Shuqing,. "Investigation of Dynamic Multivariate Chemical Process Monitoring[J]". Chinese J,Chem.Eng.2006,559-568 (279-292).
[50]C. Y. Cheng, et al. "Adaptive Kernel Principal Component Analysis (KPCA) for Monitoring Small Disturbances of Nonlinear Processes". Industrial & Engineering Chemistry Research.2010,49 (5):2254-2262.
[51]L. Haiping, et al. "MPCA:Multilinear Principal Component Analysis of Tensor Objects". Neural Networks, IEEE Transactions on.2008,19 (1):18-39.
[52]X. Jie, et al. "Combining KPCA with Sparse SVM for Nonlinear Process Monitoring", in Power and Energy Engineering Conference (APPEEC),2010 Asia-Pacific,2010, pp. 1-4.
[53]B. Libo and W. Xiangdong. "Fault Detection and Diagnosis of Continuous Process Based on Multiblock Principal Component Analysis", in Computer Engineering and Technology,2009. ICCET'09. International Conference on,2009, pp.200-204.
[54]N. A. A. Majid, et al. "Real-time for monitoring aluminium reduction cells using multi-way PCA (MPCA) and dynamic Euclidean distances", in Control and Automation, 2009. ICCA 2009. IEEE International Conference on,2009, pp.454-458.
[55]何菲,杜文莉,钱锋.”基于PCA_SVM的多故障分类方法及其在TE过程中的应用".计算机与应用化学.2010,27(10).
[56]夏睿.”工业过程在线监测技术研究”.2006.
[57]E. L. R. a. R. D. B. L.H. Chiang. "Fault detection and diagnosis in industrial systems". Springer, London.2001.
[58]S. J. Qin. "Statistical process monitoring:basics and beyond". J.Chemometrics. 2003,17:480-502.
[59]R. A. Fisher. "The use of multiple measurements in taxonomic problems". Ann. Eugenics.1936,7:179-188.
[60]Y. M. Z. a. J. Y. Y. Y. Q. Cheng. "Optimal Fisher discriminant analysis using the rank decomposition". Pattern Recognition.25 (101-111).
[61]G.-h. Gao, et al. "Hybrid Support Vector Machines-Based Multi-fault Classification". Journal of China University of Mining and Technology.2007,17 (2):246-250.
[62]H. Xiaobin, et al. "Class-incremental fisher discriminant analysis with principal component analysis for process monitoring". in Asian Control Conference,2009. ASCC 2009.7th,2009, pp.830-834.
[63]G. Zhiqiang, et al. "Bayesian method for multimode non-Gaussian process monitoring" in Decision and Control,2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on,2009, pp. 4927-4932.
[64]R. Polikar, et al. "An ensemble based data fusion approach for early diagnosis of Alzheimer's disease". Information Fusion.2008,9 (1):83-95.
[65]C. Sumana, et al. "Dynamic kernel scatter-difference-based discriminant analysis for diagnosis of Tennessee Eastman process". in American Control Conference,2009. ACC '09.,2009, pp.3417-3422.
[66]王晓哗.“K-最近邻分类技术的改进算法”.电子与信息学报.2005,27(3).
[67]边肇祺,张学工.”模式识别(第二版)”.2000：292-302.
[68]B. Waske, et al. "Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data". IEEE Transactions on Geoscience and Remote Sensing.2010,48 (7):2880-2889.
[69]I. Monroy, et al. "A semi-supervised approach to fault diagnosis for chemical processes". Computers & Chemical Engineering.2010,34 (5):631-642.
[70]R. Bai and J. Liao. "A Hybrid Documents Classification Based on SVM and Rough Sets", in Advanced Science and Technology,2009. AST'09. International e-Conference on,2009, pp.18-23.
[71]H. Chun-Chin, et al. "Integrating independent component analysis and support vector machine for multivariate process monitoring". Computers & Industrial Engineering. 2010,59 (1):145-156.
[72]E. Gumus, et al. "Evaluation of face recognition techniques using PCA, wavelets and SVM". Expert Systems with Applications.2010,In Press, Corrected Proof.
[73]Z. Hao, A. C.Maire, M.Malik, J., "SVM-KNN:Discriminative Nearest Neighbor Classification for Visual Category Recognition," in Computer Vision and Pattern Recognition,2006 IEEE Computer Society Conference on vol.2, ed,2006, pp.2126-2136.
[74]Z. Min-Ling and Z. Zhi-Hua. "A k-nearest neighbor based algorithm for multi-label classification", in Granular Computing,2005 IEEE International Conference on,2005, pp.718-721 Vol.2.
[75]R. Palaniappan and K. V. R. Ravi. "Improving visual evoked potential feature classification for person recognition using PCA and normalization". Pattern Recognition Letters.2006,27 (7):726-733.
[76]D. R. Vinson, et al. "Studies in plant-wide controllability using the Tennessee Eastman Challenge problem, the case for multivariable control", in American Control Conference, 1995. Proceedings of the,1995, pp.250-254 vol.1.
[77]J. J. Downs and E. F. Vogel. "A plant-wide industrial process control problem". Computers & Chemical Engineering.1993,17 (3):245-255.
[78]T. J. McAvoy and N. Ye. "Base control for the Tennessee Eastman problem". Computers and Chemical Engineering.1994,18 (Compendex):383-413.
[79]E. Tafazzoli and M. Saif. "Application of combined support vector machines in process fault diagnosis". in American Control Conference,2009. ACC'09.,2009, pp.3429-3433.
[80]L. H. Chiang, et al. "Fault diagnosis based on Fisher discriminant analysis and support vector machines". Computers & Chemical Engineering.2004,28 (8):1389-1401.
[81]F. Westad and M. Kermit. "Independent Component Analysis". in Comprehensive Chemometrics, D. B. Stephen, et al., Eds., ed Oxford:Elsevier,2009, pp.227-248.
[82]徐蓉.”流形学习概述”.智能系统学报.2006,1(1).
[83]韦佳.”流形学习与基于流形假设的半监督学习研究”.2009.
[84]蒋全胜.”基于流形学习的机械故障诊断理论与方法研究”.2009.
[85]S. L. ROWEIS S. "Nolinear dimensionality reduction by locally linear embedding[J]". Science.2000,290 (5000):2323-2326.
[86]N. P. BELKIN M. "Laplacian eigenmaps for dimensionality reduction and data representation[J]". Neural Coputation.2003,15 (6):1373-1396.
[87]王守觉.”仿生模式识别(拓扑模式识别)——一种模式识别新模型的理论与应用[J]”.电子学报.2002,30(10)：1417-1420.
[88]Z. H. Y. ZHANG Z Y. "Principal manifolds and nonlinear dimensionality reduction via tangent space alignment[J]". SIAM Journal of Science Computing.2005,26 (1):313-338.
[89]王自强.”流形学习算法综述”.计算机工程与应用.2008,44(35).
[90]W. Min. "Locality pursuit embedding". Pattern Recognition.2004,37 (4).
[91]张少捷,王振雷.”基于LTSA的FS-SVDD方法及其在化工过程监控中的应用”.化工学报.2010,61(8).
[92]L. I. Kuncheva. "Combining Pattern Classifiers:Methods and Algorithms (Kuncheva, L.I.; 2004) [book review]". Neural Networks, IEEE Transactions on.2007,18 (3):964-964.
[93]M. C. Lee, et al. "Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction". Artificial Intelligence in Medicine.2010,50 (1):43-53.
[94]G. Ming, et al. "Research on an integrated ICA-SVM based framework for fault diagnosis". in Systems, Man and Cybernetics,2003. IEEE International Conference on, 2003, pp.2710-2715 vol.3.