du.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
Nonstationary feature extraction techniques for automatic classification of impact acoustic signals
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2008 (English)Independent thesis Advanced level (degree of Master (Two Years))Student thesis
Abstract [en]

Condition monitoring of wooden railway sleepers applications are generally carried out by visual inspection and if necessary some impact acoustic examination is carried out intuitively by skilled personnel. In this work, a pattern recognition solution has been proposed to automate the process for the achievement of robust results. The study presents a comparison of several pattern recognition techniques together with various nonstationary feature extraction techniques for classification of impact acoustic emissions. Pattern classifiers such as multilayer perceptron, learning cector quantization and gaussian mixture models, are combined with nonstationary feature extraction techniques such as Short Time Fourier Transform, Continuous Wavelet Transform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to the presence of several different feature extraction and classification technqies, data fusion has been investigated. Data fusion in the current case has mainly been investigated on two levels, feature level and classifier level respectively. Fusion at the feature level demonstrated best results with an overall accuracy of 82% when compared to the human operator.

Place, publisher, year, edition, pages
Borlänge, 2008. , 72 p.
Keyword [en]
railway sleepers
Identifiers
URN: urn:nbn:se:du-3592OAI: oai:dalea.du.se:3592DiVA: diva2:518528
Uppsok
Technology
Supervisors
Available from: 2008-12-05 Created: 2008-12-05 Last updated: 2012-04-24Bibliographically approved

Open Access in DiVA

fulltext(983 kB)680 downloads
File information
File name FULLTEXT01.pdfFile size 983 kBChecksum SHA-512
70fbf332a1209d0541c7e2c7da8c331651035c8641d0d13ec3a043b57811dc6aa8aa9c755cf9be3d2681d2f19385f0ff2aa6e6349891fc56a1a11519dd81035d
Type fulltextMimetype application/pdf

By organisation
Computer Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 680 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 578 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