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Fault detection in hot steel rolling using neural networks and multivariate statistics

Lookup NU author(s): Dr Yougeshwar Bissessur, Professor Elaine Martin, Emeritus Professor Julian Morris


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The paper addresses the issue of maintaining consistent high quality production in the steel industry by extending techniques emanating from the fields of neural networks and multivariate statistics. Process diagnostic methodologies based on these tools were developed and applied to a six-stand hot rolling mill. The objective was to achieve better mill setup parameters so that manufactured coils consistently meet the required customer specifications. A wavelet neural network was successfully used for modelling the mill parameters and for detecting errors in the rolling stand settings. Model prediction accuracy and robustness were enhanced through stacked generalization. Multivariate statistical performance monitoring techniques were then applied on top of the mill control systems to provide early warning of strips being badly rolled. Both approaches yielded comparable results on monitored data from a hot strip mill and, in combination, provided enhanced manufacturing performance.

Publication metadata

Author(s): Martin EB; Bissessur Y; Morris AJ; Kitson P

Publication type: Article

Publication status: Published

Journal: IEE Proceedings D: Control Theory and Applications

Year: 2000

Volume: 147

Issue: 6

Pages: 633-640

ISSN (print): 1350-2379

ISSN (electronic): 1751-8652

Publisher: Institution of Electronic and Electrical Engineers


DOI: 10.1049/ip-cta:20000763


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