Symplectic geometry mode decomposition method and its decomposition ability

被引：0
|
作者：
Cheng Z. [1 ]
Wang R. [1 ]
Pan H. [2 ]
机构：
[1] College of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha
[2] College of Mechanical and Vehicle Engineering, Hunan University, Changsha
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2020年 / 39卷 / 13期
关键词：
Decomposition ability; Symplectic geometry component (SGC); Symplectic geometry mode decomposition (SGMD); Symplectic matrix similarity transformation;
D O I：
10.13465/j.cnki.jvs.2020.13.005
中图分类号：
学科分类号：
摘要：
Aiming at shortcomings of the empirical mode decomposition (EMD), the ensemble empirical mode decomposition (EEMD) and the local characteristic scale decomposition (LCD), a new analysis method called the symplectic geometry mode decomposition (SGMD) was proposed here. Firstly, the symplectic matrix similarity transformation was used to solve eigenvalues of a Hamiltonian matrix and the corresponding eigenvectors were used to reconstruct symplectic geometry components (SGCs). So, a complicated signal was de-noised and meanwhile adaptively decomposed into some SGCs. Then, using a simulation signal model, the decomposition performance and noise robustness of SGMD method were studied. Effects of frequency ratio, amplitude ratio, and initial phase difference of the components on decomposition ability of SGMD were analyzed. Finally, the proposed method was applied in gear fault test data analysis. The results showed that SGMD method can effectively decompose signals to be decomposed and eliminate noise signals. © 2020, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：27 / 35
页数：8
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