基于共同趋势模型的非平稳过程在线监控

doi:10.11949/j.issn.0438-1157.20160670

化工学报 ›› 2017, Vol. 68 ›› Issue (1): 178-187.DOI: 10.11949/j.issn.0438-1157.20160670

基于共同趋势模型的非平稳过程在线监控

林原灵, 陈前

南京航空航天大学机械结构力学及控制国家重点实验室, 江苏南京 210016

收稿日期:2016-05-16 修回日期:2016-09-25 出版日期:2017-01-05 发布日期:2017-01-05
通讯作者: 陈前
基金资助:
江苏高校优势学科建设工程资助项目。

Online non-stationary process monitoring by common trends model

LIN Yuanling, CHEN Qian

State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China

Received:2016-05-16 Revised:2016-09-25 Online:2017-01-05 Published:2017-01-05
Contact: 10.11949/j.issn.0438-1157.20160670
Supported by:
supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

摘要/Abstract

摘要：

对于非平稳过程监控，传统的基于数据平稳假设的多元统计过程控制方法是不适用的。针对上述问题，提出了一种基于共同趋势模型的非平稳过程监控方法。共同趋势模型从存在协整关系的非平稳多元变量中辨识出共同因子，将各非平稳过程变量分解成非平稳的共同趋势成分与平稳成分之和的形式。不同于现有的基于协整模型的非平稳过程监控方法，共同趋势模型能够获取各非平稳变量中的平稳成分，消除非平稳共同因子的影响并体现变量间全部的动态均衡关系。将对非平稳过程的监控变为应用共同趋势模型，分解得到各非平稳过程变量中的平稳成分，然后应用传统的多元统计方法，估计平稳成分的统计量及相应的控制限进行监测。石油蒸馏过程监控的实例研究结果表明，所提出的方法比基于协整新息变量的方法具有更可靠的监控效果。

关键词: 共同趋势模型, 过程监控, 非平稳过程, 协整检验, 过程控制, 过程系统, 系统工程

Abstract:

Non-stationary process monitoring based on common trends model was proposed, because conventional multivariate statistical process control methods with stationary data assumption were inapplicable to non-stationary process monitoring.The new common trends model was capable of identifying common factors from co-integrated non-stationary multiple variables and decomposing each non-stationary process variable into summation of a non-stationary common trends component and a stationary counterpart.Contrary to existing non-stationary process monitoring technique from cointegration model, the common trends model captured the stationary component of each non-stationary process variable, eliminated effects of non-stationary common factors and unveiled overall dynamic equilibrium relationships among variables.Hence, non-stationary process monitoring was transformed to an application of common trends model, which involved obtaining stationary component of each process variable, creating estimation for the stationary components by conventional multivariate statistical methods and setting up monitoring on corresponding control limits.A case study of monitoring petroleum distillation process showed that the proposed approach possessed more reliable process monitoring performance than the method of cointegration model.

Key words: common trends model, process monitoring, non-stationary process, cointegration testing, process control, process systems, system engineering

中图分类号:

TP277

林原灵, 陈前. 基于共同趋势模型的非平稳过程在线监控[J]. 化工学报, 2017, 68(1): 178-187.

LIN Yuanling, CHEN Qian. Online non-stationary process monitoring by common trends model[J]. CIESC Journal, 2017, 68(1): 178-187.

参考文献 32

[1]	陈前. 关于工程大系统的状态监测与故障诊断[J]. 振动、测试与诊断, 2002, 22(3):163-230. CHEN Q. Condition monitoring and fault diagnosis of large-scale engineering system[J]. Journal of Vibration Measurement and Diagnosis, 2002, 22(3):163-230.
[2]	GE Z, SONG Z, GAO F. Review of recent research on data-based process monitoring[J]. Industrial & Engineering Chemistry Research, 2013, 52(10):3543-3562.
[3]	CHEN Q, WYNNE R J, GOULDING P, et al. The application of principal component analysis and kernel density estimation to enhance process monitoring[J]. Control Engineering Practice, 2000, 8(5):531-543.
[4]	童楚东, 史旭华. 基于互信息的PCA方法及其在过程监测中的应用[J]. 化工学报, 2015, 66(10):4101-4106. TONG C D, SHI X H. Mutual information based PCA algorithm with application in process monitoring[J]. CIESC Journal, 2015, 66(10):4101-4106.
[5]	KANO M, TANAKA S, HASEBE S, et al. Monitoring independent components for fault detection[J]. AIChE Journal, 2003, 49(4):969-976.
[6]	GE Z, SONG Z. Performance-driven ensemble learning ICA model for improved non-Gaussian process monitoring[J]. Chemometrics and Intelligent Laboratory Systems, 2013, 123:1-8.
[7]	HUANG J, YAN X. Gaussian and non-Gaussian double subspace statistical process monitoring based on principal component analysis and independent component analysis[J]. Industrial & Engineering Chemistry Research, 2015, 54(3):1015-1027.
[8]	衷路生, 何东, 龚锦红, 等. 基于分布式ICA-PCA模型的工业过程故障监测[J]. 化工学报, 2015, 66(11):4546-4554. ZHONG L S, HE D, GONG J H, et al. Fault monitoring of industrial process based on distributed ICA-PCA model[J]. CIESC Journal, 2015, 66(11):4546-4554.
[9]	GUNTHER J C, CONNER J S, SEBORG D E. Process monitoring and quality variable prediction utilizing PLS in industrial fed-batch cell culture[J]. Journal of Process Control, 2009, 19(5):914-921.
[10]	韩敏, 张占奎. 基于改进核主成分分析的故障检测与诊断方法[J]. 化工学报, 2015, 66(6):2139-2149. HAN M, ZHANG Z K. Fault detection and diagnosis method based on modified kernel principal component analysis[J]. CIESC Journal, 2015, 66(6):2139-2149.
[11]	LEE J M, YOO C K, CHOI S W, et al. Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science, 2004, 59(1):223-234.
[12]	LEE J M, QIN S J, LEE I B. Fault detection of non-linear processes using kernel independent component analysis[J]. The Canadian Journal of Chemical Engineering, 2007, 85(4):526-536.
[13]	ZHANG Y. Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM[J]. Chemical Engineering Science, 2009, 64(5):801-811.
[14]	胡益, 王丽, 马贺贺, 等. 基于核PLS方法的非线性过程在线监控[J]. 化工学报, 2011, 62(9):2555-2561. HU Y, WANG L, MA H H, et al. Online nonlinear process monitoring using kernel partial least squares[J]. CIESC Journal, 2011, 62(9):2555-2561.
[15]	GODOY J L, ZUMOFFEN D A, VEGA J R, et al. New contributions to non-linear process monitoring through kernel partial least squares[J]. Chemometrics and Intelligent Laboratory Systems, 2014, 135:76-89.
[16]	COMON P, JUTTEN C. Handbook of Blind Source Separation:Independent Component Analysis and Applications[M]. Academic Press, 2010.
[17]	BERTHOUEX P M, BOX G E. Time series models for forecasting wastewater treatment plant performance[J]. Water Research, 1996, 30(8):1865-1875.
[18]	ENGLE R F, GRANGER C W J. Co-integration and error correction:representation, estimation, and testing[J]. Econometrica, 1987, 55(2):251-276.
[19]	PFAFF B. Analysis of Integrated and Cointegrated Time Series with R[M]. Springer Science & Business Media, 2008.
[20]	ENDERS W. Applied Econometric Time Series[M]. John Wiley & Sons, 2008.
[21]	陈前, 潘昱昱. 协整理论应用于非平稳FCCU系统的状态监测与故障诊断[J]. 石油学报(石油加工), 2007, 23(1):69-76. CHEN Q, PAN Y Y. Application of cointegration testing method to condition monitoring and fault diagnosis of nonstationary FCCU system[J]. Acta Petrolet Sinica (Petroleum Processing Section), 2007, 23(1):69-76.
[22]	CHEN Q, KRUGER U, LEUNG A Y T. Cointegration testing method for monitoring nonstationary processes[J]. Industrial & Engineering Chemistry Research, 2009, 48(7):3533-3543.
[23]	石海忱, 陈前, 林原灵. 协整系数矩阵的非平稳工程系统故障诊断应用研究[J]. 振动与冲击, 2015, 34(1):146-150. SHI H C, CHEN Q, LIN Y L. Fault diagnosis of non-stationary engineering system using cointegration cofficients matrix[J]. Journal of Vibration and Shock, 2015, 34(1):146-150.
[24]	STOCK J H, WATSON M W. Testing for common trends[J]. Journal of the American statistical Association, 1988, 83(404):1097-1107.
[25]	DICKEY D A, FULLER W A. Distribution of the estimators for autoregressive time series with a unit root[J]. Journal of the American statistical association, 1979, 74(366a):427-431.
[26]	AKAIKE H. A new look at the statistical model identification[J]. IEEE Transactions on Automatic Control, 1974, 19(6):716-723.
[27]	JOHANSEN S, JUSELIUS K. Maximum likelihood estimation and inference on cointegration-with applications to the demand for money[J]. Oxford Bulletin of Economics and statistics, 1990, 52(2):169-210.
[28]	JOHANSEN S. Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models[J]. Econometrica, 1991, 59(59):1551-1580.
[29]	KASA K. Common stochastic trends in international stock markets[J]. Journal of monetary Economics, 1992, 29(1):95-124.
[30]	GONZALO J, GRANGER C. Estimation of common long-memory components in cointegrated systems[J]. Journal of Business and Economic Statistics, 1995, 13(1):27-35.
[31]	ESCRIBANO A, PEÑA D. Cointegration and common factors[J]. Journal of Time Series Analysis, 1994, 15(6):577-586.
[32]	HOTELLING H. The Generalization of Student's Ratio[M]. New York:Springer, 1992.

基于共同趋势模型的非平稳过程在线监控

Online non-stationary process monitoring by common trends model

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