基于核函数和知识的化工过程安全运行智能支持系统研究
|
摘要
流程工业系统庞大,流程复杂,不安全因素众多,异常事故时有发生。因此,安全运行是流程工业生产中的头等大事,而充分运用信息技术,确保生产安全,也一直是企业紧抓不放的重要技术课题。目前,随着DCS和计算机技术的广泛应用,大量蕴涵过程信息的数据被采集和保存,但是,这些数据并没有有效利用,因此,出现“数据丰富,信息匮乏”的现象。另一方面,长期的生产实践积累了大量的经验知识,尤其是非正常工况的处理经验,也没有得到很好运用。因此,如何将这些数据变为有用的信息,从中挖掘出影响过程安全运行的特征信息,进而利用这些信息并结合经验知识,提高流程工业的长期、稳定、安全运行是本文的研究重点。
基于上述动机,本文提出过程安全运行智能支持系统的概念和框架,详细阐述了系统的结构、功能和关键技术。在此基础上,完成系统的开发,并应用到具体的工业流程。本文主要研究内容为:
首先,针对化工过程的生产特点,在研究过程运行状态识别智能算法的基础上,结合过程模型、生产目标和智能技术,提出过程安全运行智能支持系统的理论框架。在讨论和分析系统关键技术的基础上,进而提出其核心技术-一种混合智能故障诊断方法。
其次,提出一种基于分块特征向量选择的改进核主成分分析方法。建立核主成分分析模型必须计算核矩阵K,对于大样本集,K的计算很困难。本文利用分块特征向量选择方法,选择能够描述样本集特征的样本子集,然后用样本子集建立核主成分分析模型。实验表明,相比核主成分分析,二者性能没有明显不同,但该方法的核矩阵维数较少,简化了K的计算复杂度。
第三,研究核特征提取和最小二乘支持向量机在化工过程运行模式分类中的应用。化工过程的数据通常具有高维数、高噪声、强非线性的特点,直接从这些数据中识别过程的运行模式是很困难的。利用核特征提取方法将输入数据进行分解,消除冗余或相关信息,提取数据中的过程特征信息,然后建立最小二乘支持向量机过程模式运行分类模型。利用获取的工业数据,对该方法进行了深入研究。实验结果表明了该方法的有效性。第四,提出一种融合先验知识的模糊最小二乘支持向量机分类方法。针对最小二乘支持向量机对噪声或孤立点敏感的问题,提出一种基于噪声分布模型和样本紧密度的模糊最小二乘支持向量机模型。在模型的训练过程中,通过引入噪声分布模型,融合样本数据的先验知识;为了区分数据和噪声,提出基于样本紧密度的策略。运用该策略以及噪声分布模型,自动生成相应样本数据点的权值,据此调整模糊LSSVM模型的模糊隶属度。实验结果表明该方法具有很好的抗噪声能力和鲁棒性。
第五,提出一种过程故障诊断专家系统的设计方案。该方案着重解决了以下几方面的问题:在知识获取方面,采用故障树、经验知识表格和决策树方法有机结合,解决了专家系统知识获取的“瓶颈”问题;知识校验是知识获取的一个重要步骤,提出一种基于有向图的知识校验方法;知识库分为全局知识库和内存知识库,提出一种内存知识库的知识选择策略;为了消解知识冲突,融合过程知识,提出基于统计和时间序列分析的动态知识冲突消解策略。
第六,基于以上研究,着重从实际应用对系统的要求出发,开发了过程安全运行智能支持系统,并详细描述了系统的结构和主要功能。通过在某石化公司润滑油生产过程的实际应用,验证了所开发系统的有效性和实用性,进而证明了本文提出的过程安全运行系统框架以及为实现该系统所采用的各种技术的实用性。
Characteristics of process engineering are a complex flows and colossal system. There are many unsafe factors. Abnormal situations occur from time to time. Therefore, an operation safe is a most important thing for the process engineering. Using enough information technologies, it can assure that the production process is safe, and which it also is an important technology problem for the process engineering. At the present time, with the extending application of DCS (Distributed Computer System) and computer techniques, a great amount of process data which includes process information can be sampled and collected. But, these data are not employed availably, thus it leads abundant data and poor information. In other hand, an amount of empirical knowledge, especially treating abnormal situations, are accumulated. But, these information are not handled better. Therefore, it is research contents in the paper how to transform these data into informations which can be used, and extract feature information which effects on the operation safety of the process from the process data, and further make enough use of these information and combine the empirical knowledge to support the long, steady, and safe operation for the process engineering.
Based on the motivation, a conception and frame for an intelligent aided system of process safety operation is presented in the paper. The structure, function, and some key techniques for the system are described in detail. The proposed system is developed and applied to a real industrial process.
Firstly, according to the characteristic of chemical engineering, the intelligent aided system for process safety operation is presented based on an intelligent algorithm for identifying an operation state and combined with process models, production objects, and intelligent techniques. A hybrid intelligent method for fault diagnosis is presented based on the discussion and analysis for some key techniques.
Secondly, an improved kernel principal component analysis (KPCA) based on multi-block feature vector selection is presented in the paper. A kernel matrix K need be computed in the process of constructing a model for KPCA. But, for a large sample, it is very difficult to solve the kernel matrix K. Using a method of multi-block feature vector selection, a sub-sample is selected to describe the all sample, then a model for KPCA is trained by the sub-sample. The experiment shows that performance of the proposed method and KPCA are almost equivalent. The dimension of kernel matrix K for the proposed method reduces. Consequently, the computing complexity of K drops.
Thirdly, a classification method combining a kernel-based feature extraction with a least square support vector machines (LSSVM) for an operation mode of process is presented. A process data are characteristic of high dimension, strongly nonlinearity, high noise and small fault samples. Therefore, it is very difficult to directly identifying the process operation mode from these data. Using the kernel-based feature extraction, the inputting data are decomposed, and removed from redundant or related information, and extracted from process feature information. Then, the classification model based on LSSVM is built up for identifying the process operation mode. The real data from an industrial process is used to prove the proposed method. The experiment result shows that the proposed method is effective.
Fourthly, a fuzzy least squares support vector machines classification method incorporated with a prior knowledge on data is presented. To address the drawback which a least squares support vector machines is sensitive to noises or outliers, the least squares support vector machines model combining with the prior knowledge on process data is represented based on a noise distribution model and a strategy based on a sample affinity. Information of noise distribution model for process data is introduced in the training process of the model;The strategy based on the sample affinity is presented to discriminate between data and noises. A fuzzy membership is automatically generated and assigned to each corresponding data point by using the strategy and the noise model. Thus, the fuzzy membership in the LSSVM model can be adjusted. The experiment result shows that the proposed method has better performance and robustness against the noise data.
Fifthly, a fault diagnosis expert system for the chemical process is presented. The following problem is considered in the process of the design for the expert system. In the process of knowledge acquisition, a problem which a knowledge acquisition has always been the bottleneck in developing expert systems is solved by using a method combined a fault tree and an empirical knowledge table with a decision tress; A knowledge verification is an import step for the knowledge acquisition. The knowledge verification is carried out based on a directed graph approach in the paper; A selection strategy of knowledge rules in the memory knowledge base is presented; Incorporating process knowledge, a dynamical strategy of knowledge conflict resolution based on a statistics and a time-series analysis method is introduced for resolving a knowledge conflict.
Sixthly, based on the previous research and a requirement of real application environment, the intelligent aided system for chemical process safety operation is developed. The structure and the main function for the proposed system are described in detail. The proposed system is applied to a lubricating oil process in a petroleum plant. The validity and practicability of the proposed system is proven. Further, the frame of the proposed system as well as some key techniques used to the system can be confirmed with the availability.
基于上述动机,本文提出过程安全运行智能支持系统的概念和框架,详细阐述了系统的结构、功能和关键技术。在此基础上,完成系统的开发,并应用到具体的工业流程。本文主要研究内容为:
首先,针对化工过程的生产特点,在研究过程运行状态识别智能算法的基础上,结合过程模型、生产目标和智能技术,提出过程安全运行智能支持系统的理论框架。在讨论和分析系统关键技术的基础上,进而提出其核心技术-一种混合智能故障诊断方法。
其次,提出一种基于分块特征向量选择的改进核主成分分析方法。建立核主成分分析模型必须计算核矩阵K,对于大样本集,K的计算很困难。本文利用分块特征向量选择方法,选择能够描述样本集特征的样本子集,然后用样本子集建立核主成分分析模型。实验表明,相比核主成分分析,二者性能没有明显不同,但该方法的核矩阵维数较少,简化了K的计算复杂度。
第三,研究核特征提取和最小二乘支持向量机在化工过程运行模式分类中的应用。化工过程的数据通常具有高维数、高噪声、强非线性的特点,直接从这些数据中识别过程的运行模式是很困难的。利用核特征提取方法将输入数据进行分解,消除冗余或相关信息,提取数据中的过程特征信息,然后建立最小二乘支持向量机过程模式运行分类模型。利用获取的工业数据,对该方法进行了深入研究。实验结果表明了该方法的有效性。第四,提出一种融合先验知识的模糊最小二乘支持向量机分类方法。针对最小二乘支持向量机对噪声或孤立点敏感的问题,提出一种基于噪声分布模型和样本紧密度的模糊最小二乘支持向量机模型。在模型的训练过程中,通过引入噪声分布模型,融合样本数据的先验知识;为了区分数据和噪声,提出基于样本紧密度的策略。运用该策略以及噪声分布模型,自动生成相应样本数据点的权值,据此调整模糊LSSVM模型的模糊隶属度。实验结果表明该方法具有很好的抗噪声能力和鲁棒性。
第五,提出一种过程故障诊断专家系统的设计方案。该方案着重解决了以下几方面的问题:在知识获取方面,采用故障树、经验知识表格和决策树方法有机结合,解决了专家系统知识获取的“瓶颈”问题;知识校验是知识获取的一个重要步骤,提出一种基于有向图的知识校验方法;知识库分为全局知识库和内存知识库,提出一种内存知识库的知识选择策略;为了消解知识冲突,融合过程知识,提出基于统计和时间序列分析的动态知识冲突消解策略。
第六,基于以上研究,着重从实际应用对系统的要求出发,开发了过程安全运行智能支持系统,并详细描述了系统的结构和主要功能。通过在某石化公司润滑油生产过程的实际应用,验证了所开发系统的有效性和实用性,进而证明了本文提出的过程安全运行系统框架以及为实现该系统所采用的各种技术的实用性。
Characteristics of process engineering are a complex flows and colossal system. There are many unsafe factors. Abnormal situations occur from time to time. Therefore, an operation safe is a most important thing for the process engineering. Using enough information technologies, it can assure that the production process is safe, and which it also is an important technology problem for the process engineering. At the present time, with the extending application of DCS (Distributed Computer System) and computer techniques, a great amount of process data which includes process information can be sampled and collected. But, these data are not employed availably, thus it leads abundant data and poor information. In other hand, an amount of empirical knowledge, especially treating abnormal situations, are accumulated. But, these information are not handled better. Therefore, it is research contents in the paper how to transform these data into informations which can be used, and extract feature information which effects on the operation safety of the process from the process data, and further make enough use of these information and combine the empirical knowledge to support the long, steady, and safe operation for the process engineering.
Based on the motivation, a conception and frame for an intelligent aided system of process safety operation is presented in the paper. The structure, function, and some key techniques for the system are described in detail. The proposed system is developed and applied to a real industrial process.
Firstly, according to the characteristic of chemical engineering, the intelligent aided system for process safety operation is presented based on an intelligent algorithm for identifying an operation state and combined with process models, production objects, and intelligent techniques. A hybrid intelligent method for fault diagnosis is presented based on the discussion and analysis for some key techniques.
Secondly, an improved kernel principal component analysis (KPCA) based on multi-block feature vector selection is presented in the paper. A kernel matrix K need be computed in the process of constructing a model for KPCA. But, for a large sample, it is very difficult to solve the kernel matrix K. Using a method of multi-block feature vector selection, a sub-sample is selected to describe the all sample, then a model for KPCA is trained by the sub-sample. The experiment shows that performance of the proposed method and KPCA are almost equivalent. The dimension of kernel matrix K for the proposed method reduces. Consequently, the computing complexity of K drops.
Thirdly, a classification method combining a kernel-based feature extraction with a least square support vector machines (LSSVM) for an operation mode of process is presented. A process data are characteristic of high dimension, strongly nonlinearity, high noise and small fault samples. Therefore, it is very difficult to directly identifying the process operation mode from these data. Using the kernel-based feature extraction, the inputting data are decomposed, and removed from redundant or related information, and extracted from process feature information. Then, the classification model based on LSSVM is built up for identifying the process operation mode. The real data from an industrial process is used to prove the proposed method. The experiment result shows that the proposed method is effective.
Fourthly, a fuzzy least squares support vector machines classification method incorporated with a prior knowledge on data is presented. To address the drawback which a least squares support vector machines is sensitive to noises or outliers, the least squares support vector machines model combining with the prior knowledge on process data is represented based on a noise distribution model and a strategy based on a sample affinity. Information of noise distribution model for process data is introduced in the training process of the model;The strategy based on the sample affinity is presented to discriminate between data and noises. A fuzzy membership is automatically generated and assigned to each corresponding data point by using the strategy and the noise model. Thus, the fuzzy membership in the LSSVM model can be adjusted. The experiment result shows that the proposed method has better performance and robustness against the noise data.
Fifthly, a fault diagnosis expert system for the chemical process is presented. The following problem is considered in the process of the design for the expert system. In the process of knowledge acquisition, a problem which a knowledge acquisition has always been the bottleneck in developing expert systems is solved by using a method combined a fault tree and an empirical knowledge table with a decision tress; A knowledge verification is an import step for the knowledge acquisition. The knowledge verification is carried out based on a directed graph approach in the paper; A selection strategy of knowledge rules in the memory knowledge base is presented; Incorporating process knowledge, a dynamical strategy of knowledge conflict resolution based on a statistics and a time-series analysis method is introduced for resolving a knowledge conflict.
Sixthly, based on the previous research and a requirement of real application environment, the intelligent aided system for chemical process safety operation is developed. The structure and the main function for the proposed system are described in detail. The proposed system is applied to a lubricating oil process in a petroleum plant. The validity and practicability of the proposed system is proven. Further, the frame of the proposed system as well as some key techniques used to the system can be confirmed with the availability.
引文
[1] Venkatsdubramanian V., Stanley G. M. Integration of process monitoring. Diagnosis and Control: Issues and Emeriging Trends [J]. Proc. FOCAPO’93, 1993, Colorado, U.S.A.
[2] 钱宇. 过程系统工程研究进展 [J].化工研究进展, 1997, (1), 20-25.
[3] Li X. X., Qian Y., and Wang J. F. Process monitoring based on wavelet packet principal component analysis [J]. European Symposium on Computer Aided Process Engineering-13, 2004, 455-460.
[4] 黄启明, 钱宇, 林伟璐, 李秀喜. 化工过程故障研究进展 [J]. 化工自动化及仪表, 2000, 27(3), 1-5.
[5] Venkat V., Raghunathan R., Kewen Y., Surya, N K. A review of process fault dectection and diagnosis Part I: Quantitative model-based methods [J]. Computers and Chemical Engineering, 2003, 27, 293-311.
[6] Venkat V., Raghunathan R., Surya, N K. A review of process fault dectection and diagnosis Part II: Quantitative modeland Search strategies [J]. Computers and Chemical Engineering, 2003, 27, 313-326.
[7] Venkat V., Raghunathan R., Surya, N K., Kewen Y. A review of process fault dectection and diagnosis Part III: Process history based methods [J]. Computers and Chemical Engineering, 2003, 27, 327-346.
[8] 郑小霞, 钱峰. 动态系统故障诊断技术研究与发展 [J]. 化工自动化及仪表, 2005, 32(4), 1-7.
[9] Frank P. M., et al, New developments using AI in fault diagnosis [J]. Engineering Application of Artificial Intelligence, 1997, 10(1), 3-14.
[10] X. Z. Wang, B. H. Chen, and C. McGreavy, Application of wavelets and neural networks to diagnostic system development-an integrated framework and its application [J]. Computer and Chemical Engineering, 1999(23), 945-954
[11] 方崇智, 肖德云. 过程辨识 [M]. 北京, 清华大学出版社, 1988.
[12] Fank P. M. Fault diagnosis in dynamical system and analytical and knowledge-based redundancy- a survery and some new results [J]. Automatica, 1990, 26, 459-474.
[13] Zolghadri A. An algorithm for real time failure detection in kalman filters [J]. IEEE Transactions on Automatic Control, 1996, 41(10), 1537-1539
[14] 周东华, 叶银忠. 现代故障诊断与容错技术, 北京, 清华大学出版社, 2000
[15] Gertler J. J. Fault detection and diagnosis in engineering system. New York, Marcel Dekker, 1998.
[16] Vaidyanathan R., Venkatasubramanian. V. Process fault dection and diagnosis using neural networks :II dynamic process [J]. AIChE J., Annual meeting, Chiago, 1990.
[17] Leonard J. A., Kramer M. A. Diagnosis dynamic faults using modular neural nets [J]. IEEE Experts, 1993, (4), 44-53
[18] Cromm J. B. Self-learning RBF neural networks of on-line process fault diagnosis [J].Proceeding of PSE’94, Korea, 1994, 931-935.
[19] Bernieri A., Apuzzo M. D. et.al. A neural network approach for identifization and fault diagnosis on dynamic system [J]. IEEE Transactions on Instrument and Measurement, 1994, 43(6), 867-863.
[20] Iri. M., Aoki K., E. O’Shinma, and Matsuyama H. An algorithm for diagnosis of system failures in the chemical process [J]. Computers and Chemical Engineering, 1979, 3(1-4),489-493.
[21] Umeda. T., Kuriyama T., O’shima E., and Matsuyama H.A graphical approach to cause and effect analysis of chemical processing systems [J]. Chemical Engineering Science, 1980, 35(12),2379-2388.
[22] Kramer M. A. and Palowitch B. L. A rule based approach to fault diagnosis using the signed directed graph [J]. American Institute of Chemical Engineers Journal, 1987, 33(7),1067-1078.
[23] Vedam H. and Venkatasubramanian V. Signed digraph based multiple fault diagnosis [J]. Computers and Chemical Engineering, 1997(21), S655-S660.
[24] Yu C. C., and Lee C. Fault diagnosis based on qualitative/quantitative process knowledge [J]. AIChE Journal, 1991, 37(4), 617-628.
[25] Han C., Shih R., and Lee. L. Quantifying signed directed graphs with the fuzzy set for fault diagnosis resolution improvement [J]. Industrial and Engineering Chemistry Research, 1994, 33(8), 1943-1954.
[26] Liao Shu-Hsien. Expert system methodologies and applications-a decade review from 1995-2004 [J]. Expert System with Applications, 2005, 28, 93-103.
[27] Feng E., Yang H. B., Rao M. Fuzzy Expert System for Real-time Process Condition Monitoring and Incident Prevetion [J]. Expert System with Application, 1998, 15, 383-390.
[28] Becriaft W. R., Lee P. Integrated Neural Network/Eexpert System Approach for Fault Diagnosis [J]. Computers & Chemical Engineering, 1993, 17(10), 1002-1014.
[29] Jokinen P. A. Nonlinear Network Model for Continously Learning Expert System: Part I, Theory [J]. Proecess Control and Quality, 1991, 1(4), 265-279.
[30] Jokinen P. A. Nonlinear Network Model for Continously Learning Expert System: Part II, Application [J]. Proecess Control and Quality, 1992, 1(2), 155-168.
[31] Ozyurt B., Kandel A. Hybrid Hierachical Neural Network Fuzzy Expert System Approach to Chemical Process Fault Diagnosis [J]. Fuzzy Sets and Systems, 1996, 83(1), 11-25.
[32] Xu D. L., Liu J., Yang J. B., Liu G. P., Wang J. Inference and Learning Methodology of Belief-Rule-Based Expert System for Pipeline Leak Detection [J]. Expert System with Application, 2007, 32, 103-113.
[33] Chan C. W. An expert decision support system for monitoring and diagnosis of petroleum production and separation processes [J]. Expert System with Application, 29, 2005, 131-143.
[34] Wang X. W., Qu H.B., Liu P., Cheng Y. Y. A self-learning expert sytem for diagnosis in tradition Chniese Medince [J]. Expert System with Application, 26, 557-566.
[35] Khaled S., Mona El-B., Ahmed R. A multiagent approach for diagnostic expert system via the internet [J]. Expert System with Application, 27, 2004, 1-10.
[36] Jacobo V. H., Ortiz A., Cerrud Y., Schouwenaars R. Hybrid Expert System for the Faitlure Analysis of Mechanical Elements. Engineering Failure Analysis, 2007.02.002.
[37] Leung D., Romagnoli J. Dynamic probabilistic model-based expert system for fault diagnosis [J]. Computers and Chemical Engineering, 2000, 24, 2473-2492.
[38] Leung R.W.K., Lau H.C.W., Kwong C. K. An expert system to support the optimization of ion plating process: an OLPA-based fuzzy-cum-GA approach [J]. Expert System with Application, 2003, 25, 313-330.
[39] Qian Yu, Li X.X, Jiang Y., and Wen Y. Q. An expert system for real-time fault diagnosis of complex chemical processes [J]. Expert Systems with Applications, 2003, 24(4), 425-432.
[40] Qian, Y., Zeng M.J., Li X.X. Implementation of knowledge maintenance modules in an expert system for fault diagnosis of chemical process opration [J]. Expert System with Application,28(2005) 249-257.
[41] Punal A. Roca E. Lema J. M. An expert system for monitoring and diagnosis of diagnosis of anaerobic wastewater treatment plants [J]. Water Research, 2002, 36, 2656-2666.
[42] Batanov D.N., Cheng Z. An object-oriented expert system for fault diagnosis in the ethylene distillation process [J]. Computers in Industry, 1995, 237-249.
[43] Yang B.S., Lim D.S., Tan A. C. C. VIBEX: an expert system for vibration fault diagnosis of ratating machinery using decision tree and decision table [J]. Expert System with Application, 2005, 735-742.
[44] Mandel J. Use of singular value deceposition in regression analysis [J]. American Statistictian, 1982, 36, 15-24.
[45] Kresta J. V., Macgregor J. F., Marlin T. E. Multivariate strtistical monitoring of process operating performance [J]. The Canadian Journal of Chemical Engineering, 69(2), 35-47.
[46] MacGreger J. F., Kourti J. Statistic process and control multivariable processes, Control Engineering Practise [J], 1995, 3, 403-416.
[47] Ku W. F., Storer R. H. Georgakis C. Disturbance detection and islotion by dynamica principal component analysis [J]. Chemometics and Intelligent Laboratory Systems, 1995, 30, 179-196.
[48] Barshi B. R. Mutiscale PCA with application to multivariate statistical process monitoring [J]. AIChE Journal, 1998, 44(7), 1596-1610.
[49] Dong D., McAvoy T. J. Nonlinear principal component analysis based on principal curve and neural networks [J]. Computers and Chemical Engineering, 1996, 20(1), 65-78.
[50] Macgregor J. F., Kiparissides C. J., Koutoudi M. Process monitoring and diagnosis by multiblock methods [J]. AIChe Journal, 1994, 40, 826-839.
[51] Li W., Yue H. H., Valle-Cervantes S., Qin S. J. Recurise PCA for adaptive processmonitoring [J].Journal of Process Control, 2000, 10(5), 471- 486.
[52] Scholkopf B., Smola A. Muller K. R. Nonlinear component analysis as a kernel eigenvalue problem [J]. Neural Computation, 1998, 1299-1319.
[53] Choi S. W., Lee C., Lee J-M., Park J. H., Lee I-B. Fault detection and identification of nonliearn process based on kernel PCA [J]. Chemometics and Intelligent Laboratory Systems, 2005, 75(1), 55-67.
[54] Cho J. H., Lee J. M., Wook Ch. S., Lee D., Lee I-B. Fault diagnosis for process monitoring using kernel principal component analysis [J]. Chemical Engineering Science, 2005, 60(1), 279-288.
[55] Bell A. J., Sejnowski T. J. An information-maximization approach to blind separation and blind deconvolution [J]. Neural Computation, 1995, 7(6), 1004-1034.
[56] Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis [J]. IEEE Trans. On Neural Networks, 1999, 10(3), 626-634.
[57] Bach F. R., Jordan M. I. Kernel independent component analysis [J]. Journal of Machine Learning Research, 2002, (3), 1- 48.
[58] Lee J.M. Yoo C., and Lee I-B. Statistical process monitoring with independent component analysis[J]. J. Process Control. 2004, 8, 467-485.
[59] 陈国金,梁军,钱积新.独立元分析方法及其在化工过程监控和故障诊断中的应用[J]. 化工学报, 2003,54, 1474-1477.
[60] Li R.F., Wang X.Z.. Dimension reduction of process dynamic trends using independent component analysis[J]. Computers and Chemical Engineering, 2002, 26, 467-473.
[61] 张学工. 关于统计学习理论与支持向量机 [J]. 自动化学报, 2000, 26(1), 32- 42.
[62] Vapnik, VN. Statistical learning theory [M]. 许建华等译, 北京, 电子工业出版社, 2004.
[63] Osuna E., Freund R., Girosi F. Training Support Vector Machines, An Application to Face Detection [J]. In Proc. Of CVPR’97 [C], Puerto Rico, 1997.
[64] Platt J.C., Sequential Minimal Optimization: a Fast Algorithm for Training Support Vector Machines [J]. Technical Report MSR-TR-98-14, Microsoft Research, 1998.
[65] Keerthi S. S., Shevade S. K., Bhattacharyya C. et al. A fast Iterative Nearest Point Algorithm to Support Vector Machine Classifier Design [R]. Technical Report TR-ISL-99-03, India: India Institute of Science, 1999
[66] Pavlov D., Mao J., Dom B. Scaling-up Support Vector Machines Using Boosting Algorithm [J]. In Proceedings of the International Conference on Pattern Recognition. ICPR-2000, 2000.
[67] Bradley P. S., Mangasarian O. L. Massive data discrimination via linear support vector machines [R].Technical Report 98-05, Madison, WI:Univ. of Winsconsin, 1998.
[68] Mangasarian O. L., Musicant D. R. Successive Overrelaxation for Support Vector Machines [J]. IEEE Tran. On Neural Networks, 1999, 10, 1032-1037
[69] Ulrich, K. Pairwise classification and support vector machines. In B Scholkopf, C. J. C.Burges, A. J. Smola, editors, Advances in Kernel Methods Support Vector Learning [M]. Cambridge, MA, MIT Press, 1998, 255-268.
[70] Ahmed S. N.,et al. Incremental learning with support vector machines [J]. Workshop on support vector machines, Stockholm, Sweden, August, 2, 1999.
[71] An improved incremental training algorithm for support vector machines using active query [J]. Pattern Recognition, 2007, 40, 964-971
[72] 李昆仑, 黄厚宽, 田盛丰. 模糊多类 SVM 模型 [J]. 电子学报, 2004, 32(5), 830-832.
[73] Chiang L. H., Kotanchek M. E., Kordon A. K. Fault diagnosis based on fisher discriminant analysis and support vector machines [J]. Computers and Chemical Engineering, 2004, 28, 1389-1401
[74] 王华忠, 张雪申, 余金寿. 基于支持向量机的故障诊断方法[J]. 华东理工大学学报, 2004, 30(2), 179-182.
[75] Widodo A., Yang B. S., Han T. Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors [J]. Expert System with Applications, 2007, 32, 299-312.
[76] 吴静, 周建国, 晏浦柳. 支持向量机在网络故障中的应用研究[J]. 计算机工程, 2004, 30(22), 44-46.
[77] Patterson-Hine F. A., Dugan J. B. Modular techniques for dynamic fault-tree analysis [J]. Proceedings Annual Reliability and Maintainability Symposium, 1992, 363-369
[78] Cheung J. T., and Stephanopoulos, G. Representation of process trends part I: A formal representation framework [J]. Computers and Chemical Engineering, 1990, 14(4-5), 495-510.
[79] Janusz M., and Venkatasubramanian V. Automatic generation of qualitative description of process trends for fault detection and diagnosis [J]. Engineering Application of Artificial Intelligence, 1991, 4, 329-339.
[80] Rengaswamy R., and Venkatasubramanian V. A syntactic pattern-recognition approach for process monitoring and fault diagnosis [J]. Engineering Applications of Artificial Intelligence, 1995, 8(1), 35-51.
[81] Vedam H., and Venkatasubramanian V. A wavelet theory-based adaptive trend analysis system for process monitoring and diagnosis [J]. In American control conference, 1997, 309-313.
[82] Vedam H., and Venkatasubramanian V, and Bhalodia M. A B-spline based method for data compression, process monitoring and diagnosis [J]. Computers and Chemical Engineering, 1998(22), S827-S830.
[83] Rengaswamy R., Hagglund T., and Venkatasubramanian V. A qualitative shape analysis formalism for monitoring control loop performance [J]. Engineering Applications of Artificial Intelligence, 2001, 14(1), 23-33. [ 84] Sourabh D, Raghunathan R & Venkatasubramanian V. Fuzzy-logic based trend classification for fault diagnosis of chemical processes [J]. Computers & ChemicalEngineering, 2003, 27(3), 347-362.
[85] Dash S., Venkatasurbramanian V. Challenges in the industrial application of fault diagnostic system [J]. Computers & Chemical Engineering, 2000, 24, 785-791.
[86] Mylaraswamy D., Venkatasubramanian V. A hybrid framework for large scale process fault diagnosis, Computers & Chemical Engineering [J]. 1997, 21, 935-940.
[87] Ruiz D., Nougues J. M., Puigijaner L. Fault diagnosis support system for complex chemical plants [J]. Computers & Chemical Engineering, 2001, 25, 151-160.
[88] Chan C. W. An expert system support system for monitoring and diagnosis of petroleum production and separation process [J]. Expert System with Application, 2005, 29, 131-143.
[89] Yang B. S., Lim D. S., Tan A. C. C. VIBEX: an expert system for vibration fault diagnosis of ratating machinery using decision tree and decision table [J]. Expert System with Application, 2005, 28, 735-742.
[90] http://simusafe.buct.edu.cn/research
[91] Webb A. R. Statistical pattern recognition [M]. Second Edition, 王萍等译,北京,电子工业出版社,2004.
[92] Bakshi B. R. Mutiscale PCA with application to multivariate statistical process monitoring [J]. AIChE J., 1998, 44(7), 1596-1610.
[93] Russell E. L., Chiang L. H., and Braatz R. D. Fault detection in industrial processes using canonical variety analysis and dynamic principal component analysis [J]. Chemometics and Intelligent Laboratory Systems, 2000, 51, 81-93.
[94] Comon P. Independent component analysis- a new concept? [J] Signal Process, 1994, 36(3), 287-314.
[95] Li X. X., Qian Y., and Wang J. F. Process monitoring based on wavelet packet principal component analysis [J]. European Symposium on Computer Aided Process Engineering-13, 2004, 455-460.
[96] Bakshi B. R., Stephanopoulos G. Representation of process trends – III. Multiscale extraction of trends from process data [J]. Computers and Chemical Engineering, 18(4), 1994,267-302
[97] Wang X.Z., Chen B.H., Yang S.H., and McGreavy C. Application of wavelets and neural networks to diagnostic system development- an integrated framework and its application [J]. Computer and Chemical Engineering, 1999, 23, 945-954.
[98] Li, R.F. and Wang X.Z. Dimension reduction of process dynamic trends using independent component analysis [J]. Computer and Chemical Engineering, 2002, 26(3), 467-473.
[99] 姚志湘,钱宇,李秀喜. 多变量化工过程系统的状态空间及其维数的判定准则 [J].高校化学工程学报,2004, 18(1),73-78.
[100] Wilks S. S. Mathematical statistics [M]. Wiley, New York, 1962, 577-578.
[101] Bellnumeur P. N., Hespanha P. J., Kriegam D. J. Eigenfaces vs Fisherfaces: Recognitionusing calss specific linear projection[J]. IEEE Trans. Pattern. Anal. Machine Intell, 1997, 19(7), 711-720.
[102] 杨健, 杨静宇, 金忠. 最优鉴别特征的抽取及图像识别[J]. 计算机研究与发展, 2001, 38(11), 1331-1336.
[103] Dong D., McAvoy T. J. Nonlinear principal component analysis – based on principal curves and neural net works [J]. Computer and Chemical Engineering, 1996, 20(1), 65-78.
[104] Lin W. L., Qian Y., Li X. X. Nonlinear dynamic principal component analysis for on-line process monitoring and diagnosis [J]. Computer and Chemical Engineering, 2000, 24, 423-429.
[105] Li X. X., Qian Y. and Wang J. F. Process monitoring based on wavelet packet principal component analysis [J]. European Symposium on Computer Aided Process Engineering-13, 2004, 455-460.
[106] Baudat G. Anouar A. Generalized discriminate analysis using a kernel approach [J]. Neural Computation, 2000, 12(10), 2385-2404.
[107] Macgregor J. F. Using online process data to improve quality: challenges for statistician [J]. International Statistic Review, 1997, 65, 309-323
[108] 谢磊. 间歇过程统计性能监控[D]. 杭州, 浙江大学, 2005.
[109] Jain A. K., Murty M. N., Flynn P. J. Data clustering: A survey [J]. ACM Computer Survey, 1999, 31, 264-323
[110] Safavian S. R. Landgrebe D. A. A survey of decision tree classifier methodology [J]. IEEE Transaction on Systems, Man, and Cybernetics, 1991, 21(3), 660-674.
[111] Aitkenhead M. J. A co-evolving decision tree classification method [J]. Expert Systems with Application, 2008, 34(1), 18-25
[112] Hoskins J. C., Himmelblau D. M. Artificial neural network models of knowledge representation in chemical engineering [J]. Computer and Chemical Engineering,12(9-10), 881-890
[113] Li X X, Qian Y,Huang Q M, Jing Y B. Multi-Scale ART2 for state Identification of Process Operation Systems [J]. Computer-Aided Chemical Engineering-15, ed. by B.Z. Cheng and A. W. Westerberg, Elsevier, 2004, 523-529
[114] Lewis D. D., Na?ve (Bayes) at forty: The Independence assumption in information retrieval [J]. Proceedings of the 10th European Conference on Machine Learning, New York, 1998, 4-15 [ 115 ] Ramoni M., Sebasiani P. Bayesian methods for intelligent data analysis: An Introduction [M]. New York: Springer, 1999, 129-166
[116] 宫秀军, 孙建平, 史忠值. 主动贝叶斯网络分类器[J]. 计算机研究与发展, 2002, 39(5), 574-579
[117] Mitchell T. M. Machine learning [M]. 曾华军等译,北京,机械工业出版社, 2003.
[118] Han J., Kamber M. Data mining concepts and techniques [M]. 范明等译,北京,电子工业出版社, 2001
[119] Sahan S. Polat K., Kodaz H., Gunes S. A new hybrid method based on fuzzy-aritficical immune system and k-nn algorithm for breast cancer diagnosis [J]. Computers in Biology and Medicine, 2007, 37, 415-423
[120] Pawlak Z. Rough Sets [J]. International Journal of Computer and Information Science, 1982, 11(5), 341-356
[121] 张登峰, 王执铨. 故障特征选择与诊断规则提取的 VPRS 模型方法[J]. 系统仿真学报, 15(6), 793-803
[122] 黄启明. 化工过程集成运行系统与故障诊断的研究[D]. 广州, 华南理工大学博士学位论文, 2001.
[123] Pao Y. H. Adaptive pattern recognition and neural networks [J]. Addison-wesley, 1998. [ 124 ] Marriott S., Harrison R. F. A modified fuzzy ARTMAP architecture for the approximation of noisy mapping [J]. Neural Network, 1995, 8(4), 619
[125] Srinivasa N. Learning and generalization of noisy mapping using a PROBART neural network [J]. IEEE transactions on signal processing, 1997, 45(10), 2533
[126] 田盛丰. 基于核函数的学习算法[J]. 北方交通大学学报, 2003, 27(2), 1-8
[127] Joseph G., Gary R.Expert System Principal and Programming [M]. 印鉴等译, 北京, 机械工业出版社,2000.
[128] 尹朝庆,尹皓.人工智能与专家系统[M]. 北京, 中国水利水电出版社, 2002.
[129] Karmer M. A. Nonlinear principal component analysis using associative neural networks [J]. AIChE Journal, 1991, 37(2), 233-243
[130] Tan S. F., Mavrovounitois M. L. Reducing data dimensionality through optimizing neural network inputs [J]. AIChE Journal, 41(6), 1471-1480
[131] Cristianini N., Tayer S. J. An introduction to support vector machines and other kernel-based learning methods [M], 李国正等译,北京, 电子工业出版社, 2005
[132] Taylor S. J., Cristianini N. Kernel methods for pattern analysis [M], 赵铃铃等译, 北京, 机械工业出版社, 2006
[133] Su Ch. T., Yang Ch. H. Feature selection for the SVM: An application to hypertension diagnosis [J]. Expert System with Application, 2008, 34(1), 754-763.
[134] Xu Y., Zhang D., Song F., Yang J. Y., Jing Zh., Li M. A method for speeding up feature extraction based on KPCA [J]. Neurocomputing, 2007, 70, 1056-1061.
[135] Zheng W., Zou C., Zhao L. An improved algorithm for kernel principal component analysis [J]. Neural Processing Letters, 2005, 22, 49-56
[136] Cao L. J., Chua K. S., Chong W. K., Lee H. P., Gu Q. M. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine [J]. Neurocomputing, 2003, 55, 321-336.
[137] Hyvarinen A., Oja E. Independent component analysis: algorithms and application [J]. Neural Networks, 2000,13(4-5), 411-430
[138] Jang G. J., Yun S. J., Hwan Y. Feature vector transformation using independent component analysis and its application to speaker identification [J]. Proceedings of Eurospeech, 1999, Budapest Hungary, 767-770.
[139] 蒋宁. 基于 ICA_PCA 方法流出工业过程监控与故障诊断的研究[D]. 杭州, 浙江大学博士学位论文.
[140] Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans [J]. on Neural Networks, 10(3), 626-634.
[141] Kocsor A., Csirik J. Fast independent component analysis in kernel feature spaces [J]. SOFSEM, 2000, LNCS 2234, 271-281.
[142] Bach F. R., Jordan M. I. Kernel independent component analysis [J]. Journal of Machine Learning Research, 3, 1- 48.
[143] Kim S. W., John O. B. On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods [J]. Pattern Recongnition, 2004, 37(2), 227-239
[144] Sun L. Zh., Huang D. S., Cheun Y. M. Extracting nonlinear features for multispectral images by FCMC and KPCA [J]. Digital Signal Processing, 2005, 15, 331-346.
[145] Baudat. G., Anouar F. Feaure vector selection and projection using kernels [J]. Neurocomputing, 2003, 55, 21-38.
[146] Suykens J. A. K., Vandevalle, J. Least Squares Support Vector Machine Classifiers [J]. Neural Processing Letters, 1999, 9(3), 293-300.
[147] Suykens J. A. K., Baesens B., Moor B. Benchmarking Least Squares Support Vector Machine Classifiers [J]. Machine Learning, 2004, 54, 5-32
[148] Tsujinishi D., Shigeo Abe. Fuzzy Least Squares Support Vector Machines for Multiclass Problems[J]. Neural Networks, 2003, 785-792
[149] Dietterich T. G., Bakiri G. Solving multiclass learning problems via error-correcting output codes [J]. Journal of Artificial Intelligence Research, 1995, 2, 263-286.
[150] 朱永生, 王成栋, 张优云. 二次损失函数支持向量机性能的研究[J]. 计算机学报, 2003, 26(8), 982-989.
[151] Jooyong, S., Changha, H., Sunkyun, N. Robust LS-SVM Regression Using Fuzzy C-Means Clustering [J]. Advances in Natural Computation, 2006, 4221, 157-166.
[152] 张英,苏宏业,褚健. 基于模糊最小二乘支持向量机的软测量建模 [J]. 控制与决策, 2005, 25(6), 621-624.
[153] Lauer, F., Bloch, G. Incorporating prior knowledge in support vector machines for classification: A review [J]. Neurocomputing, 2007, doi:10.1016, 1-17.
[154] Suykens J.A.K., De Brabanter J., Lukas L., Vandewalle J., Weighted least squares support vector machines : robustness and sparse approximation [J]. Neurocomputing, Special issue on fundamental and information processing aspects of neurocomputing, vol. 48, no. 1-4, Oct. 2002, pp. 85-105
[155] Lin, C. F., Wang, S. D. Fuzzy Support Vector Machines [J]. IEEE Transaction on Neural Networks, 2002, 13(2), 464-471.
[156] Huang H. P., Liu, Y. H. Fuzzy Support Vector Machines for Pattern Recognition and Data Mining [J]. International Journal of Fuzzy Systems, 2002, 4(3), 826-835.
[157] Liu, F. C., Wang, D. S. Training Algorithms for Fuzzy Support Vector Machines with Noisy Data [J]. Pattern recognition letters, 2004, 25, 1647-1656.
[158] 张翔, 肖小玲, 徐光佑. 基于样本之间紧密度的模糊支持向量机方法 [J]. 软件学报, 2006, 17(5), 951-958.
[159] David, M. J. T., Robert, P. W. D. Support Vector Machines [J]. Pattern recognition letters, 1999, 20, 1191-1199. [ 160 ] Matsatsinis, N.F., Doumpos, M & Zopounidis. Knowledge acquisition and representation for expert systems in the field of financial ayalysis. Expert System with Application, 1997, 12, 247-262.
[161] Mansingh, G., Reichgelt, H., Osei-Bryson, K.-M. CPEST: An expert system for the management of pests and diseases in the Jamaican coffee industry. Expert System with Application, 2007, 32, 184-192.
[162] 史定华, 王松瑞. 故障树分析技术方法和理论[M]. 北京, 北京师范大学出版社, 1993.
[163] 于海文. 基于故障树分析法的故障诊断专家系统[M]. 中国空间科学技术, 1998, (1), 37-44.
[164] Quinlan, J. R. Quinlan, J. R. Induction of decision trees [J]. Machine Learning, 1986, 1, 81-106.
[165] Quinlan, J .R. C4.5: programs for machine leanings [J]. Canada: Morgan Kaufmann, 1993.
[166] 蒋祖华,苏海. 工程设计类知识管理类技术研究 [J]. 计算机集成制造系统,2004, 10(10), 1225-1232.
[167] William F., William T. Data Structures with C++ [M]. BeiJing: Tsinghua University Press. 2001, 579-599.
[168] Adam, D. Data Structures and Algorithms in C++ [M]. Thomsm Learning, Inc, 2003.
[169] 尹朝庆,尹浩. 人工智能与专家系统[M].北京: 中国水利水电出版社,2002.
[170] 何华灿.人工智能导论 [M].西安:西北工业大学出版社, 1988.
[171] 水天德. 现代润滑油生产工艺第 1 版. 中国石化出版社. 1997,6-15
[172] 李志英等.润滑油生产技术问答.第 1 版. 茂名石化南海高级润滑油公司,1999,177-192,193-239
[173] 重质糠醛精制装置操作规程. 中国石化公司茂名分公司,2004.
[174] 重质酮苯脱蜡装置操作规程. 中国石化公司茂名分公司,2004.
[175] 薛福珍, 林盛荣, 唐琰. 基于 OPC 数据访问规范的客户端软件研究与开发[J]. 计算机工程. 2002, 28(4), 229-231
[2] 钱宇. 过程系统工程研究进展 [J].化工研究进展, 1997, (1), 20-25.
[3] Li X. X., Qian Y., and Wang J. F. Process monitoring based on wavelet packet principal component analysis [J]. European Symposium on Computer Aided Process Engineering-13, 2004, 455-460.
[4] 黄启明, 钱宇, 林伟璐, 李秀喜. 化工过程故障研究进展 [J]. 化工自动化及仪表, 2000, 27(3), 1-5.
[5] Venkat V., Raghunathan R., Kewen Y., Surya, N K. A review of process fault dectection and diagnosis Part I: Quantitative model-based methods [J]. Computers and Chemical Engineering, 2003, 27, 293-311.
[6] Venkat V., Raghunathan R., Surya, N K. A review of process fault dectection and diagnosis Part II: Quantitative modeland Search strategies [J]. Computers and Chemical Engineering, 2003, 27, 313-326.
[7] Venkat V., Raghunathan R., Surya, N K., Kewen Y. A review of process fault dectection and diagnosis Part III: Process history based methods [J]. Computers and Chemical Engineering, 2003, 27, 327-346.
[8] 郑小霞, 钱峰. 动态系统故障诊断技术研究与发展 [J]. 化工自动化及仪表, 2005, 32(4), 1-7.
[9] Frank P. M., et al, New developments using AI in fault diagnosis [J]. Engineering Application of Artificial Intelligence, 1997, 10(1), 3-14.
[10] X. Z. Wang, B. H. Chen, and C. McGreavy, Application of wavelets and neural networks to diagnostic system development-an integrated framework and its application [J]. Computer and Chemical Engineering, 1999(23), 945-954
[11] 方崇智, 肖德云. 过程辨识 [M]. 北京, 清华大学出版社, 1988.
[12] Fank P. M. Fault diagnosis in dynamical system and analytical and knowledge-based redundancy- a survery and some new results [J]. Automatica, 1990, 26, 459-474.
[13] Zolghadri A. An algorithm for real time failure detection in kalman filters [J]. IEEE Transactions on Automatic Control, 1996, 41(10), 1537-1539
[14] 周东华, 叶银忠. 现代故障诊断与容错技术, 北京, 清华大学出版社, 2000
[15] Gertler J. J. Fault detection and diagnosis in engineering system. New York, Marcel Dekker, 1998.
[16] Vaidyanathan R., Venkatasubramanian. V. Process fault dection and diagnosis using neural networks :II dynamic process [J]. AIChE J., Annual meeting, Chiago, 1990.
[17] Leonard J. A., Kramer M. A. Diagnosis dynamic faults using modular neural nets [J]. IEEE Experts, 1993, (4), 44-53
[18] Cromm J. B. Self-learning RBF neural networks of on-line process fault diagnosis [J].Proceeding of PSE’94, Korea, 1994, 931-935.
[19] Bernieri A., Apuzzo M. D. et.al. A neural network approach for identifization and fault diagnosis on dynamic system [J]. IEEE Transactions on Instrument and Measurement, 1994, 43(6), 867-863.
[20] Iri. M., Aoki K., E. O’Shinma, and Matsuyama H. An algorithm for diagnosis of system failures in the chemical process [J]. Computers and Chemical Engineering, 1979, 3(1-4),489-493.
[21] Umeda. T., Kuriyama T., O’shima E., and Matsuyama H.A graphical approach to cause and effect analysis of chemical processing systems [J]. Chemical Engineering Science, 1980, 35(12),2379-2388.
[22] Kramer M. A. and Palowitch B. L. A rule based approach to fault diagnosis using the signed directed graph [J]. American Institute of Chemical Engineers Journal, 1987, 33(7),1067-1078.
[23] Vedam H. and Venkatasubramanian V. Signed digraph based multiple fault diagnosis [J]. Computers and Chemical Engineering, 1997(21), S655-S660.
[24] Yu C. C., and Lee C. Fault diagnosis based on qualitative/quantitative process knowledge [J]. AIChE Journal, 1991, 37(4), 617-628.
[25] Han C., Shih R., and Lee. L. Quantifying signed directed graphs with the fuzzy set for fault diagnosis resolution improvement [J]. Industrial and Engineering Chemistry Research, 1994, 33(8), 1943-1954.
[26] Liao Shu-Hsien. Expert system methodologies and applications-a decade review from 1995-2004 [J]. Expert System with Applications, 2005, 28, 93-103.
[27] Feng E., Yang H. B., Rao M. Fuzzy Expert System for Real-time Process Condition Monitoring and Incident Prevetion [J]. Expert System with Application, 1998, 15, 383-390.
[28] Becriaft W. R., Lee P. Integrated Neural Network/Eexpert System Approach for Fault Diagnosis [J]. Computers & Chemical Engineering, 1993, 17(10), 1002-1014.
[29] Jokinen P. A. Nonlinear Network Model for Continously Learning Expert System: Part I, Theory [J]. Proecess Control and Quality, 1991, 1(4), 265-279.
[30] Jokinen P. A. Nonlinear Network Model for Continously Learning Expert System: Part II, Application [J]. Proecess Control and Quality, 1992, 1(2), 155-168.
[31] Ozyurt B., Kandel A. Hybrid Hierachical Neural Network Fuzzy Expert System Approach to Chemical Process Fault Diagnosis [J]. Fuzzy Sets and Systems, 1996, 83(1), 11-25.
[32] Xu D. L., Liu J., Yang J. B., Liu G. P., Wang J. Inference and Learning Methodology of Belief-Rule-Based Expert System for Pipeline Leak Detection [J]. Expert System with Application, 2007, 32, 103-113.
[33] Chan C. W. An expert decision support system for monitoring and diagnosis of petroleum production and separation processes [J]. Expert System with Application, 29, 2005, 131-143.
[34] Wang X. W., Qu H.B., Liu P., Cheng Y. Y. A self-learning expert sytem for diagnosis in tradition Chniese Medince [J]. Expert System with Application, 26, 557-566.
[35] Khaled S., Mona El-B., Ahmed R. A multiagent approach for diagnostic expert system via the internet [J]. Expert System with Application, 27, 2004, 1-10.
[36] Jacobo V. H., Ortiz A., Cerrud Y., Schouwenaars R. Hybrid Expert System for the Faitlure Analysis of Mechanical Elements. Engineering Failure Analysis, 2007.02.002.
[37] Leung D., Romagnoli J. Dynamic probabilistic model-based expert system for fault diagnosis [J]. Computers and Chemical Engineering, 2000, 24, 2473-2492.
[38] Leung R.W.K., Lau H.C.W., Kwong C. K. An expert system to support the optimization of ion plating process: an OLPA-based fuzzy-cum-GA approach [J]. Expert System with Application, 2003, 25, 313-330.
[39] Qian Yu, Li X.X, Jiang Y., and Wen Y. Q. An expert system for real-time fault diagnosis of complex chemical processes [J]. Expert Systems with Applications, 2003, 24(4), 425-432.
[40] Qian, Y., Zeng M.J., Li X.X. Implementation of knowledge maintenance modules in an expert system for fault diagnosis of chemical process opration [J]. Expert System with Application,28(2005) 249-257.
[41] Punal A. Roca E. Lema J. M. An expert system for monitoring and diagnosis of diagnosis of anaerobic wastewater treatment plants [J]. Water Research, 2002, 36, 2656-2666.
[42] Batanov D.N., Cheng Z. An object-oriented expert system for fault diagnosis in the ethylene distillation process [J]. Computers in Industry, 1995, 237-249.
[43] Yang B.S., Lim D.S., Tan A. C. C. VIBEX: an expert system for vibration fault diagnosis of ratating machinery using decision tree and decision table [J]. Expert System with Application, 2005, 735-742.
[44] Mandel J. Use of singular value deceposition in regression analysis [J]. American Statistictian, 1982, 36, 15-24.
[45] Kresta J. V., Macgregor J. F., Marlin T. E. Multivariate strtistical monitoring of process operating performance [J]. The Canadian Journal of Chemical Engineering, 69(2), 35-47.
[46] MacGreger J. F., Kourti J. Statistic process and control multivariable processes, Control Engineering Practise [J], 1995, 3, 403-416.
[47] Ku W. F., Storer R. H. Georgakis C. Disturbance detection and islotion by dynamica principal component analysis [J]. Chemometics and Intelligent Laboratory Systems, 1995, 30, 179-196.
[48] Barshi B. R. Mutiscale PCA with application to multivariate statistical process monitoring [J]. AIChE Journal, 1998, 44(7), 1596-1610.
[49] Dong D., McAvoy T. J. Nonlinear principal component analysis based on principal curve and neural networks [J]. Computers and Chemical Engineering, 1996, 20(1), 65-78.
[50] Macgregor J. F., Kiparissides C. J., Koutoudi M. Process monitoring and diagnosis by multiblock methods [J]. AIChe Journal, 1994, 40, 826-839.
[51] Li W., Yue H. H., Valle-Cervantes S., Qin S. J. Recurise PCA for adaptive processmonitoring [J].Journal of Process Control, 2000, 10(5), 471- 486.
[52] Scholkopf B., Smola A. Muller K. R. Nonlinear component analysis as a kernel eigenvalue problem [J]. Neural Computation, 1998, 1299-1319.
[53] Choi S. W., Lee C., Lee J-M., Park J. H., Lee I-B. Fault detection and identification of nonliearn process based on kernel PCA [J]. Chemometics and Intelligent Laboratory Systems, 2005, 75(1), 55-67.
[54] Cho J. H., Lee J. M., Wook Ch. S., Lee D., Lee I-B. Fault diagnosis for process monitoring using kernel principal component analysis [J]. Chemical Engineering Science, 2005, 60(1), 279-288.
[55] Bell A. J., Sejnowski T. J. An information-maximization approach to blind separation and blind deconvolution [J]. Neural Computation, 1995, 7(6), 1004-1034.
[56] Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis [J]. IEEE Trans. On Neural Networks, 1999, 10(3), 626-634.
[57] Bach F. R., Jordan M. I. Kernel independent component analysis [J]. Journal of Machine Learning Research, 2002, (3), 1- 48.
[58] Lee J.M. Yoo C., and Lee I-B. Statistical process monitoring with independent component analysis[J]. J. Process Control. 2004, 8, 467-485.
[59] 陈国金,梁军,钱积新.独立元分析方法及其在化工过程监控和故障诊断中的应用[J]. 化工学报, 2003,54, 1474-1477.
[60] Li R.F., Wang X.Z.. Dimension reduction of process dynamic trends using independent component analysis[J]. Computers and Chemical Engineering, 2002, 26, 467-473.
[61] 张学工. 关于统计学习理论与支持向量机 [J]. 自动化学报, 2000, 26(1), 32- 42.
[62] Vapnik, VN. Statistical learning theory [M]. 许建华等译, 北京, 电子工业出版社, 2004.
[63] Osuna E., Freund R., Girosi F. Training Support Vector Machines, An Application to Face Detection [J]. In Proc. Of CVPR’97 [C], Puerto Rico, 1997.
[64] Platt J.C., Sequential Minimal Optimization: a Fast Algorithm for Training Support Vector Machines [J]. Technical Report MSR-TR-98-14, Microsoft Research, 1998.
[65] Keerthi S. S., Shevade S. K., Bhattacharyya C. et al. A fast Iterative Nearest Point Algorithm to Support Vector Machine Classifier Design [R]. Technical Report TR-ISL-99-03, India: India Institute of Science, 1999
[66] Pavlov D., Mao J., Dom B. Scaling-up Support Vector Machines Using Boosting Algorithm [J]. In Proceedings of the International Conference on Pattern Recognition. ICPR-2000, 2000.
[67] Bradley P. S., Mangasarian O. L. Massive data discrimination via linear support vector machines [R].Technical Report 98-05, Madison, WI:Univ. of Winsconsin, 1998.
[68] Mangasarian O. L., Musicant D. R. Successive Overrelaxation for Support Vector Machines [J]. IEEE Tran. On Neural Networks, 1999, 10, 1032-1037
[69] Ulrich, K. Pairwise classification and support vector machines. In B Scholkopf, C. J. C.Burges, A. J. Smola, editors, Advances in Kernel Methods Support Vector Learning [M]. Cambridge, MA, MIT Press, 1998, 255-268.
[70] Ahmed S. N.,et al. Incremental learning with support vector machines [J]. Workshop on support vector machines, Stockholm, Sweden, August, 2, 1999.
[71] An improved incremental training algorithm for support vector machines using active query [J]. Pattern Recognition, 2007, 40, 964-971
[72] 李昆仑, 黄厚宽, 田盛丰. 模糊多类 SVM 模型 [J]. 电子学报, 2004, 32(5), 830-832.
[73] Chiang L. H., Kotanchek M. E., Kordon A. K. Fault diagnosis based on fisher discriminant analysis and support vector machines [J]. Computers and Chemical Engineering, 2004, 28, 1389-1401
[74] 王华忠, 张雪申, 余金寿. 基于支持向量机的故障诊断方法[J]. 华东理工大学学报, 2004, 30(2), 179-182.
[75] Widodo A., Yang B. S., Han T. Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors [J]. Expert System with Applications, 2007, 32, 299-312.
[76] 吴静, 周建国, 晏浦柳. 支持向量机在网络故障中的应用研究[J]. 计算机工程, 2004, 30(22), 44-46.
[77] Patterson-Hine F. A., Dugan J. B. Modular techniques for dynamic fault-tree analysis [J]. Proceedings Annual Reliability and Maintainability Symposium, 1992, 363-369
[78] Cheung J. T., and Stephanopoulos, G. Representation of process trends part I: A formal representation framework [J]. Computers and Chemical Engineering, 1990, 14(4-5), 495-510.
[79] Janusz M., and Venkatasubramanian V. Automatic generation of qualitative description of process trends for fault detection and diagnosis [J]. Engineering Application of Artificial Intelligence, 1991, 4, 329-339.
[80] Rengaswamy R., and Venkatasubramanian V. A syntactic pattern-recognition approach for process monitoring and fault diagnosis [J]. Engineering Applications of Artificial Intelligence, 1995, 8(1), 35-51.
[81] Vedam H., and Venkatasubramanian V. A wavelet theory-based adaptive trend analysis system for process monitoring and diagnosis [J]. In American control conference, 1997, 309-313.
[82] Vedam H., and Venkatasubramanian V, and Bhalodia M. A B-spline based method for data compression, process monitoring and diagnosis [J]. Computers and Chemical Engineering, 1998(22), S827-S830.
[83] Rengaswamy R., Hagglund T., and Venkatasubramanian V. A qualitative shape analysis formalism for monitoring control loop performance [J]. Engineering Applications of Artificial Intelligence, 2001, 14(1), 23-33. [ 84] Sourabh D, Raghunathan R & Venkatasubramanian V. Fuzzy-logic based trend classification for fault diagnosis of chemical processes [J]. Computers & ChemicalEngineering, 2003, 27(3), 347-362.
[85] Dash S., Venkatasurbramanian V. Challenges in the industrial application of fault diagnostic system [J]. Computers & Chemical Engineering, 2000, 24, 785-791.
[86] Mylaraswamy D., Venkatasubramanian V. A hybrid framework for large scale process fault diagnosis, Computers & Chemical Engineering [J]. 1997, 21, 935-940.
[87] Ruiz D., Nougues J. M., Puigijaner L. Fault diagnosis support system for complex chemical plants [J]. Computers & Chemical Engineering, 2001, 25, 151-160.
[88] Chan C. W. An expert system support system for monitoring and diagnosis of petroleum production and separation process [J]. Expert System with Application, 2005, 29, 131-143.
[89] Yang B. S., Lim D. S., Tan A. C. C. VIBEX: an expert system for vibration fault diagnosis of ratating machinery using decision tree and decision table [J]. Expert System with Application, 2005, 28, 735-742.
[90] http://simusafe.buct.edu.cn/research
[91] Webb A. R. Statistical pattern recognition [M]. Second Edition, 王萍等译,北京,电子工业出版社,2004.
[92] Bakshi B. R. Mutiscale PCA with application to multivariate statistical process monitoring [J]. AIChE J., 1998, 44(7), 1596-1610.
[93] Russell E. L., Chiang L. H., and Braatz R. D. Fault detection in industrial processes using canonical variety analysis and dynamic principal component analysis [J]. Chemometics and Intelligent Laboratory Systems, 2000, 51, 81-93.
[94] Comon P. Independent component analysis- a new concept? [J] Signal Process, 1994, 36(3), 287-314.
[95] Li X. X., Qian Y., and Wang J. F. Process monitoring based on wavelet packet principal component analysis [J]. European Symposium on Computer Aided Process Engineering-13, 2004, 455-460.
[96] Bakshi B. R., Stephanopoulos G. Representation of process trends – III. Multiscale extraction of trends from process data [J]. Computers and Chemical Engineering, 18(4), 1994,267-302
[97] Wang X.Z., Chen B.H., Yang S.H., and McGreavy C. Application of wavelets and neural networks to diagnostic system development- an integrated framework and its application [J]. Computer and Chemical Engineering, 1999, 23, 945-954.
[98] Li, R.F. and Wang X.Z. Dimension reduction of process dynamic trends using independent component analysis [J]. Computer and Chemical Engineering, 2002, 26(3), 467-473.
[99] 姚志湘,钱宇,李秀喜. 多变量化工过程系统的状态空间及其维数的判定准则 [J].高校化学工程学报,2004, 18(1),73-78.
[100] Wilks S. S. Mathematical statistics [M]. Wiley, New York, 1962, 577-578.
[101] Bellnumeur P. N., Hespanha P. J., Kriegam D. J. Eigenfaces vs Fisherfaces: Recognitionusing calss specific linear projection[J]. IEEE Trans. Pattern. Anal. Machine Intell, 1997, 19(7), 711-720.
[102] 杨健, 杨静宇, 金忠. 最优鉴别特征的抽取及图像识别[J]. 计算机研究与发展, 2001, 38(11), 1331-1336.
[103] Dong D., McAvoy T. J. Nonlinear principal component analysis – based on principal curves and neural net works [J]. Computer and Chemical Engineering, 1996, 20(1), 65-78.
[104] Lin W. L., Qian Y., Li X. X. Nonlinear dynamic principal component analysis for on-line process monitoring and diagnosis [J]. Computer and Chemical Engineering, 2000, 24, 423-429.
[105] Li X. X., Qian Y. and Wang J. F. Process monitoring based on wavelet packet principal component analysis [J]. European Symposium on Computer Aided Process Engineering-13, 2004, 455-460.
[106] Baudat G. Anouar A. Generalized discriminate analysis using a kernel approach [J]. Neural Computation, 2000, 12(10), 2385-2404.
[107] Macgregor J. F. Using online process data to improve quality: challenges for statistician [J]. International Statistic Review, 1997, 65, 309-323
[108] 谢磊. 间歇过程统计性能监控[D]. 杭州, 浙江大学, 2005.
[109] Jain A. K., Murty M. N., Flynn P. J. Data clustering: A survey [J]. ACM Computer Survey, 1999, 31, 264-323
[110] Safavian S. R. Landgrebe D. A. A survey of decision tree classifier methodology [J]. IEEE Transaction on Systems, Man, and Cybernetics, 1991, 21(3), 660-674.
[111] Aitkenhead M. J. A co-evolving decision tree classification method [J]. Expert Systems with Application, 2008, 34(1), 18-25
[112] Hoskins J. C., Himmelblau D. M. Artificial neural network models of knowledge representation in chemical engineering [J]. Computer and Chemical Engineering,12(9-10), 881-890
[113] Li X X, Qian Y,Huang Q M, Jing Y B. Multi-Scale ART2 for state Identification of Process Operation Systems [J]. Computer-Aided Chemical Engineering-15, ed. by B.Z. Cheng and A. W. Westerberg, Elsevier, 2004, 523-529
[114] Lewis D. D., Na?ve (Bayes) at forty: The Independence assumption in information retrieval [J]. Proceedings of the 10th European Conference on Machine Learning, New York, 1998, 4-15 [ 115 ] Ramoni M., Sebasiani P. Bayesian methods for intelligent data analysis: An Introduction [M]. New York: Springer, 1999, 129-166
[116] 宫秀军, 孙建平, 史忠值. 主动贝叶斯网络分类器[J]. 计算机研究与发展, 2002, 39(5), 574-579
[117] Mitchell T. M. Machine learning [M]. 曾华军等译,北京,机械工业出版社, 2003.
[118] Han J., Kamber M. Data mining concepts and techniques [M]. 范明等译,北京,电子工业出版社, 2001
[119] Sahan S. Polat K., Kodaz H., Gunes S. A new hybrid method based on fuzzy-aritficical immune system and k-nn algorithm for breast cancer diagnosis [J]. Computers in Biology and Medicine, 2007, 37, 415-423
[120] Pawlak Z. Rough Sets [J]. International Journal of Computer and Information Science, 1982, 11(5), 341-356
[121] 张登峰, 王执铨. 故障特征选择与诊断规则提取的 VPRS 模型方法[J]. 系统仿真学报, 15(6), 793-803
[122] 黄启明. 化工过程集成运行系统与故障诊断的研究[D]. 广州, 华南理工大学博士学位论文, 2001.
[123] Pao Y. H. Adaptive pattern recognition and neural networks [J]. Addison-wesley, 1998. [ 124 ] Marriott S., Harrison R. F. A modified fuzzy ARTMAP architecture for the approximation of noisy mapping [J]. Neural Network, 1995, 8(4), 619
[125] Srinivasa N. Learning and generalization of noisy mapping using a PROBART neural network [J]. IEEE transactions on signal processing, 1997, 45(10), 2533
[126] 田盛丰. 基于核函数的学习算法[J]. 北方交通大学学报, 2003, 27(2), 1-8
[127] Joseph G., Gary R.Expert System Principal and Programming [M]. 印鉴等译, 北京, 机械工业出版社,2000.
[128] 尹朝庆,尹皓.人工智能与专家系统[M]. 北京, 中国水利水电出版社, 2002.
[129] Karmer M. A. Nonlinear principal component analysis using associative neural networks [J]. AIChE Journal, 1991, 37(2), 233-243
[130] Tan S. F., Mavrovounitois M. L. Reducing data dimensionality through optimizing neural network inputs [J]. AIChE Journal, 41(6), 1471-1480
[131] Cristianini N., Tayer S. J. An introduction to support vector machines and other kernel-based learning methods [M], 李国正等译,北京, 电子工业出版社, 2005
[132] Taylor S. J., Cristianini N. Kernel methods for pattern analysis [M], 赵铃铃等译, 北京, 机械工业出版社, 2006
[133] Su Ch. T., Yang Ch. H. Feature selection for the SVM: An application to hypertension diagnosis [J]. Expert System with Application, 2008, 34(1), 754-763.
[134] Xu Y., Zhang D., Song F., Yang J. Y., Jing Zh., Li M. A method for speeding up feature extraction based on KPCA [J]. Neurocomputing, 2007, 70, 1056-1061.
[135] Zheng W., Zou C., Zhao L. An improved algorithm for kernel principal component analysis [J]. Neural Processing Letters, 2005, 22, 49-56
[136] Cao L. J., Chua K. S., Chong W. K., Lee H. P., Gu Q. M. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine [J]. Neurocomputing, 2003, 55, 321-336.
[137] Hyvarinen A., Oja E. Independent component analysis: algorithms and application [J]. Neural Networks, 2000,13(4-5), 411-430
[138] Jang G. J., Yun S. J., Hwan Y. Feature vector transformation using independent component analysis and its application to speaker identification [J]. Proceedings of Eurospeech, 1999, Budapest Hungary, 767-770.
[139] 蒋宁. 基于 ICA_PCA 方法流出工业过程监控与故障诊断的研究[D]. 杭州, 浙江大学博士学位论文.
[140] Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans [J]. on Neural Networks, 10(3), 626-634.
[141] Kocsor A., Csirik J. Fast independent component analysis in kernel feature spaces [J]. SOFSEM, 2000, LNCS 2234, 271-281.
[142] Bach F. R., Jordan M. I. Kernel independent component analysis [J]. Journal of Machine Learning Research, 3, 1- 48.
[143] Kim S. W., John O. B. On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods [J]. Pattern Recongnition, 2004, 37(2), 227-239
[144] Sun L. Zh., Huang D. S., Cheun Y. M. Extracting nonlinear features for multispectral images by FCMC and KPCA [J]. Digital Signal Processing, 2005, 15, 331-346.
[145] Baudat. G., Anouar F. Feaure vector selection and projection using kernels [J]. Neurocomputing, 2003, 55, 21-38.
[146] Suykens J. A. K., Vandevalle, J. Least Squares Support Vector Machine Classifiers [J]. Neural Processing Letters, 1999, 9(3), 293-300.
[147] Suykens J. A. K., Baesens B., Moor B. Benchmarking Least Squares Support Vector Machine Classifiers [J]. Machine Learning, 2004, 54, 5-32
[148] Tsujinishi D., Shigeo Abe. Fuzzy Least Squares Support Vector Machines for Multiclass Problems[J]. Neural Networks, 2003, 785-792
[149] Dietterich T. G., Bakiri G. Solving multiclass learning problems via error-correcting output codes [J]. Journal of Artificial Intelligence Research, 1995, 2, 263-286.
[150] 朱永生, 王成栋, 张优云. 二次损失函数支持向量机性能的研究[J]. 计算机学报, 2003, 26(8), 982-989.
[151] Jooyong, S., Changha, H., Sunkyun, N. Robust LS-SVM Regression Using Fuzzy C-Means Clustering [J]. Advances in Natural Computation, 2006, 4221, 157-166.
[152] 张英,苏宏业,褚健. 基于模糊最小二乘支持向量机的软测量建模 [J]. 控制与决策, 2005, 25(6), 621-624.
[153] Lauer, F., Bloch, G. Incorporating prior knowledge in support vector machines for classification: A review [J]. Neurocomputing, 2007, doi:10.1016, 1-17.
[154] Suykens J.A.K., De Brabanter J., Lukas L., Vandewalle J., Weighted least squares support vector machines : robustness and sparse approximation [J]. Neurocomputing, Special issue on fundamental and information processing aspects of neurocomputing, vol. 48, no. 1-4, Oct. 2002, pp. 85-105
[155] Lin, C. F., Wang, S. D. Fuzzy Support Vector Machines [J]. IEEE Transaction on Neural Networks, 2002, 13(2), 464-471.
[156] Huang H. P., Liu, Y. H. Fuzzy Support Vector Machines for Pattern Recognition and Data Mining [J]. International Journal of Fuzzy Systems, 2002, 4(3), 826-835.
[157] Liu, F. C., Wang, D. S. Training Algorithms for Fuzzy Support Vector Machines with Noisy Data [J]. Pattern recognition letters, 2004, 25, 1647-1656.
[158] 张翔, 肖小玲, 徐光佑. 基于样本之间紧密度的模糊支持向量机方法 [J]. 软件学报, 2006, 17(5), 951-958.
[159] David, M. J. T., Robert, P. W. D. Support Vector Machines [J]. Pattern recognition letters, 1999, 20, 1191-1199. [ 160 ] Matsatsinis, N.F., Doumpos, M & Zopounidis. Knowledge acquisition and representation for expert systems in the field of financial ayalysis. Expert System with Application, 1997, 12, 247-262.
[161] Mansingh, G., Reichgelt, H., Osei-Bryson, K.-M. CPEST: An expert system for the management of pests and diseases in the Jamaican coffee industry. Expert System with Application, 2007, 32, 184-192.
[162] 史定华, 王松瑞. 故障树分析技术方法和理论[M]. 北京, 北京师范大学出版社, 1993.
[163] 于海文. 基于故障树分析法的故障诊断专家系统[M]. 中国空间科学技术, 1998, (1), 37-44.
[164] Quinlan, J. R. Quinlan, J. R. Induction of decision trees [J]. Machine Learning, 1986, 1, 81-106.
[165] Quinlan, J .R. C4.5: programs for machine leanings [J]. Canada: Morgan Kaufmann, 1993.
[166] 蒋祖华,苏海. 工程设计类知识管理类技术研究 [J]. 计算机集成制造系统,2004, 10(10), 1225-1232.
[167] William F., William T. Data Structures with C++ [M]. BeiJing: Tsinghua University Press. 2001, 579-599.
[168] Adam, D. Data Structures and Algorithms in C++ [M]. Thomsm Learning, Inc, 2003.
[169] 尹朝庆,尹浩. 人工智能与专家系统[M].北京: 中国水利水电出版社,2002.
[170] 何华灿.人工智能导论 [M].西安:西北工业大学出版社, 1988.
[171] 水天德. 现代润滑油生产工艺第 1 版. 中国石化出版社. 1997,6-15
[172] 李志英等.润滑油生产技术问答.第 1 版. 茂名石化南海高级润滑油公司,1999,177-192,193-239
[173] 重质糠醛精制装置操作规程. 中国石化公司茂名分公司,2004.
[174] 重质酮苯脱蜡装置操作规程. 中国石化公司茂名分公司,2004.
[175] 薛福珍, 林盛荣, 唐琰. 基于 OPC 数据访问规范的客户端软件研究与开发[J]. 计算机工程. 2002, 28(4), 229-231