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Multi-resolution screening of paper formation variations on production line
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
Department of Applied Electronics, Kaunas University of Technology, LT-51368 Kaunas, Lithuania.ORCID iD: 0000-0003-2185-8973
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
2009 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 36, no 2, part 2, p. 3144-3152Article in journal (Refereed) Published
Abstract [en]

This paper is concerned with a technique for detecting and monitoring abnormal paper formation variations in machine direction online in various frequency regions. A paper web is illuminated by two red diode lasers and the reflected light recorded as two time series of high resolution measurements constitute the input signal to the papermaking process monitoring system. The time series are divided into blocks and each block is analyzed separately. The task is treated as kernel based novelty detection applied to a multi-resolution time series representation obtained from the band-pass filtering of the Fourier power spectrum of the time series block. The frequency content of each frequency region is characterized by a feature vector, which is transformed using the kernel canonical correlation analysis and then categorized into the inlier or outlier class by the novelty detector. The ratio of outlying data points, significantly exceeding the predetermined value, indicates abnormalities in the paper formation. The experimental investigations performed have shown good repetitiveness and stability of the paper formation abnormalities detection results. The tools developed are used for online paper formation monitoring in a paper mill.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2009. Vol. 36, no 2, part 2, p. 3144-3152
Keyword [en]
Paper production, Support vector machine, Fourier transform, Canonical correlation analysis, Novelty detection
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-2176DOI: 10.1016/j.eswa.2008.01.043ISI: 000262178100057Scopus ID: 2-s2.0-56349109871Local ID: 2082/2573OAI: oai:DiVA.org:hh-2176DiVA, id: diva2:239394
Available from: 2008-12-03 Created: 2008-12-03 Last updated: 2018-03-23Bibliographically approved

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Ejnarsson, MarcusVerikas, AntanasNilsson, Carl Magnus

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