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CLC number: TQ086.3; TP277

On-line Access: 2013-07-01

Received: 2013-01-02

Revision Accepted: 2013-04-12

Crosschecked: 2013-06-08

Cited: 5

Clicked: 6045

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2013 Vol.14 No.7 P.520-534


Statistical process monitoring based on improved principal component analysis and its application to chemical processes*

Author(s):  Chu-dong Tong, Xue-feng Yan, Yu-xin Ma

Affiliation(s):  . Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Corresponding email(s):   xfyan@ecust.edu.cn

Key Words:  Fault detection, Principal component analysis (PCA), Correlative principal components (CPCs), Tennessee Eastman process

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Chu-dong Tong, Xue-feng Yan, Yu-xin Ma. Statistical process monitoring based on improved principal component analysis and its application to chemical processes[J]. Journal of Zhejiang University Science A, 2013, 14(7): 520-534.

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T1 - Statistical process monitoring based on improved principal component analysis and its application to chemical processes
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In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to their mean and covariance changes between the modeling sample and the online monitored data. The retained PCs containing dominant variations were selected and defined as correlative PCs (CPCs). The new Hotelling’s T 2 statistic based on CPCs was then employed to monitor the process. Case studies on the simulated continuous stirred tank reactor and the well-known tennessee Eastman process demonstrated the feasibility and effectiveness of the CPCs-based fault detection methods.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1] Alcala, C.F., Qin, S.J., 2010. Reconstruction-based contribution for process monitoring with kernel principal component analysis. Industrial & Engineering Chemistry Research, 49(17):7849-7857. 

[2] Chen, X.Y., Yan, X.F., 2012. Using improved self-organizing map for fault diagnosis in chemical industry process. Chemical Engineering Research & Design, in press,:

[3] Chiang, L.H., Russel, E.L., Braatz, R.D., 2001.  Fault Detection and Diagnosis in Industrial Systems. Springer,London :

[4] Detroja, K.P., Gudi, R.D., Patwardhan, S.C., 2007. Plant-wide detection and diagnosis using correspondence analysis. Control Engineering Practice, 15(12):1468-1483. 

[5] Dunia, R., Qin, S.J., 1998. Joint diagnosis of process and sensor faults using principal component analysis. Control Engineering Practice, 6(4):457-469. 

[6] Garcia-Alvarez, D., Fuente, M.J., Sainz, G.G., 2012. Fault detection and isolation in transient states using principal component analysis. Journal of Process Control, 22(3):551-563. 

[7] Ge, Z.Q., Song, Z.H., 2008. Batch process monitoring based on multilevel ICA-PCA. Journal of Zhejiang University-SCIENCE A, 9(8):1061-1069. 

[8] He, X.B., Wang, W., Yang, Y.P., Yang, Y.H., 2009. Variable-weighted fisher discriminant analysis for process fault diagnosis. Journal of Process Control, 19(6):923-931. 

[9] Jackson, J.E., 1991.  A Users Guide to Principal Components. John Wiley & Sons,New York :

[10] Juricek, B.C., Seborg, D.E., Larimore, W.E., 2004. Fault detection using canonical variate analysis. Industrial Engineering & Chemistry Research, 43(2):458-474. 

[11] Kano, M., Nagao, K., Hasebe, S., Hashimoto, I., Ohno, H., Strauss, R., Bakshi, B.R., 2002. Comparison of multivariate statistical process control monitoring methods with applications to the Eastman challenge problem. Computers & Chemical Engineering, 26(2):161-174. 

[12] Kourti, T., MacGregor, J.F., 1995. Process analysis, monitoring and diagnosis using multivariate projection methods. Chemometrics & Intelligent Laboratory Systems, 28(1):3-21. 

[13] Kresta, J.V., MacGregor, J.F., Marlin, T.E., 1991. Multivariate statistical monitoring of process operating performance. The Canadian Journal of Chemical Engineering, 69(1):35-47. 

[14] Ku, W., Storer, R.H., Georgakis, C., 1995. Disturbance detection and isolation by dynamic principal component analysis. Chemometrics & Intelligent Laboratory Systems, 30(1):179-196. 

[15] Lee, J.M., Yoo, C.K., Choi, S.W., Vanrolleghem, P.A., Lee, I.B., 2004. Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science, 59(1):223-234. 

[16] Lee, J.M., Yoo, C.K., Lee, I.B., 2004. Statistical process monitoring with independent component analysis. Journal of Process Control, 14(5):467-485. 

[17] Lee, J.M., Qin, S.J., Lee, I.B., 2006. Fault detection and diagnosis based on modified independent component analysis. AIChE Journal, 52(10):3501-3514. 

[18] Li, W., Yue, H., Valle, C.S., Qin, S.J., 2000. Recursive PCA for adaptive process monitoring. Journal of Process Control, 10(5):471-486. 

[19] Liu, Y.M., Ye, L.B., Zheng, P.Y., Shi, X.R., Hu, B., Liang, J., 2010. Multiscale classification and its application to process monitoring. Journal of Zhejiang University-SCIENCE C (Computers & Electronics), 11(6):425-434. 

[20] Lyman, P.R., Georgakist, C., 1995. Plant-wide control of the Tennessee Eastman problem. Computers & Chemical Engineering, 19(3):321-331. 

[21] Nomikos, P., MacGregor, J., 1995. Multivariate SPC charts for monitoring batch processes. Technometrics, 37(1):41-59. 

[22] Qin, S.J., 2003. Statistical process monitoring: basics and beyond. Journal of Chemometrics, 17(8-9):480-502. 

[23] Russell, E.L., Chiang, L.H., Braatz, R.D., 2000. Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis. Chemometrics & Intelligent Laboratory Systems, 51(1):81-93. 

[24] Stubbs, S., Zhang, J., Morris, J.L., 2012. Fault detection in dynamic processes using a simplified monitoring-specific CVA state space modelling approach. Computers & Chemical Engineering, 41:77-87. 

[25] Tamura, M., Tsujita, S., 2007. A study on the number of principal components and sensitivity of fault detection using PCA. Computers & Chemical Engineering, 31(9):1035-1046. 

[26] Togkalidou, T., Braatz, R.D., Johnson, B.K., Davidson, O., Andrews, A., 2001. Experimental design and inferential modeling in pharmaceutical crystallization. AIChE Journal, 47(1):160-168. 

[27] Valle, S., Li, W., Qin, S.J., 1999. Selection of the number of principal components: the variance of the reconstruction error criterion with a comparison to other methods. Industrial & Engineering Chemistry Research, 38(11):4389-4401. 

[28] Wang, J., He, Q.P., 2010. Multivariate statistical process monitoring based on statistics pattern analysis. Industrial Engineering & Chemistry Research, 49(17):7858-7869. 

[29] Wang, X., Kruger, U., Irwin, G.W., 2005. Process monitoring approach using fast moving window PCA. Industrial & Engineering Chemistry Research, 44(15):5691-5702. 

[30] Wold, S., 1978. Cross-validatory estimation of the number of components in factor and principal components models. Technometrics, 20(4):397-405. 

[31] Yoon, S., MacGregor, J., 2001. Fault diagnosis with multivariate statistical models, part I: using steady-state fault signatures. Journal of Process Control, 11(4):387-400. 

[32] Yu, J., Qin, S.J., 2008. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AIChE Journal, 54(7):1811-1829. 

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