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储罐底板漏磁检测腐蚀缺陷轮廓反演方法

杨志军, 戴光, 陈志华

杨志军, 戴光, 陈志华. 储罐底板漏磁检测腐蚀缺陷轮廓反演方法[J]. 无损检测, 2013, 35(10): 45-47.
引用本文: 杨志军, 戴光, 陈志华. 储罐底板漏磁检测腐蚀缺陷轮廓反演方法[J]. 无损检测, 2013, 35(10): 45-47.
YANG Zhi-Jun, DAI Guang, CHEN Zhi-Hua. Defect Profile Inversion by Leakage Magnetic Field for Tank Bottom Corrosion Defect[J]. Nondestructive Testing, 2013, 35(10): 45-47.
Citation: YANG Zhi-Jun, DAI Guang, CHEN Zhi-Hua. Defect Profile Inversion by Leakage Magnetic Field for Tank Bottom Corrosion Defect[J]. Nondestructive Testing, 2013, 35(10): 45-47.

储罐底板漏磁检测腐蚀缺陷轮廓反演方法

详细信息
    作者简介:

    杨志军(1976-),男,副教授,博士,主要从事现代无损检测技术研究和无损检测仪器开发工作。

  • 中图分类号: TG115.28

Defect Profile Inversion by Leakage Magnetic Field for Tank Bottom Corrosion Defect

  • 摘要: 漏磁检测方法对检测结果作出评价的依据是漏磁信号。目前对漏磁信号的研究分为正、逆两个方向。正问题是指由缺陷到信号的研究,它往往是针对一定尺寸的缺陷来研究其产生的漏磁信号。逆问题则是指由缺陷的信号反推缺陷的形状,也就是所谓的反演。笔者以铁磁性平板的漏磁信号为基础,分别对缺陷识别、定位、量化等方面进行了深入研究,提出一种基于多通道的铁磁性平板腐蚀缺陷量化反演方法,实现了缺陷检测的可视化。
    Abstract: In the field of magnetic flux leakage testing, magnetic leakage signal is the basis of test results evaluation. The study of magnetic leakage signal can be divided into forward and inverse directions. The forward problem refers to the study which is from the defect of a certain size to the magnetic leakage signals that defect generate. While the inverse problem refers to making a reverse deducing from the magnetic leakage signals to the shapes of defects, it is so-called inversion. Based on magnetic flux leakage signals of the ferromagnetic floor, the defect recognition, defect location, disfigurement quantitative, and so on were deeply studied. A quantization inversion method based on multiple channels was raised, and the defect test visualization was realized.
  • [1] 戴光,徒云,杨志军.基于三维有限元动态模拟圆柱形表面腐蚀缺陷漏磁场[J].无损检测,2007,29(1):2-5.
    [2] 王长龙,纪凤珠,王建斌,等.油气管道漏磁检测缺陷的三维成像技术[J].石油学报,2007,28(5):146-148.
    [3] Ramuhalli Pradeep, Udpa Lalita, Udpa Satish S. Electromagnetic NDE signal inversion by function-approximation neural networks [J]. IEEE Transactions on Magnetics, 2002, 36(6):3633-3642.
    [4] Joshi Ameet. Wavelet transform and neural network based 3D defect characterization using magnetic flux leakage [J]. International Journal of Applied Electromagnetics and Mechanics, 2007, 28(1-2):149-153.
    [5] Ji Feng-Zhu, Wang Chang-Long, Wang Jin, et al. 3-D defect profile reconstruction from magnetic flux leakage signals based on sparsity LS-SVM[J]. Acta Armamentarii, 2008, 29(5):592-595.
    [6] Hwang K. 3-D defect profile reconstruction from magnetic flux leakage signatures using wavelet basis function neural networks [D]. Ames IA: Iowa State University, 2000.
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出版历程
  • 收稿日期:  2013-06-16
  • 刊出日期:  2013-10-09

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