漏磁无损检测中的缺陷信号定量解释方法
Quantitative Interpretation Methods for Magnetic Flux Leakage Testing Signals
摘 要
由于在漏磁场正问题求解、信号反演等方面还没有形成系统的理论和方法，因此漏磁检测信号的定量解释一直是无损检测技术领域的研究重点。在综述国内外漏磁信号定量解释方法研究现状的基础上，分析了由漏磁信号定量描述缺陷特征的技术特点以及模式匹配法、统计分析法的局限性，重点探讨了利用人工神经网络方法解释漏磁信号的优点和不足，并指出了可视化、多传感器信息融合等漏磁信号定量解释技术的研究发展方向。
定量解释
人工神经网络
Magnetic flux leakage testing
Quantitative interpretation
Artificial neural network
Abstract
Because it is still difficult to find an efficient theory and method systemically in the solutions to electromagnetic fields and its inverse problems, the signal quantitative interpretation methods have been the key to magnetic flux leakage testing technique all the time. Based on the review of current research and development for MFL signal interpretation methods, the difficulty of defect quantitative characterization via MFL testing signals was analyzed, and the capabilities and limitations of the model matching method and the statistical relation model were presented. The advantages and disadvantages of the artificial neural network were mainly discussed. And such research tendencies of the MFL signals interpretation as visualization, multi-sensors data fusion were pointed out.
中图分类号 TG115.28
所属栏目 综 述
基金项目 国家自然科学基金资助项目（50305017）；中国博士后科学基金资助项目（2005038358）；湖北省教育厅青年人才基金项目资助(2007A098)
收稿日期 2006/11/23
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备注宋小春(1972-)，男，博士后，副教授，研究方向为数字化无损检测技术。
引用该论文: SONG Xiao-chun,HUANG Song-ling,KANG Yi-hua,ZHAO Wei. Quantitative Interpretation Methods for Magnetic Flux Leakage Testing Signals[J]. Nondestructive Testing, 2007, 29(7): 407～411
宋小春,黄松岭,康宜华,赵 伟. 漏磁无损检测中的缺陷信号定量解释方法[J]. 无损检测, 2007, 29(7): 407～411
被引情况:
【1】仲维畅, "磁偶极子理论在无损检测中的用途",无损检测 32, 49-52(2010)
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【4】Hwang K, Mandayam S, Udpa S S. Characterization of gas pipeline inspection signals using wavelet basis function neural networks[J]. NDT&E International,2000,33(5):531-545.
【5】Lunin V, Barat V, Podobedov D. Neural network-based crack parameterization using wavelet preprocessing MFL signal[J]. In Review of Progress in Quantitative Nondestructive Evaluation,2001,20(6):641.
【6】Chen Z, Preda G. Reconstruction of crack shapes from the MFLT signals by using a rapid forward solver and an optimization approach[J]. IEEE Transactions on Magnetics,2002,38(2):1025-1028.
【7】Zhang W, Guo J T, Huang S L. Application of neural network in metal loss evaluation for gas conducting pipelines[J]. Advances in Neural Networks,2006,(3):1254-1260.
【8】Christen R, Bergamini A. Automatic flaw detection in NDE signals using a panel of neural networks[J]. NDT & E International,2006,39(7):547-553.
【9】Ramuhalli P, Udpa L, Udpa S S. Electromagnetic NDE signal inversion by function-approximation neural networks[J]. IEEE Transactions on Magnetics,2002,38(6):3633-3642.
【10】Ramuhalli P, Udpa L, Udpa S S. Neural network-based inversion algorithms in magnetic flux leakage nondestructive evaluation[J]. Journal of Applied Physics,2003,93(10):8274-8276.
【11】Gavarini H, Perazzo R P J, Reich S L,et al. Automatic assessment of the severity of cracks in steel tubes using neural networks[J]. Insight,1998,40(2):92-93.
【12】崔 伟,黄松岭,赵 伟.基于RBF网络的漏磁检测缺陷定量分析方法[J].清华大学学报(自然科学版),2006,46(7):1216-1218.
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【14】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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【27】宋小春，黄松岭，赵 伟，等.水冷壁管壁厚主磁通超声波融合检测方法[J].中国机械工程,2006,17(10):1079-1082.
【28】金建华,康宜华.多传感器信息的决策融合法及其在电磁检测中的应用[J].无损检测,2003,24(9):638.
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