基于改进的稀疏度自适应振动数据修复方法

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摘要基于压缩感知的数据重构方法已用于解决信号采集中受损数据的修复问题，该算法首先需要已知数据稀疏度，而振动信号的稀疏度通常难以确定，增加了数据修复的难度。稀疏度自适应匹配追踪算法（SAMP）无需预估信号稀疏度，可用于受损数据修复，但SAMP算法的修复结果受终止条件影响较大，导致修复精度不高且效率较低。为此提出了基于终止准则改进的稀疏度自适应数据修复方法。首先基于振动信号波形特征和先验知识，选择适当的字典矩阵实现信号稀疏化；其次以单位矩阵为基础，根据数据的缺失模型构造观测矩阵；最后为了避免传统SAMP算法终止系数选取不当，导致支撑集引入错误原子的问题，采用改进的SAMP算法重构出完整信号，实现受损数据修复。通过仿真信号及轴承实测信号验证了方法有效性，且改进的SAMP算法在重构精度和运算效率上均有所提高。此外，改进的SAMP算法重构效果优于正交匹配追踪（OMP）与正则化正交匹配追踪（ROMP）。

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关键词 ：压缩感知, 稀疏度自适应, 终止准则, 振动数据修复

Abstract：

A data recovery method with compressed sensing (CS) theory was developed for the acquired signal. Since the sparsity of vibration signal is unknown which impedes its application, the sparsity adaptive matching pursuit (SAMP) algorithm was served for the reconstruction. However, the SAMP algorithm is greatly influenced by termination condition, which will lead to unsatisfied results. In this case, a modified SAMP algorithm based on termination criterion was proposed. Firstly, the dictionary matrix was selected according to the waveform characteristics and other priori knowledge. Then based on the unit matrix, the observation matrix can be constructed under the mission data model. Finally, to overcome the blind choice of the termination coefficient which will result in the support set with error atoms, the modified SAMP algorithm based on termination criterion was applied to recover the complete signal. The efficiency of the proposed method was validated by simulation signals and practical bearing signals, and the modified algorithm has better performance on reconstruction accuracy and computing efficiency. Besides that, the modified algorithm also outperforms orthogonal matching pursuit (OMP) and regularized orthogonal matching pursuit (ROMP).

Key words： compressed sensing (CS) sparsity adaptive termination criterion vibration data recovery

收稿日期: 2018-02-02 出版日期: 2019-08-15

引用本文:

谢馨，王华庆，宋浏阳，李景乐,郝彦嵩 . 基于改进的稀疏度自适应振动数据修复方法[J]. 振动与冲击, 2019, 38(16): 261-266.
XIE Xin WANG Huaqing SONG Liuyang LI Jingle HAO Yansong . A vibration data recovery method based on an modified sparsity adaptive algorithm. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(16): 261-266.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I16/261

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