Matrix Dimension Considering Frequency Factor in Singular Value Decomposition

被引：1

作者：

Zhao X. ^{[1
]}

Shao Q. ^{[1
]}

Ye B. ^{[1
]}

Chen T. ^{[1
]}

机构：

[1] School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou

来源：

Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2019年 / 55卷 / 16期

关键词：

Frequency period; Least common multiple; Matrix dimension; Signal separation; Singular value decomposition;

D O I：

10.3901/JME.2019.16.007

中图分类号：

学科分类号：

摘要：

The dimension of Hankel matrix has a very important influence on the signal processing effect of singular value decomposition (SVD). The traditional matrix dimension does not consider the frequency components in the signal and this is unreasonable. A least common multiple method is put forward to determine the matrix dimension based on the analysis for the frequency factor, and in this method, the least common multiple of the periods of the all frequency components in the original signal is used as a base number, and the row and column number of Hankel matrix must be the integer multiple of this base number, under this necessary condition, the dimension of Hankel matrix should be maximized, and then the optimal row number and column number are obtained by the optimization computation. The processing examples of simulation signal and rotor vibration signal are provided, which show that, under the matrix dimension determined by the least common multiple method, the calculation amount of SVD is much smaller, but waveform error of the decomposition results is much smaller than the ones of the traditional maximum dimension method. © 2019 Journal of Mechanical Engineering.

引用

页码：7 / 16

页数：9

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