东南大学学报（英文版）2021，Vol.37Issue(1)：33-41,9.DOI:10.3969/j.issn.1003-7985.2021.01.005
基于倒谱预白化和辛几何模态分解数量规律的轴承故障特征提取方法
A bearing fault feature extraction method based on cepstrum pre-whitening and a quantitative law of symplectic geometry mode decomposition
摘要
Abstract
In order to extract the fault feature of the bearing effectively and prevent the impact components caused by bearing damage being interfered with by discrete frequency components and background noise,a method of fault feature extraction based on cepstrum pre-whitening(CPW)and a quantitative law of symplectic geometry mode decomposition(SGMD)is proposed.First,CPW is performed on the original signal to enhance the impact feature of bearing fault and remove the periodic frequency components from complex vibration signals.The pre-whitening signal contains only background noise and non-stationary shock caused by damage.Secondly,a quantitative law that the number of effective eigenvalues of the Hamilton matrix is twice the number of frequency components in the signal during SGMD is found,and the quantitative law is verified by simulation and theoretical derivation.Finally,the trajectory matrix of the pre-whitening signal is constructed and SGMD is performed.According to the quantitative law,the corresponding feature vector is selected to reconstruct the signal.The Hilbert envelope spectrum analysis is performed to extract fault features.Simulation analysis and application examples prove that the proposed method can clearly extract the fault feature of bearings.关键词
倒谱预白化/辛几何模态分解/特征值/数量规律/特征提取Key words
cepstrum pre-whitening/symplectic geometry mode decomposition/eigenvalue/quantitative law/feature extraction分类
机械制造引用本文复制引用
陈奕雅,贾民平,鄢小安..基于倒谱预白化和辛几何模态分解数量规律的轴承故障特征提取方法[J].东南大学学报（英文版）,2021,37(1):33-41,9.基金项目
The National Natural Science Foundation of China(No.52075095). （No.52075095）
