Detection of transients in steel casting through standard and ai-based techniques

Authors:
Valentina Colla;Marco Vannucci;Nicola Matarese;Gerard Stephens;Marco Pianezzola;Izaskun Alonso;Torsten Lamp;Juan Palacios;Siegfried Schiewe
Affiliations:
Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, Pontedera, Italy;Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, Pontedera, Italy;Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, Pontedera, Italy;Tata Steel UK Ltd;Riva Acciaio Spa;GERDAU Sidenor I+D;VDEh-Betriebsforschungsinstitut GmbH;TECNALIA Research & innovation;ArcelorMittal Ruhrort GmbH
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Year:
2011

Citing 3
Cited 0

Ten lectures on wavelets

Ten lectures on wavelets
Transient Detection With Cross Wavelet Transforms and Wavelet Coherence

IEEE Transactions on Signal Processing
Neural networks for classification: a survey

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Quantified Score

Hi-index	0.00

Visualization

Abstract

The detection of transients in the practice of continuous casting within a steel-making industry is a key task for the prediction of final product properties but currently a direct observation of this phenomenon is not available. For this reason in this paper several standard and soft-computing based methods for the detection of transients from plant data will be tested and compared. From the obtained results it emerges that the use of a fuzzy inference system based on experts knowledge achieves very satisfactory results correctly identifying most of the transient events present in the databases provided by different companies.