| 基于核主成分分析及支持向量机的水轮机叶片裂纹源定位 |
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| 引用本文： | 王向红,朱昌明,毛汉领,黄振峰.基于核主成分分析及支持向量机的水轮机叶片裂纹源定位[J].振动与冲击,2010,29(11):226-229. |
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| 作者姓名： | 王向红 朱昌明 毛汉领 黄振峰 |
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| 作者单位： | 1长沙理工大学汽车与机械工程学院；
2上海交通大学机械与动力工程学院；3广西广播电视大学；4广西大学机械学院 |
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| 基金项目： | 国家自然科学基金，湖南省重点学科建设项目，长沙理工大学重点学科建设项目，长沙理工大学人才引进基金 |
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| 摘 要： | 结合核主成分分析(KPCA)以及支持向量机对水轮机转轮叶片裂纹源的声发射信号进行定位。结果表明,利用核主成分分析提取的特征参数进行定位的精度高于原始参数的定位精度,即输入9个特征参数时,支持向量机在叶片区域的识别率为100%,在裂纹源对焊缝距离的支持向量回归分析中的最大误差为20cm。因而结合KPCA和支持向量机对复杂的大尺寸结构进行定位是一种较好的方法,既减少了输入信号的维数,又提高了定位精度。
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| 关 键 词： | 支持向量机 核主成分分析 源定位 声发射 |
| 收稿时间： | 2009-12-24 |
| 修稿时间： | 2010-3-25 |
Source location of cracks in a turbine blade based on kernel principal component analysis and support vector machines |
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| WANG Xiang-hong,ZHU Chang-ming,MAO Han-ling,HUANG Zhen-feng.Source location of cracks in a turbine blade based on kernel principal component analysis and support vector machines[J].Journal of Vibration and Shock,2010,29(11):226-229. |
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| Authors: | WANG Xiang-hong ZHU Chang-ming MAO Han-ling HUANG Zhen-feng |
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| Affiliation: | 1. School of Automobile and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410076, China;2.School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200030, China;3. Guangxi Radio and TV University, Nanning 530004, China;4. School of Mechanical Engineering, Guangxi University, Nanning 530004, China |
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| Abstract: | Abstract This paper studies the application of kernel principal component analysis (KPCA) and support vector machines (SVM) for source location of the acoustic emission signals of cracks. The results show that the accuracy of location using the feature parameters using KPCA technique is improved compared with raw parameters. That is, the recognition rate of crack region is 100 percent and the maximum error of support vector regression for distance from source of cracks to welding seam is 20cm when the number of the input feature parameters is nine. As a result, it is a good method for source location of complex big-size structures to combine KPCA with SVM. It decreases the dimension of input signals and improves the accuracy of location as well. |
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| Keywords: | Support vector machines (SVM) Kernel principal component analysis (KPCA) source location acoustic emission |
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