基于LS-SVM的管道二维漏磁缺陷重构

针对铁磁材料的无损评估中，漏磁信号描述缺陷的几何特征难点，提出了应用支持向量机对二维缺陷重构的新方法，支持向量机输入是漏磁信号，输出是缺陷轮廓数据，建立了由缺陷的漏磁信号到缺陷二维轮廓的映射关系。网络学习采用最小二乘算法，训练样本由实验数据与仿真数据组成，测试样本为人工裂纹缺陷。该方法实现了人工裂纹缺陷的二维轮廓的重构，并与径向基神经网络重构结果进行了比较。试验结果表明，该方法具有速度快、精度高和很好的泛化能力，为漏磁检测定量化提供了一种可行的方法。

2D DEFECT RECONSTRUCTION OF PIPELINE FROM MAGNETIC FLUX LEAKAGE SIGNALS BASED ON LS- SVM

Nondestructive evaluation of ferromagnetic material is most commonly performed by magnetic flux leakage (MFL) techniques, and it is key element to describe the characters of defects from MFL detecting signals. A new method for the reconstitution of 2D profiles is presented based on least squares support vector machines (LS-SVM) technique, the input data set of SVM is MFL signals and output data set is 2D profiles parameter, the mapping relationship from MFL signals to 2D profiles of defects is established. The least squares method is introduced into network learning, the training data set is composed of experiment data set and emulational data set, the testing data set is artificial crack defects. The reconstitution of 2D profiles of artificial crack defects in the magnetic flux leakage detecting is implemented by this algorithm. Compared with the reconstitution results of RBF network, the results show that LS-SVM possesses quickens speed, enhances high accuracy and is of very good generalization ability , and it is a good way for the quantization of the MFL detecting.