期刊论文

Symplectic geometry mode decomposition method and its decomposition ability

中文

振动与冲击

1000-3835

2020

39

13

27-35

10.13465/j.cnki.jvs.2020.13.005

2016YFF0203400:国家重点研发计划 51575168:国家自然科学基金 51875183:国家自然科学基金

本校为第一机构

针对经验模态分解(Empirical Mode Decomposition,EMD)、集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)、局部特征尺度分解(Local Characteristic scale Decomposition,LCD)等方法的不足,提出了一种新的分析方法———辛几何模态分解(Symplectic Geometry Mode Decomposition,SGMD)方法,该方法采用辛矩阵相似变换求解Hamilton矩阵的特征值,并利用其对应的特征向量重构辛几何分量(Symplectic Geometry Component,SGC),从而对复杂信号去噪的同时进行自适应分解,得到若干个SGC。通过仿真信号模型,研究了SGMD方法的分解性能、噪声鲁棒性,分析了分量信号的频率比、幅值比和初相位差...

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 dec...