Abstract
This paper is concerned with data mining techniques for identifying the main parameters of the printing press, the printing process and paper affecting the occurrence of paper web breaks in a pressroom. Two approaches are explored. The first one treats the problem as a task of data classification into “break” and “non break” classes. The procedures of classifier design and selection of relevant input variables are integrated into one process based on genetic search. The search process results in a set of input variables providing the lowest average loss incurred in taking decisions. The second approach, also based on genetic search, combines procedures of input variable selection and data mapping into a low dimensional space. The tests have shown that the web tension parameters are amongst the most important ones. It was also found that, provided the basic off-line paper parameters are in an acceptable range, the paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the off-line ones. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of the break cases was equal to 76.7%.
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References
Perner, P.: Knowledge-based image inspection system for automatic defect recognition, classification and process diagnosis. Machine Vision and Applications 7, 135–147 (1994)
Klapproth, U., Perner, P., Böhme, B.: Einsatz eines expertensystems zur diagnose von druckfehlern im offsetdruck. Messen steuern regeln 34, 115–120 (1991)
Parola, M., Kaljunen, T., Beletski, N., Paukku, J.: Analysing printing press runnability by data mining. In: Prceedings of the TAGA Conference, Montreal, Canada, pp. 435–451 (2003)
Roisum, R.: Runnability of paper. Part 1: Predicting runnability. TAPPI Journal 73, 97–101 (1990)
Hristopulos, D.T., Uesaka, T.: A model of machine-direction tension variations in paper webs with runnability applications. Journal of Pulp and Paper Science 28, 389–394 (2002)
Bacauskiene, M., Verikas, A., Gelzinis, A., Valincius, D.: A feature selection technique for generation of classification committees and its application to categorization of laryngeal images. Pattern Recognition 42, 645–654 (2009)
Miyaishi, T., Shimada, H.: Using neural networks to diagnose web breaks on a newsprint paper machine. TAPPI Journal 81, 163–170 (1998)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Ann Arbor (1975)
Demartines, P., Herault, J.: Curvilinear component analysis: A self-organizing neural network for nonlinear mapping of data sets. IEEE Trans Neural Networks 8, 148–154 (1997)
Verikas, A., Malmqvist, K., Malmqvist, L., Bergman, L.: A new method for colour measurements in graphic arts. Color Research & Application 24, 185–196 (1999)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans System, Man and Cybernetics 3, 610–621 (1973)
Gelzinis, A., Verikas, A., Bacauskiene, M.: Increasing the discrimination power of the co-occurrence matrix-based features. Pattern Recognition 40, 2367–2372 (2007)
Leung, K.F., Leung, F.H.F., Lam, H.K., Ling, S.H.: Application of a modified neural fuzzy network and an improved genetic algorithm to speech recognition. Neural Computing & Applications 16, 419–431 (2007)
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
Borg, I., Groenen, P.J.F.: Modern Multidimensional Scaling: Theory and Applications. Springer, New York (1997)
Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Tans. Computers 18, 401–409 (1969)
Bishop, C.M., Svensen, M., Williams, C.K.I.: GTM: The generative topographic mapping. Neural Computation 10, 215–234 (1998)
Kohonen, T.: The self-organizing map. Proceedings of the IEEE 78, 1461–1480 (1990)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Verikas, A., Malmqvist, K., Bacauskiene, M., Bergman, L.: Monitoring the de-inking process through neural network-based colour image analysis. Neural Computing & Applications 9, 142–151 (2000)
Bauer, H.U., Pawlezik, K.R.: Quantifying the neighbourhood preservation of self-organizing feature maps. IEEE Trans Neural Networks 3, 570–579 (1992)
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Alzghoul, A., Verikas, A., Hållander, M., Bacauskiene, M., Gelzinis, A. (2009). Screening Paper Runnability in a Web-Offset Pressroom by Data Mining. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2009. Lecture Notes in Computer Science(), vol 5633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03067-3_14
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DOI: https://doi.org/10.1007/978-3-642-03067-3_14
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