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| 基于局部投影和小波降噪的弱冲击特征信号的提取 | |
| 引用本文： | 吕勇,徐金梧,李友荣,杨德斌.基于局部投影和小波降噪的弱冲击特征信号的提取[J].北京科技大学学报,2004,26(3):319-321. |
| 作者姓名： | 吕勇 徐金梧 李友荣 杨德斌 |
| 作者单位： | 1. 北京科技大学机械工程学院,北京,100083;武汉科技大学机械自动化学院,武汉,430081 2. 北京科技大学机械工程学院,北京,100083 3. 武汉科技大学机械自动化学院,武汉,430081 |
| 基金项目： | 高等学校博士学科点专项科研项目 , 湖北省重点实验室基金 |
| 摘 要： | 综合局部投影算法及小波变换两者的优点,提出了基于局部投影和小波降噪的弱冲击信号的提取方法.实验结果表明,局部投影算法可以将背景信号和特征信号分解到不同的子空间上,小波降噪可以有效地用于包含尖峰或突变信号的降噪,结合局部投影和小波降噪的弱冲击信号的提取方法对于微弱特征信号的提取是非常有效的. |
| 关 键 词： | 局部投影 弱特征信号 小波变换 非线性时间序列 故障诊断 局部 投影算法 小波降噪 冲击信号 弱特征信号 提取 Wavelet Transform Local Based Method Identification Signals Feature 结合 突变信号 尖峰 子空间 信号分解 背景信号 结果 |
| 修稿时间： | 2003年4月28日 |
Weak Feature Signals Identification Method Based on Local Projective and Wavelet Transform |
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| LU Yong,XU Jinwu,LI Yourong,YANG Debin Mechanical Engineering School,University of Science and Technology Beijing,Beijing ,China Mechanical Engineering School,Wuhan University of Science and Technology Wuhan ,China.Weak Feature Signals Identification Method Based on Local Projective and Wavelet Transform[J].Journal of University of Science and Technology Beijing,2004,26(3):319-321. | |
| Authors: | LU Yong XU Jinwu LI Yourong YANG Debin Mechanical Engineering School University of Science and Technology Beijing Beijing China Mechanical Engineering School Wuhan University of Science and Technology Wuhan China |
| Institution: | LU Yong,XU Jinwu,LI Yourong,YANG Debin Mechanical Engineering School,University of Science and Technology Beijing,Beijing 100083,China Mechanical Engineering School,Wuhan University of Science and Technology Wuhan 430081,China |
| Abstract: | A weak feature signals identification method based on local projection and wavelet transform is here introduced. Experiment indicates that the local projective algorithm can separate background signals and weak fea- ture signals into different orthogona1 sub-spaces. Wavelet transform is effective for noise reduction of sharp and break signals. The algorithm which combines the local projective and wavelet transform has an excellent effect on identifying weak feature signals in nonlinear time series. |
| Keywords: | local projective weak feature signal wavelet transform nonlinear time series fault diagnosis |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |