| 小波变换和神经网络在漏磁缺陷信号识别中的应用 |
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| 引用本文： | 胡浪涛,何辅云,查君君. 小波变换和神经网络在漏磁缺陷信号识别中的应用[J]. 无损检测, 2007, 29(4): 197-199 |
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| 作者姓名： | 胡浪涛 何辅云 查君君 |
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| 作者单位： | 合肥工业大学,计算机与信息学院,合肥,230009 |
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| 基金项目： | 国家科技部科研院社会公益研究资金项目 |
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| 摘 要： | 利用小波变换和RBF（Radius Basis Function）神经网络技术对漏磁检测系统中的缺陷信号进行分类。重点设计了试验系统，采集了四种缺陷信号，首先应用小波变换提取信号特征值，然后利用RBF神经网络训练，采用模糊聚类算法寻找基函数的中心，使缺陷的定性分类获得了很高的准确率。试验获得了较好的缺陷分类效果。
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| 关 键 词： | 漏磁检测 小波分析 模糊聚类 神经网络 |
| 文章编号： | 1000-6656（2007）04-0197-03 |
| 收稿时间： | 2006-04-04 |
| 修稿时间： | 2006-04-04 |
Application of Wavelet Transformation and Neural Network to Magnetic Flux Leakage Signal Classification |
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| HU Lang-tao,HE Fu-yun,CHA Jun-jun. Application of Wavelet Transformation and Neural Network to Magnetic Flux Leakage Signal Classification[J]. Nondestructive Testing, 2007, 29(4): 197-199 |
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| Authors: | HU Lang-tao HE Fu-yun CHA Jun-jun |
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| Affiliation: | School of Computer and Information, Hefei University of Technology, Hefei 230009, China |
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| Abstract: | According to the non-stationary characteristics of pulse echo signals of flaw in magnetic flux leakage testing system,a method of flaw classification based on the wavelet transform and radius basis function(RBF) neural network was presented.An experiment system was designed to test the method,at first,the feature of flaw was extracted with wavelet transform,then the signal features were classified with RBF neural network,and the fuzzy clustering algorithm was used to find the center of basis function.Experiments showed that the result of recognition was satisfactory and high accuracy of flaw classification could be obtained. |
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| Keywords: | Magnetic flux leakage Wavelet analysis Fuzzy clustering Neural network |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
