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Comparison of pattern recognition techniques for the classification of impact acoustic emissions
Dalarna University, School of Technology and Business Studies, Computer Engineering.
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2007 (English)In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 15, no 6, p. 345-360Article in journal (Refereed) Published
Abstract [en]

Current day condition monitoring applications involving wood are mostly carried out through visual inspection and if necessary some impact acoustic examination is carried out. These inspections are mainly done intuitively by skilled personnel. In this paper, a pattern recognition approach has been considered to automate such intuitive human skills for the development of robust and reliable methods within the area. The study presents a comparison of several pattern recognition techniques combined with various stationary feature extraction techniques for classification of impact acoustic emissions. Further issues concerning feature fusion are discussed as well. It is hoped that this kind of broad analysis could be used to handle a wide spectrum of tasks within the area, and would provide a perfect ground for future research directions. A brief introduction to the techniques is provided for the benefit of the readers unfamiliar with the techniques. Pattern classifiers such as support vector machines, etc. are combined with stationary feature extraction techniques such as linear predictive cepstral coefficients, etc. Results from support vector machines in combination with linear predictive cepstral coefficients delivered good classification rates. However, Gaussian mixture models delivered higher classification rates when feature fusion is proposed.

Place, publisher, year, edition, pages
2007. Vol. 15, no 6, p. 345-360
Keywords [en]
Transportation; Pattern recognition; Speech recognition; Signal analysis; Non-destructive testing
National Category
Computer and Information Sciences
Research subject
Komplexa system - mikrodataanalys, Automatisk inspektion av järnvägsslipers
Identifiers
URN: urn:nbn:se:du-2714DOI: 10.1016/j.trc.2007.05.004ISI: 000251106700001OAI: oai:dalea.du.se:2714DiVA, id: diva2:519817
Available from: 2007-04-10 Created: 2007-04-10 Last updated: 2018-01-12Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • 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