DETECTION OF THE NEED FOR A MODEL UPDATE IN STEEL MANUFACTURING

Heli Koskimäki (née Junno), Ilmari Juutilainen, Perttu Laurinen, Juha Röning

Abstract

When new data are obtained or simply when time goes by, the prediction accuracy of models in use may decrease. However, the question is when prediction accuracy has dropped to a level where the model can be considered out of date and in need of updating. This article describes a method that was developed for detecting the need for a model update. The method is applied in the steel industry, and the model whose need of updating is under study is a regression model developed to model the yield strength of steel plates. It is used to plan process settings in steel plate product manufacturing. To decide on the need for updating, information from similar past cases was utilized by introducing a limit called an exception limit. The limit was used to indicate when a new observation was from an area of the model input space where the prediction errors of the model have been too high. Moreover, an additional limit was formed to indicate when too many exceedings of the exception limit have occurred within a certain time scale. These two limits were then used to decide when to update the model.

References

  1. Gabrys, B., Leiviskä, K., and Strackeljan, J. (2005). Do Smart Adaptive Systems Exist, A Best-Practice for Selection and Combination of Intelligent Methods. Springer-Verlag, Berlin, Heidelberg.
  2. Haykin, S. (1999). Neural Networks, A Comprehensive Foundation. Prentice Hall, Upper Saddle River, New Jersey.
  3. Juutilainen, I. and R öning, J. (2006). Planning of strength margins using joint modelling of mean and dispersion. Materials and Manufacturing Processes, 21:367-373.
  4. Juutilainen, I. and R öning, J. (2007, in press). A method for measuring distance from a training data set. Communications in Statistics.
  5. Juutilainen, I., R öning, J., and Myllykoski, L. (2003). Modelling the strength of steel plates using regression analysis and neural networks. Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation, pages 681-691.
  6. Khattree, R. and Rao, C., editors (2003). Statistics in industry - Handbook of statistcs 22. Elsevier.
  7. Yang, M., Zhang, H., Fu, J., and Yan, F. (2004). A framework for adaptive anomaly detection based on support vector data description. Lecture Notes in Computer Science, Network and Parallel Computing, pages 443-450.
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Paper Citation


in Harvard Style

Koskimäki (née Junno) H., Juutilainen I., Laurinen P. and Röning J. (2007). DETECTION OF THE NEED FOR A MODEL UPDATE IN STEEL MANUFACTURING . In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-972-8865-82-5, pages 55-59. DOI: 10.5220/0001639500550059


in Bibtex Style

@conference{icinco07,
author={Heli Koskimäki (née Junno) and Ilmari Juutilainen and Perttu Laurinen and Juha Röning},
title={DETECTION OF THE NEED FOR A MODEL UPDATE IN STEEL MANUFACTURING},
booktitle={Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2007},
pages={55-59},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001639500550059},
isbn={978-972-8865-82-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - DETECTION OF THE NEED FOR A MODEL UPDATE IN STEEL MANUFACTURING
SN - 978-972-8865-82-5
AU - Koskimäki (née Junno) H.
AU - Juutilainen I.
AU - Laurinen P.
AU - Röning J.
PY - 2007
SP - 55
EP - 59
DO - 10.5220/0001639500550059