research-article

Implementing multivariate statistics-based process monitoring: : A comparison of basic data modeling approaches

Authors:

Jie DongAuthors Info & Claims

Volume 290, Issue C

Pages 172 - 184

https://doi.org/10.1016/j.neucom.2018.02.051

Published: 17 May 2018 Publication History

Highlights

•

Commonly used data modeling methods, including PCA, PLS and CCA, are investigated.

•

Their interconnections and differences of each other to address the multivariate statistics-based process monitoring (MSPM) problem are compared.

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Their different dynamic extensions are reviewed and the performance when used in MSPM field is evaluated.

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Continuous stirred tank reactor simulation and hot strip mill process application are carried out to show the theoretical results.

Abstract

The development of large interconnected plants has brought the need for the development of active and accurate performance monitoring methods. The commonly used approach to this problem is implementing the multivariate statistics-based process monitoring (MSPM). In MSPM, data modeling methods play the central role in developing normal operation models, based on which the monitoring statistics can be used to track the process operating performance. This paper seeks to perform a comparison study on the commonly used data modeling methods in the MSPM field, including principal component analysis, partial least squares, and canonical correlation analysis, to provide users and practitioners with informative details such that useful guidance can be offered to select the preferable methods. The interconnections between each two of them are first investigated. Then, dynamic extensions based on their static formulations including how they modify and resolve objective functions are revealed. Using the simulated data from the continuous stirred tank reactor benchmark process and real industrial data from the hot strip rolling mill process, parts of theoretical results are validated.

References

[1]

S. Yin, S.X. Ding, X. Xie, H. Luo, A review on basic data-driven approaches for industrial process monitoring, IEEE Trans. Ind. Electron. 61 (11) (2014) 6418–6428.

[2]

S. Yin, X. Zhu, C. Jing, Fault detection based on a robust one class support vector machine, Neurocmputing 145 (18) (2014) 263–268.

[3]

C. Yang, J. Hou, Fed-batch fermentation penicillin process fault diagnosis and detection based on support vector machine, Neurocomputing 190 (2016) 117–123.

[4]

S.J. Qin, Survey on data-driven industrial process monitoring and diagnosis, Annu. Rev. Control 36 (2) (2012) 220–234.

[5]

S.X. Ding, Data Driven Design of Fault diagnosis and Fault Tolerant Control Systems, Springer, London, Great Britain, 2014.

[6]

Z. Ge, Z. Song, F. Gao, Review of recent research on data-based process monitoring, Ind. Eng. Chem. Res. 52 (10) (2013) 3543–3562.

[7]

S.J. Qin, Statistical process monitoring: basics and beyond, J. Chemometr. 17 (8–9) (2003) 480–502.

[8]

H. Dong, Z. Wang, J. Lam, H. Gao, Fuzzy-model-based robust fault detection with stochastic mixed time delays and successive packet dropouts, IEEE Trans. Syst. Man Cybern. Part B Cybern. 42 (2) (2012) 365–376.

[9]

H. Dong, Z. Wang, H. Gao, Distributed H _∞ filtering for a class of Markovian jump nonlinear time-delay systems over lossy sensor networks, IEEE Trans. Ind. Electron. 60 (10) (2013) 4665–4672.

[10]

J. MacGregor, A. Cinarc, Monitoring, fault diagnosis, fault-tolerant control and optimization: Data driven methods, Comput. Chem. Eng. 47 (2012) 111–120.

[11]

K. Zhang, Performance Assessment for Process Monitoring and Fault Detection Methods, Springer Vieweg, Wiesbaden, Germany, 2016.

[12]

J.F. MacGregor, C. Jaeckle, C. Kiparissides, M. Koutoudi, Process monitoring and diagnosis by multiblock PLS methods, AIChE J. 40 (1994) 826–838.

[13]

T. Kourti, J.F. MacGregor, Process analysis, monitoring and diagnosis, using multivariate projection methods, Chemom. Intel. Lab. Syst. 28 (1995) 3–21.

[14]

Z. Chen, S.X. Ding, K. Zhang, Z. Li, Z. Hu, Canonical correlation analysis-based fault detection methods with application to alumina evaporation process, Contr. Eng. Pract. 46 (2016) 51–58.

[15]

K. Zhang, S.X. Ding, Y.A.W. Shardt, Z. Chen, K. Peng, Assessment of T2- and Q-statistics for detecting additive and multiplicative faults in multivariate statistical process monitoring, J. Frankl. Inst. 354 (2) (2017) 668–688.

[16]

U. Kruger, L. Xie, Statistical Monitoring of Complex Multivariate Processes, John Wiley & Sons, New York, USA, 2012.

[17]

K. Zhang, H.Y. Hao, Z.W. Chen, S.X. Ding, K.X. Peng, A comparison and evaluation of key performance indicator-based multivariate statistics process monitoring approaches, J. Process Control 33 (2015) 112–126.

[18]

J. Zeng, U. Kruger, J. Geluk, X. Wang, L. Xie, Detecting abnormal situations using the Kullback-Leibler divergence, Automatica 50 (2014) 2777–2786.

[19]

L. Xie, J. Zeng, U. Kruger, X. Wang, J. Geluk, Fault detection in dynamic systems using the Kullback-Leibler divergence, Control Eng. Pract. 43 (2015) 39–48.

[20]

M.M. Rashid, J. Yu, A new dissimilarity method integrating multidimensional mutual information and independent component analysis for non-gaussian dynamic process monitoring, Chemometr. Intell. Lab. Syst. 115 (2012) 44–58.

[21]

J.E. Jackson, A User Guide to Principal Components, John Wiley & Sons, New York, USA, 1991.

[22]

S.J. Qin, S. Valle, M.J. Piovoso, On unifying multiblock analysis with application to decentralized process monitoring, J. Chemometr. 15 (2001) 715–742.

[23]

B. Schölkopf, A. Smola, K.R. Müller, Nonlinear component analysis as a kernel eigenvalue problem, Neural Comput. 10 (5) (1998) 1299–1319.

Digital Library

[24]

X. Liu, K. Li, M. McAfee, G.W. Irwin, Improved nonlinear PCA for process monitoring using support vector data description, J. Process Control 21 (2011) 1306–1317.

[25]

H. Wold, Path Models with Latent Variables: the NIPALS Approach. Quantitative Sociology. International Perspectives on Mathematical and Statistical Modeling, Academic Press, New York, USA, 1975.

[26]

G.M. Morales, Partial least squares (PLS) methods: Origins, evolution, and application to social sciences, Commun. Stat. Theory Methods 40 (13) (2010) 2305–2317.

[27]

P. Geladi, B.R. Kowalski, Partial least-squares regression: a tutorial, Anal. Chim. Acta. 185 (1986) 1–17.

[28]

D. Jong, S. SIMPLS: An alternative approach to partial least squares regression, Chemom. Intell. Lab. Syst. 18 (3) (1993) 251–263.

[29]

G. Li, S.J. Qin, D.H. Zhou, Geometric properties of partial leasts quares for process monitoring, Automatica 46 (1) (2010) 204–210.

[30]

D.H. Zhou, G. Li, S.J. Qin, Total projection to latent structures for process monitoring, AIChE J. 56 (1) (2010) 168–178.

[31]

S.J. Qin, Y.Y. Zheng, Quality-relevant and process-relevant fault monitoring with concurrent projection to latent structures, AIChE J. 59 (2) (2013) 496–504.

[32]

J. Hu, C. Wen, P. Li, T. Yuan, Direct projection to latent detection, J. Frankl. Inst. 351 (2014) 1226–1250.

[33]

S. Yin, S.X. Ding, A. Haghani, H.Y. Hao, P. Zhang, A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark tennessee eastman process, J. Process Control 22 (9) (2012) 1567–1581.

[34]

R. Rosipal, L.J. Trejo, Kernel partial least squares regression in reproducing kernel hillbert space, J. Mach. Learn. Res. 2 (2001) 97–123.

[35]

G. Baffi, E.B. Martin, A.J. Morris, Non-linear dynamic projection to latent structures modelling, Chemom. Intel. Lab. Syst. 52 (2000) 5–22.

[36]

H. Hotelling, Relations between two sets of variates, Biometrika 28 (1936) 321–377.

[37]

Z. Chen, S.X. Ding, T. Peng, C. Yang, W. Gui, Fault detection for non-gaussian processes using generalized canonical correlation analysis and randomized algorithms, IEEE Trans. Ind. Electron. https://doi.org/10.1109/TIE.2017.2733501.

[38]

Z. Chen, K. Zhang, S.X. Ding, Y.A.W. Shardt, Z. Hu, Improved canonical correlation analysis-based fault detection methods for industrial processes, J. Process Control 41 (2016) 26–34.

[39]

Y. Yao, F. Gao, A survey on multistage/multiphase statistical modeling methods for batch processes, Ann. Rev. Control 33 (2009) 172–183.

[40]

Y. Xu, X. Deng, Fault detection of multimode non-gaussian dynamic process using dynamic Bayesian independent component analysis, Neurocomputing 200 (c) (2016) 70–79.

[41]

R. Shi, J.F. MacGregor, Modeling of dynamic systems using latent variable and subspace methods, J. Chemometrics 14 (2000) 423–439.

[42]

J.F. MacGregor, M.J. Bruwer, I. Miletic, M. Cardin, Z. Liu, Latent variable models and big data in the process industries, IFAC-PapersOnLine 48-8 (2015) 520–524.

[43]

J. Wang, S.J. Qin, A new subspace identification approach based on principal component analysis, J. Process Control 12 (2002) 841–855.

[44]

Q. Zhu, Q. Liu, S.J. Qin, Quality-relevant fault detection of nonlinear processes based on kernel concurrent canonical correlation analysis, Proceedings of the American Control Conference, Seattle, USA, 2017, pp. 5404–5409.

[45]

Q. Zhu, Q. Liu, S.J. Qin, Concurrent canonical correlation analysis modeling for quality-relevant monitoring, Proceedings of the 11th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems, NTNU, Trondheim, Norway, 2016, pp. 1044–1049.

[46]

W. Ku, R. Storer, C. Georgakis, Disturbance detection and isolation by dynamic principal component analysis, Chemom. Intell. Lab. Syst. 30 (1995) 179–196.

[47]

M.H. Kaspar, W.H. Ray, Dynamic PLS modelling for process control, Chem. Eng. Sci. 48 (1993) 3447–3461.

[48]

B.S. Dayal, J.F. MacGregor, Identification of finite impulse response models: Methods and robustness issues, Ind. Eng. Chem. Res. 35 (1996) 4078–4090.

[49]

F. Zhou, J.H. Park, Y. Liu, Differential feature based hierarchical PCA fault detection method for dynamic fault, Neurocomputing 202 (2017) 27–35.

Digital Library

[50]

S. Lakshminarayanan, S.L. Shah, K. Nandakumar, Modeling and control of multivariable processes: The dynamic projection to latent structures approach, AIChE J. 43 (9) (1997) 2307–2323.

[51]

J. Chen, K. Liu, On-line batch process monitoring using dynamic PCA and dynamic PLS models, Chem. Eng. Sci. 14 (2002) 63–75.

[52]

K. Zhang, Y.A.W. Shardt, Z. Chen, S.X. Ding, K.X. Peng, Unit-level modelling for KPI of batch hot strip mill process using dynamic partial least squares, Proceedings of the IFAC System Identification Symposium, Peking, China, 2015.

[53]

G. Lee, C.H. Han, E.S. Yoon, Multiple-fault diagnosis of the tennessee eastman process based on system decomposition and dynamic PLS, Ind. Eng. Chem. Res. 43 (2004) 8037–8048.

[54]

P. Odiowei, Y. Cao, Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations, IEEE Trans. Ind. Inf. 6 (2010) 36–45.

[55]

G. Li, S.J. Qin, B.S. Liu, D.H. Zhou, Quality relevant data-driven modeling and monitoring of multivariate dynamic processes: the dynamic T-PLS approach, IEEE Trans. Neural Netw. 22 (2011) 2262–2271.

[56]

G. Li, S.J. Qin, D.H. Zhou, A new method of dynamic latent variable modelling for process monitoring, IEEE Trans. Ind. Electron. 61 (11) (2014) 6438–6445.

[57]

E.L. Russell, L.H. Chiang, R.D. Braatz, Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis, Chemom. Intell. Lab. Syst. 51 (2000) 81–93.

[58]

E.L. Russell, L.H. Chiang, R.D. Braatz, Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes, Springer.

[59]

S.X. Ding, S. Yin, K.X. Peng, H.Y. Hao, B. Shen, A novel scheme for key performance indicator prediction and diagnosis with application to an industrial hot strip mill, IEEE Trans. Ind. Inf. 9 (4) (2013) 39–47.

[60]

W. Larimore, System identification reduced order filtering and modeling via canonical variate analysis, Proceedings of the American Control Conference, SanFrancisco, 1983, pp. 175–181.

[61]

K.X. Peng, K. Zhang, G. Li, D.H. Zhou, Contribution rate plot for nonlinear quality-related fault diagnosis with application to the hot strip mill process, Contr. Eng. Pract. 21 (2013) 360–369.

[62]

K. Peng, K. Zhang, G. Li, Online contribution rate based fault diagnosis for nonlinear industrial processes, Acta Autom. Sinica 40 (3) (2014) 423–430.

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Implementing multivariate statistics-based process monitoring: A comparison of basic data modeling approaches

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Information & Contributors

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Published In

cover image Neurocomputing

Neurocomputing Volume 290, Issue C

May 2018

238 pages

ISSN:0925-2312

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Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 17 May 2018

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