MFL signals and artificial neural networks applied to detection and classification of pipe weld defects
MFL signals and artificial neural networks applied to detection and classification of pipe weld defects
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DOI：
10.1016/j.ndteint.2006.04.003
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发表时间：
2006-12-01
影响因子：
4.2
通讯作者：
Miranda, I. V. J.
中科院分区：
文献类型：
--
作者：
Carvalho, A. A.;Rebello, J. M. A.;Miranda, I. V. J.
这项工作评估使用人工神经网络（ANN）的模式识别的漏磁（MFL）信号在管道焊缝智能清管器获得。最初，人工神经网络被用来区分模式信号与非缺陷（ND）和信号与缺陷（D）沿沿着。在接下来的步骤中，人工神经网络被应用于分类信号模式与三种类型的缺陷在焊接接头：外部腐蚀（EC），内部腐蚀（IC）和未焊透（LP）。将缺陷有意地插入外径为304.8 mm的API 5L-X65钢管道的焊缝中。以这种方式，将用1025个点数字化的MFL信号本身用作ANN输入。最初，信号被用作神经网络的输入，没有进行任何类型的预处理，后来对信号应用了四种类型的预处理：傅里叶分析、移动平均滤波器、小波分析和Savitzky-Golay滤波器。采用信号处理技术提高了神经网络在缺陷分类中的性能，结果表明，神经网络对D类和ND类信号的识别率为94.2%，对腐蚀（CO）和LP类信号的识别率为92.5%。也可以使用神经网络对缺陷模式信号进行分类：EC、IC和LP，验证集的平均成功率为71.7%。(C)2006爱思唯尔有限公司保留所有权利。
This work evaluates the use of artificial neural networks (ANNs) for pattern recognition of magnetic flux leakage (MFL) signals in weld joints of pipelines obtained by intelligent pig. Initially the ANNs were used to distinguish the pattern signals with non-defect (ND) and signals with defects (D) along of the weld bead. In the next step the ANNs were applied to classify signal patterns with three types of defects in the weld joint: external corrosion (EC), internal corrosion (IC) and lack of penetration (LP). The defects were intentionally inserted in the weld bead of a pipeline of API 5L-X65 steel with an outer diameter of 304.8 mm. In this way, the MFL signal itself, digitized with 1025 points, was used as the ANN input. Initially the signals were used as inputs for the neural network without any type of pre-processing, later four types of pre-processing were applied to the signals: Fourier analysis, Moving-average filter, Wavelet analysis and Savitzky-Golay filter. Signal processing techniques were employed to improve the performance of the neural networks in distinguishing between the defect classes.The results showed that it is possible to classify signals of classes D and ND using ANN with very efficient results (94.2%), as well as for corrosion (CO) and LP signals (92.5%). Also it is possible to classify the defect pattern signals: EC, IC and LP using neural networks with an average rate of success of 71.7% for the validation set. (C) 2006 Elsevier Ltd. All rights reserved.