漏磁内检测技术可以对管道缺陷进行诊断但是小管径弯头结构复杂该区域的漏磁信号会受到弯头曲率和提离值变化的影响。为了提高小管径弯头漏磁内检测缺陷量化技术的精度基于三轴漏磁内检测数据提取弯头缺陷轴向、径向漏磁场分量的峰值、峰面积和峰能量特征建立小管径弯头缺陷量化线性回归模型。同时引入提离值修正因子对弯头进行漏磁缺陷尺寸量化根据不同弯头缺陷漏磁数据特点建立缺陷特征库实现对缺陷长度、深度和宽度的量化。实验结

The magnetic flux leakage (MFL) internal inspection technique can diagnose pipeline defects. However, the complex structure of small-diameter elbow pipes affects the MFL signals in this region due to the curvature and lift-off changes. To improve the accuracy of defect quantification in small-diameter elbow MFL internal inspection, this study extracts peak values, peak areas, and peak energies of axial and radial MFL components from three-axis MFL internal inspection data. A linear regression model is then established to quantify the defects in small-diameter elbow pipes. Additionally, a lift-off correction factor is introduced to quantify the defect size. A defect feature database is established based on the MFL data characteristics of different elbow defects, allowing for the quantification of defect length, depth, and width. Experimental results show that the introduced lift-off correction factor reduces the error of the established defect quantification model to within 0-5.5mm, meeting the requirements for engineering applications