Improved performance of process monitoring based on selection of key principal components

doi:10.1016/j.cjche.2015.11.014

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (12): 1951-1957.DOI: 10.1016/j.cjche.2015.11.014

• 第25届中国过程控制会议专栏 • 上一篇下一篇

Improved performance of process monitoring based on selection of key principal components

Bing Song, Yuxin Ma, Hongbo Shi

Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China

收稿日期:2015-05-20 修回日期:2015-07-18 出版日期:2015-12-28 发布日期:2016-01-19
通讯作者: Hongbo Shi
基金资助:
Supported by the National Natural Science Foundation of China (No. 61374140) and Shanghai Pujiang Program (Project No. 12PJ1402200)

Improved performance of process monitoring based on selection of key principal components

Bing Song, Yuxin Ma, Hongbo Shi

Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China

Received:2015-05-20 Revised:2015-07-18 Online:2015-12-28 Published:2016-01-19
Contact: Hongbo Shi
Supported by:
Supported by the National Natural Science Foundation of China (No. 61374140) and Shanghai Pujiang Program (Project No. 12PJ1402200)

摘要/Abstract

摘要： Conventional principal component analysis (PCA) can obtain low-dimensional representations of original data space, but the selection of principal components (PCs) based on variance is subjective, which may lead to information loss and poormonitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression.On the basis of the proposed relevance of variables with each principal component, key principal components can be determined. All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data spacewithout information loss.A squaredMahalanobis distance,which is introduced as themonitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.

关键词: Principal component analysis, Information loss, Fault detection, Key principal component

Abstract: Conventional principal component analysis (PCA) can obtain low-dimensional representations of original data space, but the selection of principal components (PCs) based on variance is subjective, which may lead to information loss and poormonitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression.On the basis of the proposed relevance of variables with each principal component, key principal components can be determined. All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data spacewithout information loss.A squaredMahalanobis distance,which is introduced as themonitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.

Key words: Principal component analysis, Information loss, Fault detection, Key principal component

Bing Song, Yuxin Ma, Hongbo Shi. Improved performance of process monitoring based on selection of key principal components[J]. Chinese Journal of Chemical Engineering, 2015, 23(12): 1951-1957.

Bing Song, Yuxin Ma, Hongbo Shi. Improved performance of process monitoring based on selection of key principal components[J]. Chin.J.Chem.Eng., 2015, 23(12): 1951-1957.

参考文献

[1] Z.Q. Ge, Z.H. Song, F.R. Gao, Review of recent research on data-based process monitoring, Ind. Eng. Chem. Res. 52 (2013) 3543-3562.

[2] U. Kruger, G. Dimitriadis, Diagnosis of process faults in chemical systems using a local partial least squares approach, AIChE J. 54 (2008) 2581-2596.

[3] J.M. Lee, C.K. Yoo, S.W. Choi, P.A. Vanrolleghem, I.B. Lee, Nonlinear process monitoring using kernel principal component analysis, Chem. Eng. Sci. 59 (2004) 223-234.

[4] Z.Q. Ge, Z.H. Song, Mixture Bayesian regularization method of PPCA for multimode process monitoring, AIChE J. 56 (2010) 2838-2849.

[5] S.J. Zhao, J. Zhang, Y.M. Xu, Performance monitoring of processes with multiple operating modes through multiple PLS models, J. Process Control 16 (2006) 763-772.

[6] B. Song, H.B. Shi, Y.X. Ma, J.P. Wang, Multisubspace principal component analysis with local outlierfactor for multimode process monitoring, Ind. Eng. Chem. Res. 53 (2014) 16453-16464.

[7] H.D. Jin, Y.H. Lee, G. Lee, C.H. Han, Robust recursive principal component analysis modeling for adaptive monitoring, Ind. Eng. Chem. Res. 45 (2006) 696-703.

[8] W.F. Ku, R.H. Storer, C. Georgakis, Disturbance detection and isolation by dynamic principal component analysis, Chemom. Intell. Lab. Syst. 30 (1995) 179-196.

[9] D.S. Kim, I.B. Lee, Process monitoring based on probabilistic PCA, Chemom. Intell. Lab. Syst. 67 (2003) 109-123.

[10] S.J. Qin, R. Dunia, Determining the number of principal components for best reconstruction, J. Process Control 10 (2000) 245-250.

[11] M. Tamura, S. Tsujita, A study on the number of principal components and sensitivity of fault detection using PCA, Comput. Chem. Eng. 31 (2007) 1035-1046.

[12] I.T. Jolliffe, A note on the use of principal components in regression, Appl. Stat. (1982) 300-303.

[13] T. Togkalidou, R.D. Braatz, B.K. Johnson, O. Davidson, A. Andrews, Experimental design and inferential modeling in pharmaceutical crystallization, AIChE J. 47 (2001) 160-168.

[14] Q.C. Jiang, X.F. Yan, W.X. Zhao, Fault detection and diagnosis in chemical processes using sensitive principal component analysis, Ind. Eng. Chem. Res. 52 (2013) 1635-1644.

[15] Q. Chen, U. Kruger, M. Meronk, A. Leung, Synthesis of T2 and Q statistics for process monitoring, Control. Eng. Pract. 12 (2004) 745-755.

[16] Z.Q. Ge, F.R. Gao, Z.H. Song, Two-dimensional Bayesian monitoring method for nonlinear multimode processes, Chem. Eng. Sci. 66 (2011) 5173-5183.

[17] X. Xie, H.B. Shi, Dynamic multimode process modeling and monitoring using adaptive Gaussian mixture models, Ind. Eng. Chem. Res. 51 (2012) 5497-5505.

[18] T.J. Rato, M.S. Reis, Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR), Chemom. Intell. Lab. Syst. 125 (2013) 101-108.

[19] N.L. Ricker, Optimal steady-state operation of the Tennessee Eastman challenge process, Comput. Chem. Eng. 19 (1995) 949-959.

[20] J.J. Downs, E.F. Vogel, A plant-wide industrial process control problem, Comput. Chem. Eng. 17 (1993) 245-255.

[21] J.M. Lee, C. Yoo, I.B. Lee, Statistical monitoring of dynamic processes based on dynamic independent component analysis, Chem. Eng. Sci. 59 (2004) 2995-3006.

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