基于广义形态分量分析的降噪技术研究

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振动与冲击 ›› 2013, Vol. 32 ›› Issue (1) : 145-149.

论文

基于广义形态分量分析的降噪技术研究

李辉1 郑海起2 唐力伟2

作者信息 +

Research on Generalized Morphological Component Analysis Based De-noising Method

LI Hui1，ZHENG Hai-qi2，TANG Li-wei2

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文章历史 +

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。

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.

导出引用

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

LI Hui;ZHENG Hai-qi;TANG Li-wei. Research on Generalized Morphological Component Analysis Based De-noising Method[J]. Journal of Vibration and Shock, 2013, 32(1): 145-149