基于二维阻抗特征的管道环焊缝缺陷涡流检测

Eddy current NDt for the cracks of girth welds of pipes based on 2D impedance characteristics

油气长输管道环焊缝处缺陷对管道安全的危害性巨大,管道缺陷造成的事故大部分发生在管道焊接处。目前,对管道进行无损检测(NTD)是预测事故隐患、保证管道安全运行的常用手段,但传统无损检测方法无法有效识别位于环焊缝处等表面形貌复杂位置的缺陷。为了克服传统检测方法的缺点,提出一种基于图像处理和神经网络的嵌入式涡流检测系统。从涡流信号合成的二维阻抗图入手,对其进行霍夫变换和轮廓提取得到特征分量,使用类内散布矩阵筛选分类特性好的特征用以训练基于FPGA加速的神经网络,实现在焊缝基底噪声较大的情况下对缺陷的自动分类与识别。实验结果表明,本系统可以有效识别位于环焊缝处等形貌复杂位置的缺陷信号,正确率可达92%,且系统体积小、功耗低,适合应用于管道内检测环境。

Cracks of girth welds of long-distance oil and gas pipeline bring extremely harm to the pipeline safety, and most of accidents caused by pipeline defects occurred at pipeline welds. So far non destructive testing （NDT） is a common method for predicting potential risk and ensuring the safe operation of pipeline, but traditional NDT methods can＇ t effectively identify the defects lying in girth welds or other complex surface. In order to overcome the disadvantages of traditional methods, an embedded eddy current testing system is presented based on image processing and neural network. Hough transform and contour extraction are used to extract the features from 2D impedance image composed of eddy current signals. Features with good classification characteristics are selected by the within class scatter matrix to train neural network based on FPGA speeding up. Automatic cracks of girth welds classification and identification is realized with a heavy weld noise floor. Experimental results show that this system can effectively identify the defect signals lying in weld of the cylinder or other complex surface. The accuracy of the system is as high as 92% , and has lower power consumption and smaller size, which is suitable for pipeline inner inspection.