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化工学报  2015 

基于全变量信息的子空间监控方法

DOI: 10.11949/j.issn.0438-1157.20141618, PP. 1395-1401

Keywords: 化工过程系统,子空间,信息缺失,监控模型,数值分析

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Abstract:

实际化工过程采集得到的数据往往维度较高,直接建模比较复杂。主元分析(principalcomponentanalysis,PCA)方法可以提取原始数据主要特征,得到低维数据,但传统的PCA过程监控方法仅保留了方差较大的主元,会造成信息缺失,这将大大影响过程监控性能。针对这一问题,提出了一种新的基于全变量信息(fullvariableinformation,FVI)的子空间监控方法。首先,依据每个变量与主元空间(principalcomponentsubspace,PCS)和残差空间(residualsubspace,RS)相似性的高低,将原始数据空间划分为3个维度较低的子空间,3个子空间保存了全部过程变量,可以更充分地利用过程信息。其次,在每个子空间中,分别建立监控模型,并利用贝叶斯推断整合子空间的监控结果。最后,通过数值仿真及TennesseeEastman(TE)过程仿真研究验证FVI方法的有效性。

References

[1]  Ge Zhiqiang. Improved two-level monitoring system for plant-wide processes [J]. Chemometrics and Intelligent Laboratory Systems, 2014, 132: 141-151
[2]  Qin S J. Statistical process monitoring: basics and beyond [J]. Journal of Chemometrics, 2003, 17: 480-502
[3]  Tong Chudong, Song Yu,Yan Xuefeng. Distributed statistical process monitoring based on four-subspace construction and Bayesian inference [J]. Industrial and Engineering Chemistry Research, 2013, 52 (29): 9897-9907
[4]  Ge Zhiqiang, Zhang Muguang, Song Zhihuan. Nonlinear process monitoring based on linear subspace and Bayesian inference [J]. Journal of Process Control, 2010, 20: 676-688
[5]  Ge Zhiqiang, Gao Furong, Song Zhihuan. Two-dimensional Bayesian monitoring method for nonlinear multimode processes [J]. Chemical Engineering Science, 2011, 66: 5173-5183
[6]  Ge Zhiqiang, Song Zhihuan. Distributed PCA model for plant-wide process monitoring [J]. Industrial and Engineering Chemistry Research, 2013, 52: 1947-1957
[7]  Zhang Muguang (张沐光), Song Zhihuan (宋执环). Subspace fault detection method based on independent component contribution [J].Control Theory and Applications (控制理论与应用), 2010, 27: 296-302
[8]  Zhang Yingwei, Chai Tianyou, Li Zhiming, Yang Chunyu. Modeling and monitoring of dynamic processes [J]. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23 (2): 277-284
[9]  Jiang Qingchao, Yan Xuefeng, Zhao Weixiang. Fault detection and diagnosis in chemical processes using sensitive principal component analysis [J]. Industrial and Engineering Chemistry Research, 2013, 52 (4): 1635-1644
[10]  Lv Zhaomin, Jiang Qingchao, Yan Xuefeng. Batch process monitoring based on multi-subspace multi-way principal component analysis and time series Bayesian inference [J]. Industrial and Engineering Chemistry Research, 2014, 53 (15): 6457-6466
[11]  Noriah M, Ian T. Variable Selection and interpretation of covariance principal components [J]. Communications in Statistics - Simulation and Computation, 2001, 30 (2): 339-354
[12]  Bjorck A, Golub G. Numerical methods for computing angles between linear subspaces [J].Mathematics of Computation, 1973, 27: 579-594
[13]  Song Bing (宋冰), Ma Yuxin (马玉鑫), Fang Yongfeng (方永锋), Shi Hongbo (侍洪波). Fault detection for chemical process based on LSNPE method [J]. CIESC Journal (化工学报), 2014, 65 (2): 620-627
[14]  Zhang Ni (张妮), Tian Xuemin (田学民), Cai Lianfang (蔡连芳). Non-linear process fault detection method based on RISOMAP [J]. CIESC Journal (化工学报), 2013, 64 (6): 2125-2130
[15]  Kim D S, Lee I B. Process monitoring based on probabilistic PCA [J]. Chemometrics and Intelligent Laboratory Systems, 2003, 67: 109-123
[16]  Wang Xun, Kruger Uwe, Lennox Barry. Recursive partial least squares algorithms for monitoring complex industrial processes [J]. Control Engineering Practice, 2003, 11: 613-632
[17]  Kruger Uwe, Dimitriadis Grigorios. Diagnosis of process faults in chemical systems using a local partial least squares approach [J]. American Institute of Chemical Engineers, 2008, 54: 2581-2596
[18]  Zhu P, Knyazev A V. Angles between subspaces and their tangents [J]. Journal of Numerical Mathematics, 2013, 21 (4): 325-340
[19]  Bishop C M. Pattern Recognition and Machine Learning [M]. Springer, 2006: 272-279
[20]  Downs J J, Vogel E F. A plant-wide industrial process control problem [J]. Computers and Chemical Engineering, 1993, 17 (3): 245-255
[21]  Ricker N L. Optimal steady-state operation of the Tennessee Eastman challenge process [J]. Computers and Chemical Engineering, 1995, 19 (9): 949-959
[22]  Wang J, He Q P. Multivariate statistical process monitoring based on statistics pattern analysis [J].Industrial and Engineering Chemistry Research, 2010, 49 (17): 7858-7869

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