A bearing fault feature extraction method based on cepstrum pre-whitening and a quantitative law of symplectic geometry mode decomposition  

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

作　　者：Chen Yiya Jia Minping Yan Xiaoan 陈奕雅;贾民平;鄢小安(东南大学机械工程学院,南京211189;南京林业大学机械工程学院,南京210037)

机构地区：[1]School of Mechanical Engineering,Southeast University,Nanjing 211189,China [2]School of Mechatronics Engineering,Nanjing Forestry University,Nanjing 210037,China

出　　处：《Journal of Southeast University(English Edition)》2021年第1期33-41,共9页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No.52075095).

摘　　要：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.为了有效提取轴承的故障特征,避免轴承损伤引起的冲击成分受到离散频率分量和背景噪声的干扰,提出了一种基于倒谱编辑信号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.首先,对原始信号进行倒谱预白化来增强轴承故障的冲击特性,去除复杂振动信号中的周期性频率成分,产生只包含背景噪声和损伤引起的非平稳冲击成分的白化信号.其次,发现了辛几何模态分解中哈密顿矩阵的有效特征值数目与信号中的频率个数成2倍的数量规律,并通过仿真和理论推导验证了该数量规律.最后,构造预白化信号的轨迹矩阵,进行辛几何模态分解,根据发现的数量规律,选择相应的特征向量重构信号,进行希尔伯特包络谱分析,并提取故障特征.通过仿真分析和应用实例证明,所提方法可以清晰地提取轴承的故障特征.

关 键 词：cepstrum pre-whitening symplectic geometry mode decomposition EIGENVALUE quantitative law feature extraction 

分 类 号：TH17[机械工程—机械制造及自动化]