hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Data Mining and Analysis for Characterizing Paper from On-line Multisensor Measurements
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
2008 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

The objective of this thesis is to develop a multi-resolution tool for screening paper formation variations, aiming to detect abnormalities in various frequency regions ranging from millimeters to several meters. The abnormalities detected in different frequency regions give an indication for the paper maker about specific disturbances in the paper production process. A paper web, running at a speed of 30 m/s, is illuminated by two red diode lasers and the reflected light are recorded as two time series of high resolution measurements constitutes 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 a kernel based novelty detection applied to a multi-resolution time series representation obtained from the frequency bands of the Fourier power spectra of the blocks. The frequency content of each frequency region is characterized by a feature vector, which is transformed using the 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 that the presented paper formation deficiencies monitoring technique and the system can be used for on-line monitoring of paper deficiencies manifesting themselves in a broad frequency range. A software, implementing the technique, was developed and used for online paper formation monitoring at a Swedish paper mill.

Place, publisher, year, edition, pages
Göteborg: Department of Signals and Systems, Chalmers University of Technology , 2008. , p. 62
Series
Technical report R, ISSN 1403-266X ; 2008:2
Keywords [en]
Feature Extraction, Kernel Based Novelty Detection, Kernel CCA, Monitoring, Newsprint, Paper Web, Time-Series Representation
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-1975Local ID: 2082/2370OAI: oai:DiVA.org:hh-1975DiVA, id: diva2:239193
Presentation
2008-02-08, Wigforssalen, Högskolan i Halmstad, Kristian IV:s väg 3, Halmstad, 10:00 (English)
Opponent
Available from: 2008-09-29 Created: 2008-09-29 Last updated: 2018-03-23Bibliographically approved

Open Access in DiVA

No full text in DiVA

Authority records BETA

Ejnarsson, Marcus

Search in DiVA

By author/editor
Ejnarsson, Marcus
By organisation
Halmstad Embedded and Intelligent Systems Research (EIS)
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 95 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf