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基于压缩感知理论的时变信号压缩技术

郭亮,  高宏力,  黄海凤,  张筱辰

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文章导航 > 西南交通大学学报  > 2015 >  28(3): 511-516.
郭亮, 高宏力, 黄海凤, 张筱辰. 基于压缩感知理论的时变信号压缩技术[J]. 西南交通大学学报, 2015, 28(3): 511-516. doi: 10.3969/j.issn.0258-2724.2015.03.020
引用本文: 郭亮, 高宏力, 黄海凤, 张筱辰. 基于压缩感知理论的时变信号压缩技术[J]. 西南交通大学学报, 2015, 28(3): 511-516. doi: 10.3969/j.issn.0258-2724.2015.03.020
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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
shu

基于压缩感知理论的时变信号压缩技术

doi: 10.3969/j.issn.0258-2724.2015.03.020
基金项目: 

国家自然科学基金资助项目(51275426)

详细信息
    作者简介:

    郭亮(1988 -),男,博士研究生,研究方向为智能化状态监测与故障诊断、智能信息处理,电话:028-87601734, E-mail:guoliang2248@gmail.com

    通讯作者:

    高宏力(1971-),男,教授,博士生导师,研究方向为复杂机电系统可靠性技术、智能化状态监测与故障诊断技术, E-mail:hongli_gao@home.swjtu.edu.cn

Time-Varying Signal Compression Technology Based on Compressed Sensing

    摘要
  • 摘要: 机械设备在线状态监测与故障诊断系统中,为实现采集数据的远程传输和实时处理,需对数据进行压缩和滤波处理.通过分析机械振动信号时变性的特点,以压缩感知理论为基础,构造了满足受限等距性质(restricted isometric property, RIP)的稀疏变换矩阵和压缩感知矩阵;提出了基于压缩感知的时变信号压缩算法,并利用Lasso算法对压缩信号进行稀疏重构,恢复原始信号.采用不同类型的时变仿真信号和实测信号进行实验,对比了提出算法与现有算法的压缩与去噪效果.实验结果表明,新算法有更好的压缩去噪效果,当压缩比为40%时,能量保持率达到了95%以上,能满足工程实际需求.

     

    Abstract: 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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出版历程
  • 收稿日期:  2014-08-21
  • 刊出日期:  2015-06-25

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