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首页> 外文期刊>NDT & E international >MFL signals and artificial neural networks applied to detection and classification of pipe weld defects
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MFL signals and artificial neural networks applied to detection and classification of pipe weld defects

机译：MFL信号和人工神经网络在管道焊缝缺陷检测和分类中的应用

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摘要

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 percent), as well as for corrosion (CO) and LP signals (92.5 percent). 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 percent for the validation set.
机译：这项工作评估了人工神经网络（ANN）在智能猪获得的管道焊缝中磁通量泄漏（MFL）信号模式识别中的应用。最初，人工神经网络用于区分焊缝沿线的无缺陷（ND）模式信号和有缺陷（D）信号。在下一步中，将ANN用于对焊缝中具有三种类型缺陷的信号模式进行分类：外部腐蚀（EC），内部腐蚀（IC）和缺乏渗透（LP）。将缺陷故意插入外径为304.8 mm的API 5L-X65钢管道的焊缝中。这样，将以1025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，而无需进行任何类型的预处理，随后将四种类型的预处理应用于信号：傅立叶分析，移动平均滤波器，小波分析和Savitzky-Golay滤波器。信号处理技术被用来改善神经网络在区分缺陷类别方面的性能。结果表明，可以使用ANN对D类和ND类信号进行分类，从而获得非常有效的结果（94.2％），以及腐蚀（CO）和LP信号（92.5％）。还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％。

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