New kernel independent and principal components analysis-based process monitoring approach with application to hot strip mill process
- Author(s): Kaixiang Peng 1 ; Kai Zhang 1 ; Xiao He 2 ; Gang Li 2 ; Xu Yang 1
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View affiliations
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Affiliations:
1:
Key Laboratory for Advanced Control of Iron and Steel Process, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, People's Republic of China;
2: Department of Automation, TNList, Tsinghua University, Beijing 100084, People's Republic of China
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Affiliations:
1:
Key Laboratory for Advanced Control of Iron and Steel Process, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, People's Republic of China;
- Source:
Volume 8, Issue 16,
06 November 2014,
p.
1723 – 1731
DOI: 10.1049/iet-cta.2013.0691 , Print ISSN 1751-8644, Online ISSN 1751-8652
In this article, a new kernel independent and principal components analysis (kernel ICA–PCA) based process monitoring approach is proposed for hot strip mill process (HSMP). HSMP appears widely in iron and steel industry, which runs in an environment with significant nonlinearity, non-Gaussianity and some other uncertainties. The present method, namely kernel ICA–PCA, firstly addresses the nonlinearity via the popular kernel trick, then applies kernel ICA model to isolate the non-Gaussian independent information, finally, utilises kernel PCA model to account for the uncertain part and extract the principal components. To avoid the disadvantage of the original fault detection statistics, a k nearest neighbour data description-based technique is employed into the kernel ICA–PCA for monitoring the variations occurring in the independent and principal components, whereas traditional Q statistic is employed to reflect the disturbance in the residuals. All of their thresholds will be determined by a new emerging bootstrap-based technique. The applicability of the new scheme is represented via hot strip mill process dataset recorded in the iron and steel company.
Inspec keywords: fault diagnosis; hot rolling; pattern classification; principal component analysis; process monitoring
Other keywords: steel company; nonGaussian independent information; iron industry; HSMP; kernel ICA–PCA; bootstrap-based technique; steel industry; k nearest neighbour data description-based technique; iron company; kernel independent and principal components analysis-based process monitoring approach; hot strip mill process; kernel trick; fault detection statistics
Subjects: Statistics; Metallurgical industries; Heat treatment; Forming processes
References
-
-
1)
-
32. He, Q.H., Wang, J.: ‘Principal component based k-nearest-neighbor rule for semicounsuctor process fault detection’. Proc. American Control Conf., Washington, USA, June 2008, pp. 1606–1611.
-
-
2)
-
1. Gertler, J.: ‘Fault detection and diagnosis in engineering systems’ (Marcel Dekker, New York, 1998).
-
-
3)
-
12. Kano, M., Tanaka, S., Hasebe, S., Hashimoto, I., Ohno, H.: ‘Monitoring independent components for fault detection’, AIChE J., 2003, 49, (4), pp. 969–976 (doi: 10.1002/aic.690490414).
-
-
4)
-
30. Mahadevan, S., Shah, S.L.: ‘Fault detection and diagnosis in process data using one-class support vector machines’, J. Process Control, 2009, 19, (10), pp. 1627–1639 (doi: 10.1016/j.jprocont.2009.07.011).
-
-
5)
-
11. Li, R.F., Wang, X.Z.: ‘Dimension reduction of process dynamic trends using independent component analysis’, Comput. Chem. Eng., 2002, 26, (3), pp. 467–473 (doi: 10.1016/S0098-1354(01)00773-6).
-
-
6)
-
35. Yang, X., Li, Q., Tong, C.N., et al: ‘Vertical vibration model for unsteady lubrication in rolls-strip interface of cold rolling mills’. Adv. Mech. Eng., 2012, Article ID 734510, DOI: 10.1155/2012/734510.
-
-
7)
-
20. Albazzaz, H., Wang, X.Z.: ‘Introduction of dynamics to an approach for batch process monitoring using independent component analysis’, Chem. Eng. Commun., 2007, 194, (2), pp. 218–233 (doi: 10.1080/00986440600829739).
-
-
8)
-
15. Ge, Z.Q., Song, Z.H.: ‘Process monitoring based on independent component analysis-principal component analysis (ICA–PCA) and similarity factors’, Ind. Eng. Chem. Res., 2007, 46, (7), pp. 2054–2063 (doi: 10.1021/ie061083g).
-
-
9)
-
17. Peng, K.X., Zhang, K., Li, G., Zhou, D.H.: ‘Contribution rate plot for nonlinear quality-related fault diagnosis with application to the hot strip mill process’, Control Eng. Pract., 2013, 21, (4), pp. 360–369 (doi: 10.1016/j.conengprac.2012.11.013).
-
-
10)
-
31. He, Q.H., Wang, J.: ‘Large-scale semicounductor process fault detection using a fast pattern recognition-based method’, IEEE Trans. Semicond. Manuf., 2010, 23, (2), pp. 194–200 (doi: 10.1109/TSM.2010.2041289).
-
-
11)
-
34. Hongli, D., Ho, D.W.C., Gao, H.: ‘Fault detection for Markovian jump systems with sensor saturations and randomly varying nonlinearities’, IEEE Trans. Circuits Syst., I:Regul. Pap., 2012, 59, (10), pp. 2354–2362 (doi: 10.1109/TCSI.2012.2185330).
-
-
12)
-
10. Lee, J.M., Yoo, C.K., Lee, I.B.: ‘Statistical process monitoring with independent component analysis’, J. Process Control, 2004, 14, (5), pp. 467–485 (doi: 10.1016/j.jprocont.2003.09.004).
-
-
13)
-
13. Li, G., Qin, S. J., Zhou, D. H.: ‘Geometric properties of partial least squares for process monitoring’, Automatica, 2010, 46, (1), pp. 204–210 (doi: 10.1016/j.automatica.2009.10.030).
-
-
14)
-
18. Wang, J.C., Zhang, Y.B., Cao, H., Zhu, W.Z.: ‘Dimension reduction method of independent component analysis for process monitoring based on minimum mean square error’, J. Process Control, 2012, 22, (2), pp. 477–487 (doi: 10.1016/j.jprocont.2011.11.005).
-
-
15)
-
2. Ding, S.X.: ‘Model-based fault diagnosis techniques: design schemes, algorithms and tools’ (Springer-Heidelberg, Berlin, 2008, 2nd edn. 2013).
-
-
16)
-
27. Yu, J.: ‘A new fault diagnosis method of multimode processes using Bayesian inference based Gaussian mixture contribution decomposition’. Eng. Appl. Artif. Intell., 2013, 26, (1), pp. 456–466 (doi: 10.1016/j.engappai.2012.09.003).
-
-
17)
-
29. Kim, S.B., Jitpitaklert, W., Sukchotrat, T.: ‘One-class classification-based control charts for monitoring autocorrelated multivariate processes’, Commun. Stat. – Simul. Comput., 2010, 39, (3), pp. 461–474 (doi: 10.1080/03610910903480826).
-
-
18)
-
9. Qin, S.J.: ‘Statistical process monitoring: basics and beyond’, J. Chemometr., 2003, 17, (8-9), pp. 480–502 (doi: 10.1002/cem.800).
-
-
19)
-
28. Sukchotrat, T., Kim, S.B., Tsung, F.: ‘One-class classification-based control charts for multivariate process monitoring’, IIE Trans., 2009, 42, (2), pp. 107–120 (doi: 10.1080/07408170903019150).
-
-
20)
-
22. Choi, S.W., Lee, C., Lee, J.M., Park, J.H., Lee, I.B.: ‘Fault detection and identification of nonlinear processes based on kernel PCA’, Chemometr. Intell. Lab. Syst., 2005, 75, (1), pp. 55–67 (doi: 10.1016/j.chemolab.2004.05.001).
-
-
21)
-
19. Zhao, C.H., Gao, F.R., Wang, F.L.: ‘Nonlinear batch process monitoring using phase-based kernel independent component analysis-principal component analysis’, Ind. Eng. Chem. Res., 2009, 48, (20), pp. 9163–9174 (doi: 10.1021/ie8012874).
-
-
22)
- A. Hyvarinen . Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw. , 3 , 624 - 634
-
23)
-
9. Shen, B., Wang, Z., Shu, H., Wei, G.: ‘On nonlinear H∞ filtering for discrete-time stochastic systems with missing measurements’, IEEE Trans. Autom. Control, 2008, 53, (9), pp. 2170–2180 (doi: 10.1109/TAC.2008.930199).
-
-
24)
-
6. Russell, E., Chiang, L., Braatz, R.: ‘Data-driven methods for fault detection and diagnosis in chemical processes’ (Springer-Verlag, London, 2000).
-
-
25)
-
34. Yang, X., Tong, C.N.: ‘Coupling dynamic model and control of chatter in cold rolling’. J. Dyn. Syst. Meas. Control, Trans. ASME, 2012, 134, (4), pp. 1–8 (doi: 10.1115/1.4005498).
-
-
26)
-
4. Doraiswami, R., Cheded, L.: ‘Kalman filter for parametric fault detection: an internal model principle-based approach’, IET Control Theory Appl., 2012, 6, (5), pp. 715–725 (doi: 10.1049/iet-cta.2011.0106).
-
-
27)
-
24. Zhang, Y.W.: ‘Fault detection and diagnosis of nonlinear processes using improved kernel independent component analysis (KICA) and support vector machine (SVM)’, Ind. Eng. Chem. Res., 2008, 47, (18), pp. 6961–6971 (doi: 10.1021/ie071496x).
-
-
28)
-
26. Yu, J.: ‘A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes’. Chem. Eng. Sci., 2012, 68, (1), pp. 506–519 (doi: 10.1016/j.ces.2011.10.011).
-
-
29)
-
25. Yang, J., Gao, X., Zhang, D., Yang, J.Y.: ‘Kernel ICA: An alternative formulation and its application to face recognition’. Pattern Recognit., 2005, 38, (10), pp. 1784–1787 (doi: 10.1016/j.patcog.2005.01.023).
-
-
30)
-
23. Cui, P.L., Li, J.H., Wang, G.Z.: ‘Improved kernel principal component analysis for fault detection’, Expert Syst. Appl., 2008, 34, (2), pp. 1210–1219 (doi: 10.1016/j.eswa.2006.12.010).
-
-
31)
-
14. Lee, J.M., Qin, S.J., Lee, I.B.: ‘Fault detection and diagnosis based on modified independent component analysis’, AIChE J., 2006, 52, (10), pp. 3501–3514 (doi: 10.1002/aic.10978).
-
-
32)
-
7. Phaladiganon, P., Kim, S.B., Chen, V.C.P., Jiang, W.: ‘Principal component analysis-based control charts for multivariate nonnormal distributions’, Expert Syst. Appl., 2012, 4, (11), pp. 2527–2538.
-
-
33)
-
16. Rosipal, R., Trejo, L.J.: ‘Kernel partial least squares regress in reproducing kernel Hilbert space’, J. Mach. Learn. Res., 2002, 2, pp. 97–123.
-
-
34)
-
8. Kourti, T., MacGregor, J.F.: ‘Process analysis, monitoring and diagnosis, using multivariate projection methods’, Chemometr. Intell. Lab. Syst., 1995, 28, (1), pp. 3–21 (doi: 10.1016/0169-7439(95)80036-9).
-
-
35)
-
21. Cho, J.H., Lee, J.M., Choi, S.W., Lee, D.K., Lee, I.B.: ‘Fault identification for process monitoring using kernel principal component analysis’, Chem. Eng. Sci., 2005, 60, (1), pp. 279–288 (doi: 10.1016/j.ces.2004.08.007).
-
-
1)