Abstract
Closed-loop control is a basic technology in control engineering. Its role is to avoid the tracking error between set points and real variables. The evaluation of plant performance can be based on multivariate statistical process control connected to closed-loop errors behaviour. Due to its practicality, this approach has found many applications in several industries. This paper suggests a combined use of principal component analysis (PCA) and self-organisation map (SOM) algorithms to evaluate the process on the basis of closed-loop errors dynamic. Generally, it is possible to evaluate a product quality in the basis of the dynamic changes of the closed-loop control errors. In this paper, a new method based on the analysis of the control errors is proposed; it is carried out by a combined use of the PCA-SOM algorithm. Comparatively to the conventional PCA method, this new technique is characterised by the performant indexes that give an accurate evaluation of the process variability and its impact on the product quality. As shown in the different simulation results, the proposed approach gives a global evaluation and improves considerably the performance of computed indexes used for the evaluation of the controlled process.
Similar content being viewed by others
References
Yoon S, MacGregor JF (2000) Statistical and causal model-based approaches to fault detection and isolation. AIChE J 9:1813–1899
Jin HD, Lee YH, Lee G, Han CH (2006) Robust recursive principal component analysis modelling for adaptive monitoring. Indus Eng Chem Res 45:696–703
Narasimhan S, Shah S (2008) Model identification and error covariance matrix estimation from noisy data using PCA. Cont Eng Pract 16:146–155
Kourti T (2005) Application of latent variable methods to process control and multivariate statistical process control in industry. Int J Adapt Cont Sign Process 19:213–246
Lee YH, Jin HD, Han CH (2006) On-line process state classification for adaptive monitoring. Indus Eng Chem Res 45:3095–3107
Geng Z, Zhu Q (2005) Multiscale non linear principal component analysis and its application for chemical process monitoring. Indus Eng Chem Res 44:3585–3593
Olson DL, Delen D (2008) Advanced data mining techniques. Springer, New York
Min L, Degang C, Cheng W, Hongxing L (2006) Fuzzy reasoning based on a new fuzzy rough set and its application to scheduling problems. Comput Math Appl 51:1507–1518
Bouhouche S, Lahreche M, Bast J (2007) Quality monitoring using principal component analysis and fuzzy logic. Application in continuous casting process. Am J Appl Scien 4:637–644
Marjanovic O, Lennox B, Sandoz D, Smith K, Crofts M (2006) Real-time monitoring of an industrial batch process. Comput Chem Eng 30:1476–1481
Zhang Y, Dudzic MS (2006) Industrial application of multivariate SPC to continuous caster start–up operations for breakout prevention. Cont Eng Pract 14:1357–1375
Lee D, Moon CH, Moon SC, Park HD (2009) Development of healing control technology for reducing breakout in thin slab casters. Cont Eng Pract 17:3–13
Manabu K, Yoshiaki N (2008) Data-based process monitoring, process control, and quality improvement: recent development and applications in steel industry. Comput Chem Eng 32:12–24
Xuan TD, Srinivasan R (2008) Online monitoring of multi-phase batch processes using phase-based multivariate statistical process control. Comput Chem Eng 32:230–243
Kadlec P, Gabrys B, Strandt S (2009) Data-driven soft sensors in the process industry. Comput Chem Eng 33:795–814
Vesanto J (2000) Using SOM in data mining. Licentiate thesis, Helsinki University of Technology
Kohonen T (2001) Self-organizing maps, 3rd edn. Springer, Heidelberg
Chiang LH, Russell EL, Braatz RD (2001) Fault detection and diagnosis in industrial systems, 2nd edn. Springer, London Berlin Heidelberg
Kelegama DC, Hua LL, Qin LJ (2000) Self organization map for clustering and classification. Ecol Agent Organ 7:53–56
Joliffe IT (2002) Principal component analysis. Springer, Heidelberg
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Salah, B., dit Leksir Yazid, L. & Jurgen, B. Evaluation-based closed-loop errors using principal component analysis and self-organisation map with an application to a pickling process. Int J Adv Manuf Technol 70, 1033–1041 (2014). https://doi.org/10.1007/s00170-013-5341-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-013-5341-y