基于PSO-LSSVM的疲劳裂纹漏磁定量识别技术  

Quantitative Identification of Magnetic Flux Leakage of Fatigue Crack Based on PSO-LSSVM

作　　者：邱忠超 张卫民[2] 高玄怡[3] 张瑞蕾[1] QIU Zhong-chao;ZHANG Wei-min;GAO Xuan-yi;ZHANG Rui-lei(School of Electronic Science and Control Engineering,Institute of Disaster Prevention,Sanhe, Hebei 065201,China;School of Mechanical Engineering,Beijing Institute of Technology, Beijing 100081,China;School of Information and Electronics,Beijing Institute of Technology, Beijing 100081,China)

机构地区：[1]防灾科技学院电子科学与控制工程学院,河北三河065201 [2]北京理工大学机械与车辆学院,北京100081 [3]北京理工大学信息与电子学院,北京100081

出　　处：《北京理工大学学报》2018年第11期1101-1104,1140,共5页Transactions of Beijing Institute of Technology

基　　金：中央高校基本科研业务费专项(ZY20180227);国家自然科学基金资助项目(51275048)

摘　　要：针对疲劳裂纹难以定量识别的问题,提出一种将主成分分析(PCA)和粒子群优化的最小二乘支持向量机(PSO-LSSVM)相结合的建模方法,通过建立漏磁信号与疲劳裂纹宽度、深度之间的非线性映射关系,对疲劳裂纹宽度、深度进行定量识别.搭建漏磁检测系统,采用疲劳拉伸试验制备一系列疲劳裂纹样本,通过疲劳裂纹漏磁定量识别实验,建立漏磁缺陷样本库,对基于PSO-LSSVM的疲劳裂纹漏磁定量识别方法的可行性进行验证.结果表明,该方法能够有效定量识别尺寸小于1mm;疲劳裂纹的宽度、深度,误差在0.1mm左右.To solve the problem of fatigue cracks quantitative identification,a modeling method combining principal component analysis(PCA)and particle swarm optimization least squares support vector machine(PSO-LSSVM)was proposed to establish a nonlinear mapping relationship between magnetic flux leakage signals and fatigue cracks for quantitative identification of the fatigue crack width and depth.Firstly,a magnetic flux leakage detection system was built,and a series of fatigue crack samples were prepared by fatigue tensile test.Then,the quantitative identification experiments of fatigue crack magnetic flux were carried out to establish a magnetic flux leakage defect sample library.Finally,the feasibility of the quantitative identification method of fatigue crack magnetic flux leakage based on PSO-LSSVM was verified.The results show that the method can effectively identify the width and depth of fatigue cracks with a size less than 1 mm,and the error is about 0.1 mm.

关 键 词：疲劳裂纹 PSO-LSSVM 定量识别 漏磁检测 

分 类 号：TG115.28[金属学及工艺—物理冶金]