A novel scheme for key performance indicator prediction and diagnosis with application to an industrial hot strip mill

IEEE Transactions on Industrial Informatics

Volumn 9, Issue 4, 2013, Pages 2239-2247

  Ding, Steven X ^a   Yin, Shen ^b   Peng, Kaixiang ^c   Hao, Haiyang ^a   Shen, Bo ^a  

^a UNIVERSITY OF DUISBURG ESSEN   (Germany)

^b HARBIN INSTITUTE OF TECHNOLOGY   (China)

^c UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING   (China)

Author keywords

Data driven; hot strip mill; key performance indicator (KPI); prediction and diagnosis

Indexed keywords

COMPLEX INDUSTRIAL PROCESS; DATA-DRIVEN; DIAGNOSIS ALGORITHMS; HOT STRIP MILLS; KEY PERFORMANCE INDICATORS; LEFT-COPRIME FACTORIZATIONS; PARTIAL LEAST SQUARE (PLS); PREDICTION PERFORMANCE;

FORECASTING; LEAST SQUARES APPROXIMATIONS;

BENCHMARKING;

EID: 84886674516     PISSN: 15513203     EISSN: None     Source Type: Journal    
DOI: 10.1109/TII.2012.2214394     Document Type: Article

References (29)

1. J. Gertler, Fault Detection and Diagnosis in Engineering Systems. New York: Marcel Dekker, 1998.
2. S. X. Ding, Model-Based Fault Diagnosis Techniques. Berlin, Germany: Springer, 2008.
3. Z. Gao, X. Dai, T. Breikin, and H. Wang, "Novel parameter identification by using a high-gain observer with application to a gas turbine engine," IEEE Trans. Ind. Inf., vol. 4, no. 4, pp. 271-279, Nov. 2008.
4. B. Shen, Z. Wang, H. Shu, and G. Wei, "On nonlinear ff., filtering for discrete-time stochastic systems with missing measurements," IEEE Trans. Autom. Control, vol. 53, no. 10, pp. 2170-2180, Oct. 2008.
5. L. Chiang, E. Russell, and R. Braatz, Fault Detection and Diagnosis in Industrial Systems. London, U.K.: Springer-Verlag, 2001.
6. R. Gong, S. Huang, and T. Chen, "Robust and efficient rule extraction through data summarization and its application in welding fault diagnosis," IEEE Trans. Ind. Inf., vol. 4, no. 3, pp. 198-206, Aug. 2008.
7. Y. Zhang, H. Zhou, S. Qin, and T. Chai, "Decentralized fault diagnosis of large-scale processes using multiblock kernel partial least squares," IEEE Trans. Ind. Inf., vol. 6, no. 1, pp. 3-10, Feb. 2010.
8. H. Saxen and F. Pettersson, "Nonlinear prediction of the hot metal silicon content in the blast furnace," ISIJ Int., vol. 47, no. 12, pp. 1732-1737, 2007. (Pubitemid 351257096)
9. J. Zhou, K. Chee, F. Lewis, and Z. Zhong, "Intelligent diagnosis and prognosis of tool wear using dominant feature identification," IEEE Trans. Ind. Inf, vol. 5, no. 4, pp. 454-464, Nov. 2009.
10. A. Nurkkala, F. Pettersson, and H. Saxen, "Nonlinear modeling method applied to prediction of hot metal silicon in the ironmaking blast furnace," Ind. Eng. Chemistry Res., vol. 50, no. 15, pp. 9236-9248, 2011.
11. D. Wang, J. Liu, and R. Srinivasan, "Data-driven soft sensor approach for quality prediction in a refining process," IEEE Trans. Ind. Inf, vol. 6, no. 1, pp. 11-17, Feb. 2010.
12. C Gao, L. Jian, and S. Luo, "Modeling of the thermal state change of blast furnace hearth with support vector machines," IEEE Trans. Ind. Electron., vol. 59, no. 2, pp. 1134-1145, Feb. 2012.
13. D. Wang, "Robust data-driven modeling approach for real-time final product quality prediction in batch process operation," IEEE Trans. Ind. Inf, vol. 7, no. 2, pp. 337-371, May 2011.
14. S. Qin, "Statistical process monitoring: Basics and beyond," J. Chemo-metrics, vol. 17, no. 8-9, pp. 480-502, Aug.-Sep. 2003.
15. W. Chuang, C. Chen, J. Yen, and Y. Hsu, "Using MPCA of spectra model for fault detection in a hot strip mill," J. Mater. Process. Technol., vol. 209, no. 8, pp. 4162-4168, Apr. 2009.
16. A. Höskuldsson, "PLS regression methods," Journal of Chemometrics, vol. 2, pp. 211-228, 1988.
17. M. Ma, D. Wong, S. Jang, and S. Tseng, "Fault detection based on statistical multivariate analysis and microarray visualization," IEEE Trans. Ind. Inf., vol. 6, no. 1, pp. 18-24, Feb. 2010.
18. G. Li, S. Qin, and D. Zhou, "Geometric properties of partial least squares for process monitoring," Automatica, vol. 46, no. 1, pp. 204-210, Jan. 2010.
19. D. Zhou, G. Li, and S. Qin, "Total projection to latent structures for process monitoring," AIChE J., vol. 56, no. 1, pp. 168-178, Jan. 2010.
20. C. Gao, J. Zeng, and Z. Zhou, "Identification of multiscale nature and multiple dynamics of the blast furnace system from operating data," AIChE J., vol. 57, no. 12, pp. 3448-3458, Dec. 2011.
21. M. Jia, F. Chu, F. Wang, and W. Wang, "On-line batch process monitoring using batch dynamic kernel principal component analysis," Chemometrics Intell. Lab. Syst., vol. 101, no. 2, pp. 110-122, Apr. 2010.
22. Y. Yao, T. Chen, and F. Gao, "Multivariate statistical monitoring of two-dimensional dynamic batch processes utilizing non-Gaussian information," J. Process Control, vol. 20, no. 10, pp. 1188-1197, Dec. 2010.
23. S. Yin, S. X. Ding, P. Zhang, A. Haghani, and A. Naik, "Study on modifications of PLS approach for process monitoring," in Proc. 18th IFAC World Congress, Milan, Italy, 2011, pp. 12389-12394.
24. J. Wang and S. Qin, "A new subspace identification approach based on principal component analysis," J. Process Control, vol. 12, no. 8, pp. 841-855, Dec. 2002. (Pubitemid 34772635)
25. S. X. Ding, P. Zhang, A. Naik, E. Ding, and B. Huang, "Subspace method aided data-driven design of fault detection and isolation systems," J. Process Control, vol. 19, no. 9, pp. 1496-1510, Oct. 2009.
26. M. Verhaegen and X. Yu, "A class of subspace model identification algorithms to identify periodically and arbitrarily time-varying systems," Automatica, vol. 31, no. 2, pp. 201-216, Feb. 1995.
27. W. Favoreel, B. D. Moor, and P. V. Overschee, "Subspace state space system identification for industrial processes," J. Process Control, vol. 10, no. 2-3, pp. 149-155, Apr. 2000. (Pubitemid 30556712)
28. S. Qin, "An overview of subspace identification," Comput. Chem. Eng., vol. 30, no. 10-12, pp. 1502-1513, Sep. 2006. (Pubitemid 44292280)
29. S. X. Ding, "Data-driven design of model-based fault diagnosis systems," in Proc. IFAC Symp. ADCHEM, Singapore, 2012, pp. 840-847.