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Quality Classification and Monitoring Using Adaptive Metric Distance and Neural Networks: Application in Pickling Process
Abstract:Modern manufacturing facilities are large scale, highly complex, and operate with large number of variables under closed loop control. Early and accurate fault detection and diagnosis for these plants can minimise down time, increase the safety of plant operations, and reduce manufacturing costs. Fault detection and isolation is more complex particularly in the case of the faulty analog control systems. Analog control systems are not equipped with monitoring function where the process parameters are continually visualised. In this situation, It is very difficult to find the relationship between the fault importance and its consequences on the product failure. We consider in this paper an approach to fault detection and analysis of its effect on the production quality using an adaptive centring and scaling in the pickling process in cold rolling. The fault appeared on one of the power unit driving a rotary machine, this machine can not track a reference speed given by another machine. The length of metal loop is then in continuous oscillation, this affects the product quality. Using a computerised data acquisition system, the main machine parameters have been monitored. The fault has been detected and isolated on basis of analysis of monitored data. Normal and faulty situation have been obtained by an artificial neural network (ANN) model which is implemented to simulate the normal and faulty status of rotary machine. Correlation between the product quality defined by an index and the residual is used to quality classification.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056627Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1237
 S.Bouhouche, "Contribution to quality and process optimisation using mathematical modeling", Ph.D thesis, Institut f├╗r Maschinenbau, ISSN: 1617-3309, ID-Nr: 104 TU Bergakademie Freiberg Germany, 2002
 M.Norgaard, O. Ravn, N.K. Poulsen and L.K. Hansen, "Neural Nerworks for Modelling and Control of Dynamic Systems", ISBN 1- 85233-227-1, Springer-Verlag, Second Edition, 2001
 L.H.Chiang, E.L. Russel land R.D. Braatz, "Fault detection and Diagnosis in Industrial Systems", ISBN 1-85233-327-8, Springer- Verlag, Second Edition, 2001
 S.Bouhouche, M.S.Boucherit and M.Lahreche, "Improvement of breakout detection system in continuous casting process using neural networks", IEEE Proceedings on Advanced Process Control - Applications for Industry Workshop, Vancouver, Canada, pp 53-63, 2001
 G.Sorgrl, T.Poppe and M.schlang, "Real-time control with neural networks in steel processing", European Commission for Technical Steel Research, ECSC Workshop, Proceedings of Application of Artificial Neural Network Systems in the Steel Industry, Brussels, 22-23 January1998,
 D.Pham and X.Liu, "State space identification of dynamic systems using neural networks", Engineering Application in Artificial Intelligence, pp 198-203, (3),1990.
 D.Lee, J.S.Lee and T.Kang, "Adaptive fuzzy control of the molten steel level in a strip casting process", Control Engineering Practices, (11), pp 1511-1520, 1996
 C.Harris, M.Brown, K.M.Bossley, D.J.Mills and F.Ming, "Advances in neuro-fuzzy algorithms for real-time modelling and control, Engineering", Application of Artificial Intelligence, 9, (1), pp 1-16, 1996
 T.Kim and S.R.T.Kumara, "Boundary defect recognition using neural networks", International Journal of Production Research, 35, (9), pp 2397-2412, 1997,
 A.P.Loh, K.O.Looi and K.F.Fong, "Neural network modelling and control strategies for a pH process", Journal of Process Control, 15 (6), pp 355-362, 1995
 W.Zhenni, D.Christine, T.Ming and J.A.Morris, "A procedure for determining the topology of multilayer feedforward neural networks", Neural Networks, 7, (2), pp 291-300, 1994,
 D.Pham and X.Liu, "State space identification of dynamic systems using neural networks", Engineering Application in Artificial Intelligence, (3), pp 198-203, 1990.
 Bouhouche.S, Boucherit.MS, Lahreche.M and Bast.J, "Controlled Solidification in Continuous Casting Using Neural Networks", Book EPMESC IX Conference, Hong Kong November 2003