Paper Title:
Research on Automatic Flaw Classification and Feature Extraction of Ultrasonic Testing
  Abstract

In this paper, Lifted Wavelet Transform (LWT) and BP neural network are used for automatic flaw classification of pipeline girth welds. LWT is proposed to extract flaw feature from ultrasonic echo signals, ideally matched local characteristics of original signal and increasing the computational speed and flaw classification efficiency. After extracting features of all flaw echoes, a feature library is constructed. A modified BP neural network is followed as a classifier, trained by the library. 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 BP neural network, is more fit for automatic flaw classification than traditional methods.

  Info
Periodical
Key Engineering Materials (Volumes 381-382)
Edited by
Wei Gao, Yasuhiro Takaya, Yongsheng Gao and Michael Krystek
Pages
631-634
DOI
10.4028/www.scientific.net/KEM.381-382.631
Citation
J. Li, X. Zhan, J. Zhuge, Z. Zeng, S. J. Jin, "Research on Automatic Flaw Classification and Feature Extraction of Ultrasonic Testing", Key Engineering Materials, Vols. 381-382, pp. 631-634, 2008
Online since
June 2008
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Price
$32.00
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