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

被引：0

作者：

Qiu Z.-C. ^{[1
,2
]}

Zhang W.-M. ^{[2
]}

Gao X.-Y. ^{[3
]}

Zhang R.-L. ^{[1
]}

机构：

[1] School of Electronic Science and Control Engineering, Institute of Disaster Prevention, Sanhe, 065201, Hebei

[2] School of Mechanical Engineering, Beijing Institute of Technology, 100081, Beijing

[3] School of Information and Electronics, Beijing Institute of Technology, Beijing

来源：

Zhang, Wei-Min (Zhangwm@bit.edu.cn) | 2018年 / Beijing Institute of Technology卷 / 38期

关键词：

Fatigue crack; Magnetic flux leakage; PSO-LSSVM; Quantitative identification;

D O I：

10.15918/j.tbit1001-0645.2018.11.001

中图分类号：

学科分类号：

摘要：

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. © 2018, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.

引用

页码：1101 / 1104and1140

共 6 条

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