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漏磁无损检测中的缺陷信号定量解释方法
引用本文: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): 407-411,426
作者姓名:宋小春  黄松岭  康宜华  赵伟
作者单位:1. 湖北工业大学,机械学院,武汉,430068;清华大学,电机系,电力系统国家重点实验室,北京,100084
2. 清华大学,电机系,电力系统国家重点实验室,北京,100084
3. 华中科技大学机械学院,武汉,430074
基金项目:国家自然科学基金;中国博士后科学基金;湖北省教育厅青年基金
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。

关 键 词:漏磁检测  定量解释  人工神经网络
文章编号:1000-6656(2007)07-0407-05
收稿时间:2006-11-23
修稿时间:2006-11-23

Quantitative Interpretation Methods for Magnetic Flux Leakage Testing Signals
SONG Xiae-chun,HUANG Seng-ling,KANG Yi-hua,ZHAO Wei. Quantitative Interpretation Methods for Magnetic Flux Leakage Testing Signals[J]. Nondestructive Testing, 2007, 29(7): 407-411,426
Authors:SONG Xiae-chun  HUANG Seng-ling  KANG Yi-hua  ZHAO Wei
Affiliation:1. School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China; 2. State Key Lab of Power Systems, Dept. of Electrical Engineering, Tsinghua University, Beijing 100084, China;3. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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.
Keywords:Magnetic flux leakage testing   Quantitative interpretation   Artificial neural network
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