Compressed Sensing and Reconstruction Method Based on Sparsity in Phase Space

被引：0

作者：

Wen G. ^{[1
]}

Luan R. ^{[1
]}

Ren Y. ^{[1
]}

Ma Z. ^{[1
]}

机构：

[1] Mechanical Engineering College of Xi'an Jiaotong University, Xi'an

来源：

Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | 2017年 / 37卷 / 02期

关键词：

Compressed sensing; Orthogonal matching pursuit; Phase space reconstruction; Principal component analysis;

D O I：

10.16450/j.cnki.issn.1004-6801.2017.02.003

中图分类号：

学科分类号：

摘要：

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. © 2017, Editorial Department of JVMD. All right reserved.

引用

页码：228 / 234

页数：6

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