Missing vibration data reconstruction using compressed sensing based on over-complete dictionary

被引：4
|
作者：
Yu L. [1 ]
Qu J. [1 ]
Gao F. [1 ]
Tian Y. [2 ]
Shen J. [1 ]
机构：
[1] Department of Control, Naval Aeronautical Engineering Institute Qingdao Branch, Qingdao
[2] Department of Electronics, Naval Aeronautical Engineering Institute Qingdao Branch, Qingdao
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2017年 / 39卷 / 08期
关键词：
Compressed sensing (CS); Dictionary learning; K-singular value decomposition (K-SVD); Regular orthogonal matching pursuit (ROMP); Vibration data recovery;
D O I：
10.3969/j.issn.1001-506X.2017.08.29
中图分类号：
学科分类号：
摘要：
To deal with the problem that the data collector fails to obtain complete vibration data due to short circuit or environment changing or other reasons, a method combining compressed sensing and over-complete dictionary is proposed. Firstly, lots of related vibration data are learned so as to obtain an over-complete dictionary for the data remained to be recovered by K-singular value decomposition. Then a measurement matrix is constructed under the frame of compressed sensing. Finally, data recovery is implemented by regular orthogonal matching pursuit. Experiments of vibration database and practical aero engine vibration demonstrate the proposed method is superior to traditional methods based on discrete cosine transform or discrete Fourier transform and has certain robustness. © 2017, Editorial Office of Systems Engineering and Electronics. All right reserved.
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收藏
页码：1871 / 1877
页数：6
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