基于PSO-LS-SVM的漏磁信号二维轮廓重构  

2-D Pipeline Defect Reconstruction from Magnetic Flux Leakage Signals Based on LS-SVM

作　　者：纪凤珠[1] 孙世宇[1] 王长龙[1] 王瑾[1] 左宪章[1] 

机构地区：[1]军械工程学院电气工程系,石家庄050003

出　　处：《无损检测》2011年第6期19-22,34,共5页Nondestructive Testing

基　　金：河北省自然科学基金资助项目(E2008001258)

摘　　要：漏磁检测技术被广泛应用于铁磁材料的无损评估中,用漏磁信号描述缺陷的几何特征一直是漏磁检测的难点。提出应用最小二乘支持向量机对缺陷轮廓重构的方法,并利用粒子群算法来优化LS-SVM的参数及核函数参数。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。训练样本由试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BP神经网络、GA-LS-SVM两种方法进行了比较。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。Nondestructive evaluation of ferromagnetic material is most commonly performed by magnetic flux leakage（ MFL） techniques, and the key element is to describe the characters of defects from MFL inspection signals. A novel method for the reconstitution of 2-D profiles is presented based on least squares support vector machines （LS-SVM） technique, and particle swarm optimization（PSO） is adopted to optimize the model parameter of LS SVM. The input data sets of SVM is MFL signals and output data sets is 2-D profiles parameter, the mapping relationship from MFL signals to 2-D profiles of defects is established. The least squares method is introduced into network learning, the training data sets are composed of experiment data sets and simulation data sets, the testing data sets are artificial crack defects. The reconstitution of 2-D profiles of artificial crack defects in the magnetic flux leakage testing was implemented by this algorithm. Comparing with the reconstitution results of BP network and GA-LS-SVM, the results show that LS-SVM possesses quick speed, high accuracy and very good generalization ability , and it is a good way for the quantization of the MFL testing.

关 键 词：漏磁检测 PSO-LS-SVM 二维轮廓 重构 

分 类 号：TG115.28[金属学及工艺—物理冶金]