An Automatic Flaw Classification Method of Ultrasonic Nondestructive Testing for Pipeline Girth Welds
As flaw classification is normally manual determination in ultrasonic nondestructive testing field, an automatic identification of flaw type based on Lifted Wavelet Transform (LWT) and BP neural network (BPN) is introduced in this paper. LWT is proposed to extract flaw feature from ultrasonic echo signals, ideally matched local characteristics of original signals. The computational speed and flaw classification efficiency is increased. Then a feature library is constructed. A modified BPN is followed as a classifier, trained by the library. And then when feature is extracted from any other flaw echo, the feature eigenvector is sent to the trained BPN. The output of the BPN is the input flaw signals type, realizing automatic flaw classification. For comparison, a Radial Basis Function neural network (RBFN) is tested under the same condition as BPN. Experiment results prove the proposed method, LWT with BPN, is fit for automatic flaw classification.
Jian Li Xianglin Zhan Shijiu Jin
State Key Laboratory of Precision Measuring Technology & Instruments,Tianjin University,P.R.China Department of Electrical Engineering,Civil Aviation University of China,P.R.China
国际会议
2009 IEEE International Conference on Information and Automation(2009年 IEEE信息与自动化国际学术会议)
珠海、澳门
英文
1488-1493
2009-06-22（万方平台首次上网日期，不代表论文的发表时间）