Separation of Single Frequency Component Using Singular Value Decomposition

2018 ◽  
Vol 38 (1) ◽  
pp. 191-217 ◽  
Author(s):  
Xuezhi Zhao ◽  
Bangyan Ye
Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6850
Author(s):  
Tao Meng ◽  
Huanchang Wei ◽  
Feng Gao ◽  
Huichao Shi

In order to accurately evaluate the flow stability of the flow standard facility, the flow fluctuation in the standard facility needs to be accurately measured. However, the flow fluctuation signal is always superimposed with the fluctuation signal of the measuring flowmeter or measurement system (mainly noise), which leads to inaccurate measurement of the flow fluctuation and even an unreliable evaluation result of the flow stability. In addition, when there are multiple fluctuation sources, flow fluctuations with different frequencies are superimposed together, which is extremely unfavorable for evaluating the impact of flow fluctuation with different single frequencies. In this paper, a new measuring method was proposed to obtain the fluctuation signal and the flow fluctuation based on singular value decomposition (SVD). Simulation experiments on the fluctuation signal (single frequency and multiple frequencies) under different levels of noise were conducted, and simulation results showed that the proposed method could accurately obtain the fluctuation signal and the flow fluctuation, even under high noise. Finally, an experimental platform was set-up based on a water flow standard facility and a flow fluctuation generator, and experiments on the output signal of a venturi flowmeter were carried out. The experiment results showed that the proposed method could effectively obtain the fluctuation signal and accurately measure the flow fluctuation.


2019 ◽  
Vol 25 (6) ◽  
pp. 1246-1262 ◽  
Author(s):  
Zhen Li ◽  
Weiguang Li ◽  
Xuezhi Zhao

The selection of effective singular values using the singular value decomposition (SVD) method has always been a hot topic. In this paper, we found that there was a special relationship between effective singular values and feature frequency components. Theoretical derivations illustrated that each frequency component produced two adjacent nonzero singular values with one ranking another closely. Size of singular values was directly proportional to amplitude of feature frequency. The number of singular values was only related to the number of feature frequency components. For these discoveries, a novel feature frequency separation method based on SVD was proposed, through which axis orbits of large rotating machines were readily purified. The results show that the algorithm was very accurate in feature frequency extraction.


2017 ◽  
Author(s):  
Ammar Ismael Kadhim ◽  
Yu-N Cheah ◽  
Inaam Abbas Hieder ◽  
Rawaa Ahmed Ali

2020 ◽  
Vol 13 (6) ◽  
pp. 1-10
Author(s):  
ZHOU Wen-zhou ◽  
◽  
FAN Chen ◽  
HU Xiao-ping ◽  
HE Xiao-feng ◽  
...  

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