A quality-related fault detection approach based on dynamic least squares for process monitoring

IEEE Transactions on Industrial Electronics

Volumn 63, Issue 4, 2016, Pages 2625-2632

  Jiao, Jianfang ^a   Yu, Han ^b   Wang, Guang ^a  

^a BOHAI UNIVERSITY   (China)

^b HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Auto regressive model; Data driven; Dynamic system; Least squares; Process monitoring; Quality related fault detection

Indexed keywords

DYNAMICAL SYSTEMS; LEAST SQUARES APPROXIMATIONS; PROCESS CONTROL; PROCESS MONITORING; SIGNAL ENCODING;

AUTO REGRESSIVE MODELS; AUTOREGRESSIVE MOVING AVERAGE; DATA DRIVEN; LEAST SQUARE; LEAST-SQUARES APPROACH; OBLIQUE PROJECTIONS; PARTIAL LEAST SQUARE (PLS); TIME SERIES MODELING;

FAULT DETECTION;

EID: 84963734946     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2015.2497204     Document Type: Article

References (18)

1. S. Yin, S. X. Ding, X. Xie, and H. Luo, "A review on basic data-driven approaches for industrial process monitoring," IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 6418-6428, Nov. 2014.
2. S. Yin, X. Li, H. Gao, and O. Kaynak, "Data-based techniques focused on modern industry: An overview," IEEE Trans. Ind. Electron., vol. 62, no. 1, pp. 657-667, Jan. 2015.
3. G. Li, S. J. Qin, and D. Zhou, "Geometric properties of partial least squares for process monitoring," Automatica, vol. 46, no. 1, pp. 204-210, 2010.
4. D. Zhou, G. Li, and S. J. Qin, "Total projection to latent structures for process monitoring," AIChE J., vol. 56, no. 1, pp. 168-178, 2010.
5. S. Yin, S. X. Ding, P. Zhang, A. Haghani Abandan Sari, and A. Naik, "Study on modifications of PLS approach for process monitoring," in Proc. World Congr., 2011, vol. 18, pp. 12389-12394.
6. S. J. Qin and Y. Zheng, "Quality-relevant and process-relevant fault monitoring with concurrent projection to latent structures," AIChE J., vol. 59, no. 2, pp. 496-504, 2013.
7. G. Wang and S. Yin, "Quality-related fault detection approach based on orthogonal signal correction and modified PLS," IEEE Trans. Ind. Informat., vol. 11, no. 2, pp. 398-405, Apr. 2015.
8. K. Peng, K. Zhang, and G. Li, "Quality-related process monitoring based on total Kernel PLS model and its industrial application," Math. Prob. Eng., vol. 2013, 2013.
9. Y. Zhang, R. Sun, and Y. Fan, "Fault diagnosis of nonlinear process based on KCPLS reconstruction," Chemom. Intell. Lab. Syst., vol. 140, pp. 49-60, 2015.
10. K. Zhang, H. Hao, Z. Chen, S. X. Ding, and K. Peng, "A comparison and evaluation of key performance indicator-based multivariate statistics process monitoring approaches," J. Process Control, vol. 33, pp. 112-126, 2015.
11. W. Ku, R. H. Storer, and C. Georgakis, "Disturbance detection and isolation by dynamic principal component analysis," Chemom. Intell. Lab. Syst., vol. 30, no. 1, pp. 179-196, 1995.
12. N. L. Ricker, "The use of biased least-squares estimators for parameters in discrete-time pulse-response models," Ind. Eng. Chem. Res., vol. 27, no. 2, pp. 343-350, 1988.
13. N. Fletcher, A. Morris, G. Montague, and E. Martin, "Local dynamic partial least squares approaches for the modelling of batch processes," Can. J. Chem. Eng., vol. 86, no. 5, pp. 960-970, 2008.
14. J. Chen and K.-C. Liu, "On-line batch process monitoring using dynamic PCA and dynamic PLS models," Chem. Eng. Sci., vol. 57, no. 1, pp. 63-75, 2002.
15. G. Lee, S.-O. Song, and E. S. Yoon, "Multiple-fault diagnosis based on system decomposition and dynamic PLS," Ind. Eng. Chem. Res., vol. 42, no. 24, pp. 6145-6154, 2003.
16. Q. Liu, S. J. Qin, and T. Chai, "Quality-relevant monitoring and diagnosis with dynamic concurrent projection to latent structures," in Proc. World Congr., 2014, vol. 19, pp. 2740-2745.
17. G. Li, B. Liu, S. J. Qin, and D. Zhou, "Quality relevant data-driven modeling and monitoring of multivariate dynamic processes: The dynamic T-PLS approach," IEEE Trans. Neural Netw., vol. 22, no. 12, pp. 2262-2271, Dec. 2011.
18. S. Wold, M. Sjöström, and L. Eriksson, "PLS-regression: A basic tool of chemometrics," Chemom. Intell. Lab. Syst., vol. 58, no. 2, pp. 109-130, 2001.