Comparison of pattern recognition techniques for the classification of impact acoustic emissions

Emerging Technologies

Document identifier: oai:dalea.du.se:2714
Access full text here:10.1016/j.trc.2007.05.004
Keyword: Natural Sciences, Computer and Information Sciences, Naturvetenskap, Data- och informationsvetenskap, Transportation; Pattern recognition; Speech recognition; Signal analysis; Non-destructive testing, Automatisk inspektion av järnvägsslipers
Publication year: 2007
Abstract:

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.

Authors

Siril Yella

Högskolan Dalarna; Datateknik
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Narendra Gupta

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Mark Dougherty

Högskolan Dalarna; Datateknik
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