%0 Journal Article
%T 相空间稀疏化的信号压缩感知与重构方法<br>Compressed Sensing and Reconstruction Method Based on Sparsity in Phase Space
%A 温广瑞
%A 栾日维
%A 任延晖
%A 马再超
%J 振动.测试与诊断
%D 2017
%R 10.16450/j.cnki.issn.1004-6801.2017.02.003
%X 针对旋转机械振动信号受强噪声干扰导致传统FFT频域稀疏性差，难以进行正交匹配重构的问题，提出了相空间稀疏化结合正交匹配追踪（orthogonal matching pursuit, 简称OMP）的信号压缩感知(compressed sensing, 简称CS)方法。首先,对信号进行相空间重构（phase space reconstruction, 简称PSR），并采用主分量分析(principal component analysis, 简称PCA)提取主要分量和重构信号，以提高信号的频域稀疏性；然后,采用随机高斯矩阵测量及压缩频域稀疏性得到优化的信号;最后,采用正交匹配追踪算法重构信号。仿真信号和转子典型不对中信号的分析结果表明，该方法可以提高受强噪声干扰的振动信号在频域内的稀疏性，实现转子振动信号的有效压缩和准确重构。<br>Aiming at the poor frequency sparsity of the vibration signal from a rotating machinery, which is interfered with strong noise and represented by the conventional FFT, a compressed sensing based on the sparsity in phase space is proposed to realize the signal reconstruction with orthogonal matching pursuit (OMP). First, the frequency sparsity is improved by reconstructing the original signal in a phase space and then principal components analysis (PCA) is implemented to extract the features in the constructed space; second, a random gauss matrix and OMP are adopted respectively to compress and reconstruct the improved signal. Analysis of the simulation signal and the misalignment signal of a rotor system suggest that the proposed method can enhance the frequency sparsity of the investigated signal. Moreover, the efficient compression and the accurate reconstruction of the investigated signal have also been realized.
%K 压缩感知
%K 相空间重构
%K 主分量分析
%K 正交匹配&lt
%K br&gt
%K compressed sensing
%K phase space reconstruction
%K principal component analysis
%K orthogonal matching pursuit
%U http://zdcs.nuaa.edu.cn/ch/reader/view_abstract.aspx?file_no=201702003&flag=1