多尺度方法在连续过程多变量监测中的应用研究
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摘要
保障生产安全和减小产品质量波动一直是工业过程的两个主题。随着现代工业过程日趋大型化和复杂化,人们迫切需要提高系统的可靠性和安全性,因此过程的故障监测和诊断成为研究的重点之一。统计过程监测是一种基于数据驱动(data driven)的方法,由于不依赖于精确的数学模型,仅依赖于易得的过程数据,因而具有重要的理论价值和广泛的应用价值。
本文所作的工作是:以主元分析(PCA)方法为主线,引入了小波分析、多变量累计和等方法,针对不同工业过程对象的特点,使用不同的统计方法进行监测,并且提出了新的故障监测算法。完成的具体工作如下:
1) PCA方法的重油催化装置结焦故障的早期监测与诊断
当前石化工业因结焦而导致的生产事故呈上升趋势,已成为影响装置长周期稳定运行的主要因素。主元分析是一种能够对过程进行监测和诊断的有效方法。以宁夏某炼油厂90万吨/年的重油催化裂化装置为例,通过对历史数据进行多次的分析与比较,选择能够代表过程信息变化的11个重要变量,从而简化了过程数据处理的复杂度。然后建立主元模型,结合多变量统计控制图进行故障监测,并运用平均贡献图直观、明确地判别出引起故障的主要原因。
本文通过对一个典型的重油催化裂化装置的监测表明,PCA方法能有效地对结焦故障进行早期的监测及诊断,提醒操作人员采取相应措施,阻止结焦的进一步发展,避免结焦严重所造成的停炉损失。
2) MCUSUM-MSPCA方法对缓变故障的监测
针对化工过程中难以监测到的微小偏移性故障,提出了一种新的基于多变量统计过程的监测方法。把传统的单变量累计和控制图(CUSUM)扩展为多变量的形式,通过累计作用提取过程的趋势变化,并与小波变换提取测量变量内在时频特征的特性,以及传统的主元分析(PCA)去除变量间关联的优势相结合,构成新的多变量累计和多尺度主元分析(MCUSUM-MSPCA)方法。通过对TE过程的仿真研究,验证了该方法的可行性和有效性。与PCA方法相比,MCUSUM-MSPCA方法能在不同频率范围内,有效、及时地监测到过程中的缓变故障。
而且通过多次的实验研究与比较,得到了最优的条件进行建模。在故障发生后极短的时间内,新方法能够迅速、有效地监测到异常状况,极大地改善了对该过程缓变故障的监测效果,提高了过程监测的灵敏性。
The safety of operation and consistency of product quality are always two themes of the process industry. In modern process industries, with the rapid development of mass production and complexity, reliability and security are being greatly addressed to avoid large economical loss resulted from accidents and abonormal breakdowns of industrial productions. Therefore, the processing monitoring is becoming one of the most active research areas in process control. Statistical process monitoring (SPM) are data-driven methods, which relies only on the historic process data and do not require any form of model information, they have been paid more attention.
This thesis mainly applied principal component analysis (PCA), along with the wavelet transform and multi-variable cumulative sum (MCUSUM). For different industrial processes objects, certian improvements to traditional PCA have been made, and a new integrated algorithms of fault detection are also proposed. The work includes:
1) PCA-based early fault detection and diagnosis of oil refining
Coking has been a big problem in process industry, which usually takes a relative long time to develop and is hard to detect in early stage, but can result in facility shutdown. Operation cost and facility capacity will be negatively affected. PCA is an effective method for process monitoring and fault diagnosis. It can efficiently eliminate correlation among process variables and reduce the influence of random noise and disturbance in system, while also keeps important characteristics in original data collected for a complex industrial process modeling. Based on a principle component model, fault detection and diagnosis analysis are carried on in an oil refinary process with multivariate statistical techniques such as principle scores plot, Q residuals plot and contributions plot. The test results show that PCA is an efficient method to monitor the performance of the process, and can detect faults in early stage, so as to avoid facility shutdown, and reduce the operation cost and stabilize the product quality.
2) MCUSUM-MSPCA based small shift monitoring in TE process
In order to detect the process deviation initiated by gradual small shifts, a new multivariate statistical process monitoring method is proposed, which extends the conventional cumulative sum (CUSUM) for single variable to multi-variable case (MCUSUM) and further combines it with wavelet transform and principal component analysis (PCA) to form MCUSUM-MSPCA. The result shows that the process monitoring performance is significantly improved by proposed method. Compared with conventional PCA, MCUSUM-MSPCA can effectively detect variations at different resolutions, and make the process monitoring more reliable and prompt.
Finally, the dissertation is concluded with a summary and some remain challenges.
本文所作的工作是:以主元分析(PCA)方法为主线,引入了小波分析、多变量累计和等方法,针对不同工业过程对象的特点,使用不同的统计方法进行监测,并且提出了新的故障监测算法。完成的具体工作如下:
1) PCA方法的重油催化装置结焦故障的早期监测与诊断
当前石化工业因结焦而导致的生产事故呈上升趋势,已成为影响装置长周期稳定运行的主要因素。主元分析是一种能够对过程进行监测和诊断的有效方法。以宁夏某炼油厂90万吨/年的重油催化裂化装置为例,通过对历史数据进行多次的分析与比较,选择能够代表过程信息变化的11个重要变量,从而简化了过程数据处理的复杂度。然后建立主元模型,结合多变量统计控制图进行故障监测,并运用平均贡献图直观、明确地判别出引起故障的主要原因。
本文通过对一个典型的重油催化裂化装置的监测表明,PCA方法能有效地对结焦故障进行早期的监测及诊断,提醒操作人员采取相应措施,阻止结焦的进一步发展,避免结焦严重所造成的停炉损失。
2) MCUSUM-MSPCA方法对缓变故障的监测
针对化工过程中难以监测到的微小偏移性故障,提出了一种新的基于多变量统计过程的监测方法。把传统的单变量累计和控制图(CUSUM)扩展为多变量的形式,通过累计作用提取过程的趋势变化,并与小波变换提取测量变量内在时频特征的特性,以及传统的主元分析(PCA)去除变量间关联的优势相结合,构成新的多变量累计和多尺度主元分析(MCUSUM-MSPCA)方法。通过对TE过程的仿真研究,验证了该方法的可行性和有效性。与PCA方法相比,MCUSUM-MSPCA方法能在不同频率范围内,有效、及时地监测到过程中的缓变故障。
而且通过多次的实验研究与比较,得到了最优的条件进行建模。在故障发生后极短的时间内,新方法能够迅速、有效地监测到异常状况,极大地改善了对该过程缓变故障的监测效果,提高了过程监测的灵敏性。
The safety of operation and consistency of product quality are always two themes of the process industry. In modern process industries, with the rapid development of mass production and complexity, reliability and security are being greatly addressed to avoid large economical loss resulted from accidents and abonormal breakdowns of industrial productions. Therefore, the processing monitoring is becoming one of the most active research areas in process control. Statistical process monitoring (SPM) are data-driven methods, which relies only on the historic process data and do not require any form of model information, they have been paid more attention.
This thesis mainly applied principal component analysis (PCA), along with the wavelet transform and multi-variable cumulative sum (MCUSUM). For different industrial processes objects, certian improvements to traditional PCA have been made, and a new integrated algorithms of fault detection are also proposed. The work includes:
1) PCA-based early fault detection and diagnosis of oil refining
Coking has been a big problem in process industry, which usually takes a relative long time to develop and is hard to detect in early stage, but can result in facility shutdown. Operation cost and facility capacity will be negatively affected. PCA is an effective method for process monitoring and fault diagnosis. It can efficiently eliminate correlation among process variables and reduce the influence of random noise and disturbance in system, while also keeps important characteristics in original data collected for a complex industrial process modeling. Based on a principle component model, fault detection and diagnosis analysis are carried on in an oil refinary process with multivariate statistical techniques such as principle scores plot, Q residuals plot and contributions plot. The test results show that PCA is an efficient method to monitor the performance of the process, and can detect faults in early stage, so as to avoid facility shutdown, and reduce the operation cost and stabilize the product quality.
2) MCUSUM-MSPCA based small shift monitoring in TE process
In order to detect the process deviation initiated by gradual small shifts, a new multivariate statistical process monitoring method is proposed, which extends the conventional cumulative sum (CUSUM) for single variable to multi-variable case (MCUSUM) and further combines it with wavelet transform and principal component analysis (PCA) to form MCUSUM-MSPCA. The result shows that the process monitoring performance is significantly improved by proposed method. Compared with conventional PCA, MCUSUM-MSPCA can effectively detect variations at different resolutions, and make the process monitoring more reliable and prompt.
Finally, the dissertation is concluded with a summary and some remain challenges.
引文
[1]Venkatasubramanian V,Rengaswamy R,Kavuri S N,Yin K.A review of process fault detection and diagnosis(Part Ⅲ)[J].Computer & Chemical Engineering,2003,27(3):327-346
[2]Calandranis J,Stephanopoulos G,Nunokawa S.On-line performance monitoring and diagnosis [J].Chemical Engineering Progress,1990,86(10):60-68
[3]Nimmo I.Adequately Addressing abnormal operations[J].Chemical Engineering Progress,1995,91(9):36-45
[4]Laser M.Recent safety and environmental legislation[J].Trans.IchemE,2000,78:419-422
[5]Chiang L H,Russell E L,Braatz R D.Fault detection and diagnosis in industrial systems.Springer-Verlag,London,2000
[6]周东华,孙优贤.控制系统的故障检测与诊断[M].北京:清华大学出版社,1994
[7]赵翔.故障诊断技术的研究现状与发展趋势[J].机床与液压,2002,4:3-6
[8]黄文虎,夏松波,刘瑞岩.设备故障诊断原理技术及应用[M].北京:科学出版社,1996
[9]高洪波.智能故障诊断中综合信号处理的研究[D].西安:西北工业大学,2004
[10]Frank P M.Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—a survey and some new results[J].Automatic,1990,26(3):459-747
[11]Venkatasubramanian V,Rengaswamy R,Yin K,Kavuri S N.A review of process fault detection and diagnosis:Part Ⅰ:Quantitative model-based methods[J].Computers&Chemical Engineering,2003,27(3):293-311
[12]Patton R J,Chert J.Review of Parity Space Approaches to Fault Diagnosis for Aerospace System [J].Journal of Guidance,Control and Dynamics,1994,17(2):278-285
[13]Michel K.Fault diagnosis based on analytical models for linear and nonlinear systems-A tutorial [J].Proc.SAFEPROCESS,2003,37-50
[14]Frank P M,Ding S X.Survey of robust residual generation and evaluation methods in observer-based fault detection systems[J].Process Control,1997,7(6):403-424
[15]Patton R J,Chen J.A review of parity space:approaches to fault diagnosis[J].Proceedings of IFAC Fault Detection,Supervision and Safety for Technical,Germany,1991,65-81
[16]Isermann R.Fault diagnosis of machines via parameter estimation and knowledge processing -tutorial paper[J].Automatica,1993,29(4):815-835
[17]Rehbein D.Expert System in process control[J].ISA Trans,1992,31(2):49-55
[18]闻新,张洪钺,周露.控制系统的故障诊断和容错控制[M].北京:机械工业出版社.1998
[19]Bernieri A,Apuzzo M D,Sansone L,et al.A neural network approach for identification and fault diagnosis on dynamic system[J].IEEE Transactions on Instrument and Measurement,1994,43(6):867-873
[20]Peter B.Fuzzy-model-based parity equations for fault isolation[J].Control Engineering Practice,1999,7(2):261-270
[21]陈玉东,施颂椒,翁正新.动态系统的故障诊断方法综述[J].化工自动化及仪表,2001,28(3):1-14
[22]Dokas I M,Karras D A,Panagiotakopoulos D C.Fault tree analysis and fuzzy expert systems:Early warning and emergency response of landfill operations[J].Environmental Modelling &Software,2009,24(1):8-25
[23]Davis J F,Bakshi B R,Kosanovich K A,Piovoso M J.Process monitoring,data analysis and data interpretation[J].AIChE Symposium Series,1996,92:1-11
[24]Li W,Qin S J.Consistent dynamic PCA based on errors invariables subspace identification[J].Process Control,2001,11(6):661-678
[25]Kruger U,Zhou Y Q,Irwin G W.Improved principal component monitoring of large-scale processes[J].Process Control,2004,14(8):879-888
[26]Huang Y,Gertler J,McAvoy T J.Sensor and actuator fault isolation by structured partial PCA with nonlinear extensions[J].Process Control,2000,10(5):459-469
[27]Lopes J A,Menezes J C.Multivariate monitoring of fermentation processes with nonlinear modelling methods[J].Analytica Chimica Acta,2004,515(1):101-108
[28]李尔国,俞金寿.一种基于输入训练神经网络的非线性PCA故障诊断方法[J].控制与决策,2003,18(2):102-105
[29]Rosen C,Lennox J A.Multivariate and multi-scale monitoring ofwastewater treatment operation [J].Wat.Res.,2001,35(14):3402-3410
[30]Dayal B S,MacGregor J E Improved PLS algorithms[J].Chemometrics,1997,11(1):73-85
[31]Li W H,Yue H H,Valle-Cervantes S,Qin S J.Recursive PCA for adaptive process monitoring [J].Process Control,2000,10(5):471-486
[32]Misra M,Yue H H,Qin S J,Ling C.Multivariate process monitoring and fault diagnosis by mufti-scale PCA[J].Computer Chemical Engineering,2002,26(9):1281-1293
[33]Bakshi B R.Multi-scale PCA with application to multivariate statistical process monitoring[J].AIChE,1998,44(7):1596-1610
[34]Yoon S,MacGregor J F.Principal-component analysis of multi-scale data for process monitoring and fault diagnosis[J].AIChE,2004,50(11):2891-2903
[35]Wang D,Romagnoli J A.Robust multi-scale principal components analysis with applications to process monitoring[J].Process Control,2005,15(8):869-882
[36]Geng Z Q,Zhu Q X.Multi-scale nonlinear principal component analysis and its application for chemical process monitoring[J].Industrial and Engineering Chemistry Research,2005,44(10):3585-3593
[37]Wold S,Kettaneh N,Tjessem K.Hierarchical multi-block PLS and PC models for easier interpretation and as an alternative to variable selection[J].Chemometrics,1996,10(5-6):463-482[38]Wangen L E,Kowalski B R.A multi-block partial least squares algorithm for investigating complex chemical systems[J].Chemometrics,1989,3(1):3-20
[39]Westerhuis J A,Kourti T,MacGregor J E Analysis of multi-block and hierarchical PCA and PLS models[J].Chemometrics,1998,12(5):301-321
[40]王海清.工业过程监测:基于小波和统计学的方法[D].浙江大学博士学位论文,2000
[41]Young R.An introduction to nonlinear Fourier series[M].New:Academic Press,1980
[42]Morlet J.Wave propation and sampling theory and complex waves[J].Geophysics.1982,47(2):222-236
[43]Daubechies I.Orthonormal bases of compactly supported wavelets[J].Comm.On Pure and Application Math.,1988,41(7):909-996
[44]Mallat S.Multifrequency channel decompositions of images and wavelet representation[J].IEEE Trans.on Acoustics Speech And Signal Processing,1989,37(12):2091-2110
[45]杨福生.小波变换的工程分析与应用[M].北京:科学出版社,1999
[46]Mallat S.A theory for multi-resolution signal decompositon:the wavelet representation[J].IEEE Trans on PAMI,1989,11(7):674-693
[47]胡昌华.基于MATLAB 6.X的系统分析与设计一小波分析[M].西安:西安电子科技大学出版社,2004
[48]Page E S.Continuous inspection scheme[J].Biometrika,1954,41:100-104
[49]Hawkins D M.A CUSUM for a scale parameter[J].Quality Technology,1981,13(4):228-231
[50]Thomas P R.Statistical method for quality improvement[M].New York:John Wiley &Sons Inc,1989,102-118
[51]Montgomery D C.Introduction to Statistical Quality Control[M].New York:John Wiley & Sons Inc,1996,314-330
[52]张杰,阳宪惠.多元统计过程控制[M].北京:化学工业出版社,2000
[53]Hotelling H.Analysis of a complex of statistical variables into principal components[J].Educational Psychology,1933,24(6):417-441
[54]Dunio R,Qin S J.Subspace approach to multidimensional fault identification and reconstruction [J].AIChE,1998,44(8):1813-1831
[55]Qin S J,Dunia R.Determining the number of principal components for best reconstruction[J].Journal of Process Control,2000,10(2-3):245-250
[56]王海清,余世明.基于故障诊断性能优化的主元个数选取方法[J].化工学报,2004,55(2):214-219
[57]Hwang D S,Chang S K,Hsu P L.A practical design for a robust fault detection and isolation system[J].International Journal of System Science,1997,28(3),265-275
[58]Wold S.Cross-validatory estimation of the number of components in factor and principal components models[J].Technometrics,1978,20(4):397-405
[59]Gertler J,Monajemy R.Generating directional residuals with dynamic parity relations[J].Automatica,1995,31(4):627-635
[60]李鹏.催化裂化装置结焦问题的探讨[J].石油炼制与化工,2003,34(4):31-34
[61]王建军.当前炼油企业设备故障的特点及对策[J].石油化工设备技术,2007,28(3):18-21
[62]Ghazzaw A,Lennox B.Monitoring a complex refining process using multivariate statistics[J].Control Engineering Practice.2008,16(3):294-307
[63]Kano M,Nagao K,et al.Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem[J].Computers and Chemical Engineering.2002,26(2):161-174
[64]Qin S J.Statistical process monitoring:basics and beyond[J].Chemometrics.2003,17(8-9):480-502
[65]Ningyun Lu,Fuli Wang,and Furong Gao.Combination method of principal component and wavelet analysis for multivariate process monitoring and fault diagnosis[J].Ind.Eng.Chem.Res.,2003,42(18):4198-4207
[66]Wang D,Romagnoli J A.Robust multi-scale principal components analysis with applications to process monitoring[J].Process Control,2005,15(8):869-882
[67]徐涛,王祁.基于小波变换的多尺度主元分析在传感器故障诊断中的应用[J].测试技术学报,2006,20(5):418-423
[68]Maurya M R,Rengaswamy R,Venkatasubramanian V.Fault diagnosis by qualitative trend analysis of the principal components[J].Chemical Engineering Research and Design,2005,83(9):1122-1132
,[69]Jinsong Zhao,Jianchao Huang,Wei Sun.On-line early fault detection and diagnosis of municipal solid waste incinerators[J].Waste Management,2008,28(11):2406-2414
[70]葛志强,宋执环.一种新的多工况过程在线监测方法[J].化工学报,2008,59(1):135-141
[71]张新荣,熊伟丽,徐保国.基于Q统计量分量的故障检测算法研究[J].计算机与应用化学学报,2008,25(12):1537-1542
[72]Misra M,Yue H H,Qin S J,Ling C.Multivariate process monitoring and fault diagnosis by multi-scale PCA[J].Computers.and Chemical Engineering,2002,26(9):1281-1293
[73]Kadlec P,Gabrys B,Strandt S.Data-driven Soft Sensors in the process industry[J].Computers & Chemical Engineering,2009,33(4):795-814
[74]Lieftucht D,Kruger U,Irwin G W.Improved reliability in diagnosing faults using multivariate statistics [J]. Computers and Chemical Engineering, 2006, 30(5): 901- 912
[75] Kano M, Nakagawa Y. Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry [J]. Computers and Chemical Engineering, 2008, 32(1-2): 12-24
[76] Lieftucht D, Volker M, Sonntag C, Kruger U. Improved fault diagnosis in multivariate systems using regression-based reconstruction [J].Control Engineering Practice, 2009, 17(4): 478-493
[77] Zhiqiang Ge, Chunjie Yang, Zhihuan Song. Improved kernel PCA-based monitoring approach for nonlinear processes [J].Chemical Engineering Science, 2009, 64(9): 2245-2255
[78] Yingwei Zhang. Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM[J].Chemical Engineering Science, 2009, 64(5): 801-811
[79] Downs, J. J. and Vogel, E.F. A plant-wide industrial process control problem [J].Computers and chemical Engineering, 1993, 17(3): 245-255
[80] Verron S, Tiplica T, Kobi A. Fault detection and identification with a new feature selection based on mutual information [J]. Journal of Process Control, 2008, 18(5): 479- 490
[81] Chen G, McAvoy T J. Predictive on-line monitoring of continuous processes [J]. Journal of Process Control, 1998, 8(5-6): 409-420
[82] Lee J M, Yoo C K, Lee I B. Statistical process monitoring with independent component analysis [J]. Journal of Process Control, 2004, 14(5): 467-485
[2]Calandranis J,Stephanopoulos G,Nunokawa S.On-line performance monitoring and diagnosis [J].Chemical Engineering Progress,1990,86(10):60-68
[3]Nimmo I.Adequately Addressing abnormal operations[J].Chemical Engineering Progress,1995,91(9):36-45
[4]Laser M.Recent safety and environmental legislation[J].Trans.IchemE,2000,78:419-422
[5]Chiang L H,Russell E L,Braatz R D.Fault detection and diagnosis in industrial systems.Springer-Verlag,London,2000
[6]周东华,孙优贤.控制系统的故障检测与诊断[M].北京:清华大学出版社,1994
[7]赵翔.故障诊断技术的研究现状与发展趋势[J].机床与液压,2002,4:3-6
[8]黄文虎,夏松波,刘瑞岩.设备故障诊断原理技术及应用[M].北京:科学出版社,1996
[9]高洪波.智能故障诊断中综合信号处理的研究[D].西安:西北工业大学,2004
[10]Frank P M.Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—a survey and some new results[J].Automatic,1990,26(3):459-747
[11]Venkatasubramanian V,Rengaswamy R,Yin K,Kavuri S N.A review of process fault detection and diagnosis:Part Ⅰ:Quantitative model-based methods[J].Computers&Chemical Engineering,2003,27(3):293-311
[12]Patton R J,Chert J.Review of Parity Space Approaches to Fault Diagnosis for Aerospace System [J].Journal of Guidance,Control and Dynamics,1994,17(2):278-285
[13]Michel K.Fault diagnosis based on analytical models for linear and nonlinear systems-A tutorial [J].Proc.SAFEPROCESS,2003,37-50
[14]Frank P M,Ding S X.Survey of robust residual generation and evaluation methods in observer-based fault detection systems[J].Process Control,1997,7(6):403-424
[15]Patton R J,Chen J.A review of parity space:approaches to fault diagnosis[J].Proceedings of IFAC Fault Detection,Supervision and Safety for Technical,Germany,1991,65-81
[16]Isermann R.Fault diagnosis of machines via parameter estimation and knowledge processing -tutorial paper[J].Automatica,1993,29(4):815-835
[17]Rehbein D.Expert System in process control[J].ISA Trans,1992,31(2):49-55
[18]闻新,张洪钺,周露.控制系统的故障诊断和容错控制[M].北京:机械工业出版社.1998
[19]Bernieri A,Apuzzo M D,Sansone L,et al.A neural network approach for identification and fault diagnosis on dynamic system[J].IEEE Transactions on Instrument and Measurement,1994,43(6):867-873
[20]Peter B.Fuzzy-model-based parity equations for fault isolation[J].Control Engineering Practice,1999,7(2):261-270
[21]陈玉东,施颂椒,翁正新.动态系统的故障诊断方法综述[J].化工自动化及仪表,2001,28(3):1-14
[22]Dokas I M,Karras D A,Panagiotakopoulos D C.Fault tree analysis and fuzzy expert systems:Early warning and emergency response of landfill operations[J].Environmental Modelling &Software,2009,24(1):8-25
[23]Davis J F,Bakshi B R,Kosanovich K A,Piovoso M J.Process monitoring,data analysis and data interpretation[J].AIChE Symposium Series,1996,92:1-11
[24]Li W,Qin S J.Consistent dynamic PCA based on errors invariables subspace identification[J].Process Control,2001,11(6):661-678
[25]Kruger U,Zhou Y Q,Irwin G W.Improved principal component monitoring of large-scale processes[J].Process Control,2004,14(8):879-888
[26]Huang Y,Gertler J,McAvoy T J.Sensor and actuator fault isolation by structured partial PCA with nonlinear extensions[J].Process Control,2000,10(5):459-469
[27]Lopes J A,Menezes J C.Multivariate monitoring of fermentation processes with nonlinear modelling methods[J].Analytica Chimica Acta,2004,515(1):101-108
[28]李尔国,俞金寿.一种基于输入训练神经网络的非线性PCA故障诊断方法[J].控制与决策,2003,18(2):102-105
[29]Rosen C,Lennox J A.Multivariate and multi-scale monitoring ofwastewater treatment operation [J].Wat.Res.,2001,35(14):3402-3410
[30]Dayal B S,MacGregor J E Improved PLS algorithms[J].Chemometrics,1997,11(1):73-85
[31]Li W H,Yue H H,Valle-Cervantes S,Qin S J.Recursive PCA for adaptive process monitoring [J].Process Control,2000,10(5):471-486
[32]Misra M,Yue H H,Qin S J,Ling C.Multivariate process monitoring and fault diagnosis by mufti-scale PCA[J].Computer Chemical Engineering,2002,26(9):1281-1293
[33]Bakshi B R.Multi-scale PCA with application to multivariate statistical process monitoring[J].AIChE,1998,44(7):1596-1610
[34]Yoon S,MacGregor J F.Principal-component analysis of multi-scale data for process monitoring and fault diagnosis[J].AIChE,2004,50(11):2891-2903
[35]Wang D,Romagnoli J A.Robust multi-scale principal components analysis with applications to process monitoring[J].Process Control,2005,15(8):869-882
[36]Geng Z Q,Zhu Q X.Multi-scale nonlinear principal component analysis and its application for chemical process monitoring[J].Industrial and Engineering Chemistry Research,2005,44(10):3585-3593
[37]Wold S,Kettaneh N,Tjessem K.Hierarchical multi-block PLS and PC models for easier interpretation and as an alternative to variable selection[J].Chemometrics,1996,10(5-6):463-482[38]Wangen L E,Kowalski B R.A multi-block partial least squares algorithm for investigating complex chemical systems[J].Chemometrics,1989,3(1):3-20
[39]Westerhuis J A,Kourti T,MacGregor J E Analysis of multi-block and hierarchical PCA and PLS models[J].Chemometrics,1998,12(5):301-321
[40]王海清.工业过程监测:基于小波和统计学的方法[D].浙江大学博士学位论文,2000
[41]Young R.An introduction to nonlinear Fourier series[M].New:Academic Press,1980
[42]Morlet J.Wave propation and sampling theory and complex waves[J].Geophysics.1982,47(2):222-236
[43]Daubechies I.Orthonormal bases of compactly supported wavelets[J].Comm.On Pure and Application Math.,1988,41(7):909-996
[44]Mallat S.Multifrequency channel decompositions of images and wavelet representation[J].IEEE Trans.on Acoustics Speech And Signal Processing,1989,37(12):2091-2110
[45]杨福生.小波变换的工程分析与应用[M].北京:科学出版社,1999
[46]Mallat S.A theory for multi-resolution signal decompositon:the wavelet representation[J].IEEE Trans on PAMI,1989,11(7):674-693
[47]胡昌华.基于MATLAB 6.X的系统分析与设计一小波分析[M].西安:西安电子科技大学出版社,2004
[48]Page E S.Continuous inspection scheme[J].Biometrika,1954,41:100-104
[49]Hawkins D M.A CUSUM for a scale parameter[J].Quality Technology,1981,13(4):228-231
[50]Thomas P R.Statistical method for quality improvement[M].New York:John Wiley &Sons Inc,1989,102-118
[51]Montgomery D C.Introduction to Statistical Quality Control[M].New York:John Wiley & Sons Inc,1996,314-330
[52]张杰,阳宪惠.多元统计过程控制[M].北京:化学工业出版社,2000
[53]Hotelling H.Analysis of a complex of statistical variables into principal components[J].Educational Psychology,1933,24(6):417-441
[54]Dunio R,Qin S J.Subspace approach to multidimensional fault identification and reconstruction [J].AIChE,1998,44(8):1813-1831
[55]Qin S J,Dunia R.Determining the number of principal components for best reconstruction[J].Journal of Process Control,2000,10(2-3):245-250
[56]王海清,余世明.基于故障诊断性能优化的主元个数选取方法[J].化工学报,2004,55(2):214-219
[57]Hwang D S,Chang S K,Hsu P L.A practical design for a robust fault detection and isolation system[J].International Journal of System Science,1997,28(3),265-275
[58]Wold S.Cross-validatory estimation of the number of components in factor and principal components models[J].Technometrics,1978,20(4):397-405
[59]Gertler J,Monajemy R.Generating directional residuals with dynamic parity relations[J].Automatica,1995,31(4):627-635
[60]李鹏.催化裂化装置结焦问题的探讨[J].石油炼制与化工,2003,34(4):31-34
[61]王建军.当前炼油企业设备故障的特点及对策[J].石油化工设备技术,2007,28(3):18-21
[62]Ghazzaw A,Lennox B.Monitoring a complex refining process using multivariate statistics[J].Control Engineering Practice.2008,16(3):294-307
[63]Kano M,Nagao K,et al.Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem[J].Computers and Chemical Engineering.2002,26(2):161-174
[64]Qin S J.Statistical process monitoring:basics and beyond[J].Chemometrics.2003,17(8-9):480-502
[65]Ningyun Lu,Fuli Wang,and Furong Gao.Combination method of principal component and wavelet analysis for multivariate process monitoring and fault diagnosis[J].Ind.Eng.Chem.Res.,2003,42(18):4198-4207
[66]Wang D,Romagnoli J A.Robust multi-scale principal components analysis with applications to process monitoring[J].Process Control,2005,15(8):869-882
[67]徐涛,王祁.基于小波变换的多尺度主元分析在传感器故障诊断中的应用[J].测试技术学报,2006,20(5):418-423
[68]Maurya M R,Rengaswamy R,Venkatasubramanian V.Fault diagnosis by qualitative trend analysis of the principal components[J].Chemical Engineering Research and Design,2005,83(9):1122-1132
,[69]Jinsong Zhao,Jianchao Huang,Wei Sun.On-line early fault detection and diagnosis of municipal solid waste incinerators[J].Waste Management,2008,28(11):2406-2414
[70]葛志强,宋执环.一种新的多工况过程在线监测方法[J].化工学报,2008,59(1):135-141
[71]张新荣,熊伟丽,徐保国.基于Q统计量分量的故障检测算法研究[J].计算机与应用化学学报,2008,25(12):1537-1542
[72]Misra M,Yue H H,Qin S J,Ling C.Multivariate process monitoring and fault diagnosis by multi-scale PCA[J].Computers.and Chemical Engineering,2002,26(9):1281-1293
[73]Kadlec P,Gabrys B,Strandt S.Data-driven Soft Sensors in the process industry[J].Computers & Chemical Engineering,2009,33(4):795-814
[74]Lieftucht D,Kruger U,Irwin G W.Improved reliability in diagnosing faults using multivariate statistics [J]. Computers and Chemical Engineering, 2006, 30(5): 901- 912
[75] Kano M, Nakagawa Y. Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry [J]. Computers and Chemical Engineering, 2008, 32(1-2): 12-24
[76] Lieftucht D, Volker M, Sonntag C, Kruger U. Improved fault diagnosis in multivariate systems using regression-based reconstruction [J].Control Engineering Practice, 2009, 17(4): 478-493
[77] Zhiqiang Ge, Chunjie Yang, Zhihuan Song. Improved kernel PCA-based monitoring approach for nonlinear processes [J].Chemical Engineering Science, 2009, 64(9): 2245-2255
[78] Yingwei Zhang. Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM[J].Chemical Engineering Science, 2009, 64(5): 801-811
[79] Downs, J. J. and Vogel, E.F. A plant-wide industrial process control problem [J].Computers and chemical Engineering, 1993, 17(3): 245-255
[80] Verron S, Tiplica T, Kobi A. Fault detection and identification with a new feature selection based on mutual information [J]. Journal of Process Control, 2008, 18(5): 479- 490
[81] Chen G, McAvoy T J. Predictive on-line monitoring of continuous processes [J]. Journal of Process Control, 1998, 8(5-6): 409-420
[82] Lee J M, Yoo C K, Lee I B. Statistical process monitoring with independent component analysis [J]. Journal of Process Control, 2004, 14(5): 467-485