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基于广义形态分量分析的降噪技术研究
引用本文:李 辉 郑海起,唐力伟.基于广义形态分量分析的降噪技术研究[J].振动与冲击,2013,32(1):145-149.
作者姓名:李 辉 郑海起  唐力伟
作者单位:1石家庄铁路职业技术学院机电工程系,石家庄 0500412军械工程学院一系,石家庄 050003
基金项目:国家自然科学基金资助项目(50975185,50775219)
摘    要:针对强噪声环境中有用信号提取的难题,提出了基于广义形态分量分析的降噪方法。通过引入虚拟观测信号,将一维观测信号扩展为多维虚拟观测信号,再通过广义形态分量分析,实现观测信号的盲源分离,从而达到降噪的目的。通过仿真信号和齿轮磨损故障振动实验信号的研究结果表明:广义形态分量分析技术能有效分离强背景噪声中的微弱信号,有效提取故障特征,其降噪性能优于传统的独立分量分析。

关 键 词:广义形态分量分析    稀疏分量分析    故障诊断    降噪    独立分量分析  
收稿时间:2011-11-7
修稿时间:2011-12-9

De-noising method based on generalized morphological component analysis
LI Hui,ZHENG Hai-qi,TANG Li-wei.De-noising method based on generalized morphological component analysis[J].Journal of Vibration and Shock,2013,32(1):145-149.
Authors:LI Hui  ZHENG Hai-qi  TANG Li-wei
Affiliation:1. Department of Electromechanical Engineering, Shijiazhuang Institute of Railway Technology, Shijiazhuang 050041,China; 2. First Department, Ordnance Engineering College, Shijiazhuang 050003,China
Abstract:Morphological component analysis (MCA) is a novel signal or image processing technique based on signal morphological diversity and sparse representation. MCA takes advantage of the sparse representation of the analyzed data in over-complete dictionaries to separate features in the data based on their morphology. According to the problem of extracting the useful signal from strong background noise, a novel de-noising approach based on generalized morphological component analysis (GMCA) is presented. By introducing the virtual observation signal into the original signal, the one dimentional signal vetor converts into multi-dimentional ones. The GMCA is then applied to the virtual observation signals, the blind source separation is finished and the noise is eliminated. The simulative and experimental results show that not only the weak signal is separated, but also the signal noise ratio of separated signal is improved, the fault of the gear wear can be effectively detected and diagnozed. The denoising performance is better than the traditional independent component analysis method.
Keywords:Generalized morphological component analysis                                                      Sparse component analysis                                                      Fault diagnosis                                                      Denoising                                                      Independent component analysis
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