Resonance enhanced singular value decomposition and its application to the vibration monitoring of turboprop engine

被引：0
|
作者：
Cheng L. [1 ,2 ]
Liang T. [1 ]
Guo L. [3 ]
Cheng M. [1 ,4 ]
Zeng L. [1 ]
机构：
[1] Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an
[2] Advanced Aero Engine Collaborative Innovation Center, Beijing
[3] The Chinese People's Liberation Army 95606 Troops, Guiyang
[4] The Chinese People's Liberation Army 93066 Troops, Shenyang
来源：
| 2018年 / Chinese Vibration Engineering Society卷 / 37期
关键词：
Band-pass filtering; Bandwidth; Feature extraction; Singular value decomposition; Vibration detection;
D O I：
10.13465/j.cnki.jvs.2018.22.031
中图分类号：
学科分类号：
摘要：
Singular value decomposition (SVD) can linearly decompose a signal into a series of components. In this paper, the basic principles and problems of singular value decomposition (SVD) based on Hankel matrix were analyzed, and three basic properties of SVD were revealed, which are linear decomposition, frequency domain disorder of reconstructed components and band-pass filtering. Based on this, a resonance enhanced singular value decomposition (SVD) method was proposed. The numerical simulation results show that the proposed method not only solves the frequency-domain disorder problem of the traditional singular value decomposition (SVD) well, but also can realize the linear bandpass filtering with a given bandwidth near any given frequency. The amplitude, frequency and phase features of the original signal are extracted completely. This is the advantage the existing signal processing methods do not have. The proposed method was successfully applied to the characteristic frequency extraction of a turboprop engine vibration monitoring. The results show that the proposed method has excellent feature extraction effect. © 2018, Editorial Office of Journal of Vibration and Shock. All right reserved.
引用
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页码：206 / 213
页数：7
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