• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 28 Issue 3
Jun.  2015
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Article Contents
GUO Liang, GAO Hongli, HUANG Haifeng, ZHANG Xiaochen. Time-Varying Signal Compression Technology Based on Compressed Sensing[J]. Journal of Southwest Jiaotong University, 2015, 28(3): 511-516. doi: 10.3969/j.issn.0258-2724.2015.03.020
Citation: GUO Liang, GAO Hongli, HUANG Haifeng, ZHANG Xiaochen. Time-Varying Signal Compression Technology Based on Compressed Sensing[J]. Journal of Southwest Jiaotong University, 2015, 28(3): 511-516. doi: 10.3969/j.issn.0258-2724.2015.03.020

Time-Varying Signal Compression Technology Based on Compressed Sensing

doi: 10.3969/j.issn.0258-2724.2015.03.020
  • Received Date: 21 Aug 2014
  • Publish Date: 25 Jun 2015
  • Data compression and de-noise play important roles in the real-time storage and remote transmission of vibration signal for the on-line monitoring and fault diagnosis of mechanical equipments. By analyzing the time-varying characteristics of vibration signals, the sparse transfer matrix and compressing sensing matrix which satisfying the restricted isometric property (RIP) were constructed. Then, the signal compression and de-noise method was proposed based on compression sensing. The Lasso algorithm was used to reconstruct the compressed vector to restore raw signal. The proposed method was applied to analyzing the simulation and spindle vibration signal and was compared with the wavelet-based technique. The experimental results show that the proposed method is more effective in data compression and de-noise, and when the signal compression rate is 40%, the energy retention rate exceeds 95% to meet industrial requirements.

     

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