du.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Condition monitoring using pattern recognition techniques on data from acoustic emissions
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
2006 (engelsk)Inngår i: ICMLA 2006: 4th International Conference on Machine Learning and Applications, Proceedings, 2006Konferansepaper, Publicerat paper (Annet vitenskapelig)
Abstract [en]

Condition monitoring applications deploying the usage of impact acoustic techniques are mostly done intuitively by skilled personnel. In this article, a pattern recognition approach is taken to automate such intuitive human skills for the development of more robust and reliable testing methods. The focus of this work is to use the approach as a part of a major research project in the rail inspection area, within the domain of Intelligent Transport Systems. Data from impact acoustic tests made on wooden beams have been used. The relation between condition of the wooden beams and respective sounds they make when struck, has been analyzed experimentally. Features were extracted from the acoustic emissions of wooden beams and were used for pattern classification. Features such as magnitude of the signal, natural logarithm of the magnitude and Mel-frequency cepstral coefficients, yielded good results. The extracted feature vectors were used as input to various pattern classifiers for further pattern recognition task. The effect of using classifiers like Support vector machines and Multi-layer perceptron has been tested and compared. Results obtained experimentally, demonstrate that Support vector machines provide good detection rates for the classification of impact acoustic signals in the NDT domain.

sted, utgiver, år, opplag, sider
2006.
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys, Automatisk inspektion av järnvägsslipers
Identifikatorer
URN: urn:nbn:se:du-2707DOI: doi.ieeecomputersociety.org/10.1109/ICMLA.2006.19ISI: 000244477800001OAI: oai:dalea.du.se:2707DiVA, id: diva2:521726
Konferanse
International Conference on Machine Learning and Applications, , Orlando, USA, December 14-16, 2006
Tilgjengelig fra: 2007-04-10 Laget: 2007-04-10 Sist oppdatert: 2018-01-12bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fullteksthttp://www.computer.org/csdl/proceedings/icmla/2006/2735/00/27350003-abs.html

Søk i DiVA

Av forfatter/redaktør
Yella, SirilDougherty, Mark
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 610 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf