• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2018, Vol. 54 ›› Issue (6): 118-127.doi: 10.3901/JME.2018.06.118

• 仪器科学与技术 • 上一篇    下一篇

基于双稀疏字典模型机械振动信号压缩感知方法

郭俊锋, 石斌, 雷春丽, 魏兴春, 李海燕   

  1. 兰州理工大学机电工程学院 兰州 730050
  • 收稿日期:2017-01-15 修回日期:2017-07-30 出版日期:2018-03-20 发布日期:2018-03-20
  • 通讯作者: 石斌(通信作者),男,1990年出生。主要研究方向为机械振动信号处理。E-mail:18919852073@163.com
  • 作者简介:郭俊锋,男,1978年出生,博士,副教授。主要研究方向为现代测试技术及故障诊断。E-mail:junf_guo@163.com
  • 基金资助:
    国家自然科学基金资助项目(51465034)。

Method of Compressed Sensing for Mechanical Vibration Signals Based on Double Sparse Dictionary Model

GUO Junfeng, SHI Bin, LEI Chunli, WEI Xingchun, LI Haiyan   

  1. School of Mechanical and Electronic Engineering, Lanzhou University of Technology, Lanzhou 730050
  • Received:2017-01-15 Revised:2017-07-30 Online:2018-03-20 Published:2018-03-20

摘要: 针对机械装备在状态监测与故障诊断过程中,依据传统香农-内奎斯特采样定理进行数据采集时,面临的大量机械振动信号存储、传输和处理等困难问题,提出基于双稀疏字典模型机械振动信号压缩感知方法。分析机械振动信号在基于双稀疏字典模型训练得到的过完备字典上的近似稀疏性;然后利用高斯随机矩阵作为测量矩阵对机械振动信号进行压缩测量;最后通过双稀疏字典模型训练得到的过完备字典,结合正交匹配追踪算法完成对原始机械振动信号的重构。仿真测试结果表明,在相同压缩率下,相比经典K-奇异值分解(K-Singular value decomposition,K-SVD)字典训练方法,所提的方法有更高的重构精度,同时重构时间缩短将近50%。该方法既可以得到较高的信号压缩比又有着精确的信号重构性能。

关键词: 双稀疏字典模型, 稀疏表示, 压缩重构, 振动信号

Abstract: Aiming at the mechanical equipment in the condition monitoring and fault diagnosis process, traditional Shannon-Nyquist sampling theorem is used for data collection, which confront difficult problems of storage, transmission and processing for large amount of vibration signal. Hence, a method of compressed sensing for mechanical vibration signal based on double sparse dictionary model is proposed in this research. Firstly, the sparsity of the mechanical vibration signal based on over-complete dictionary by double sparse dictionary model is analyzed; Then, Gaussian random matrix is used as the sensing matrix to measure the mechanical vibration signal; Finally, over-complete dictionary by double sparse dictionary model and orthogonal matching pursuit algorithm are combined to reconstruct the original mechanical vibration signal. The test results of simulated data demonstrate that at the same compression rate, when compared to the method using classical K-Singular value decomposition(K-SVD) dictionary-training algorithm, the proposed method obtains high accuracy of signal reconstruction and reconstruction time decreases by 50%.The proposed method not only obtains high compression rate of vibration signal and accuracy of signal reconstruction.

Key words: compressed reconstruction, double sparse dictionary model, sparse representation, vibration signal

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