ISSN:
1013-9826
Source:
Scientific.Net: Materials Science & Technology / Trans Tech Publications Archiv 1984-2008
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
In this paper, Lifted Wavelet Transform (LWT) and BP neural network are used forautomatic flaw classification of pipeline girth welds. LWT is proposed to extract flaw feature fromultrasonic echo signals, ideally matched local characteristics of original signal and increasing thecomputational speed and flaw classification efficiency. After extracting features of all flaw echoes, afeature library is constructed. A modified BP neural network is followed as a classifier, trained by thelibrary. When feature of any flaw echo is extracted and sent to BP network, flaw type is the output,realizing automatic flaw classification. Experiment results prove the proposed method, LWT with BPneural network, is more fit for automatic flaw classification than traditional methods
Type of Medium:
Electronic Resource
URL:
http://www.tib-hannover.de/fulltexts/2011/0528/01/57/transtech_doi~10.4028%252Fwww.scientific.net%252FKEM.381-382.631.pdf
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