改进型EEMD和MSB解调方法及其在轴承故障特征提取中的应用  

An improved decomposition method using EEMD and MSB and its application in rolling bearing fault feature extraction

作　　者：甄冬[1] 田少宁 郭俊超 孟召宗 谷丰收 ZHEN Dong;TIAN Shao‑ning;GUO Jun‑chao;MENG Zhao‑zong;GU Feng‑shou(School of Mechanical Engineering,Hebei University of Technology,Tianjin 300130,China;Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control,School of Mechanical Engineering,Tianjin University of Technology,Tianjin 300384,China;National Demonstration Center for Experimental and Electrical Engineering Education,Tianjin University of Technology,Tianjin 300384,China;Centre for Efficiency and Performance Engineering,University of Huddersfield,Huddersfield HD13DH,UK)

机构地区：[1]河北工业大学机械工程学院,天津300130 [2]天津理工大学天津市先进机电系统设计与智能控制重点实验室,天津300384 [3]天津理工大学机电工程国家级实验教学示范中心,天津300384 [4]Centre for Efficiency and Performance Engineering,University of Huddersfield,Huddersfield HD13DH

出　　处：《振动工程学报》2023年第5期1447-1456,共10页Journal of Vibration Engineering

基　　金：国防科技大学装备综合保障技术重点实验室基金资助项目(6142003200102);国家自然科学基金资助项目(51605133);中国博士后科学基金面上项目(2021M702122)。

摘　　要：针对滚动轴承振动信号的强非线性和非平稳特性,提出了一种基于改进集成经验模态分解(IEEMD)和调制信号双谱(MSB)分析的故障特征提取方法。将集成经验模态分解(EEMD)应用于滚动轴承的振动信号处理,将其分解成一系列的本征模态函数(IMFs);通过累计均值(MSAM)准则将IMFs自适应地分为低频IMFs和高频IMFs,其中高频IMFs采用小波阈值降噪进行处理;将降噪后的高频IMFs与低频IMFs进行重构以获取高信噪比的瞬态脉冲信号;利用MSB进一步抑制瞬态脉冲信号中的随机噪声和干扰分量,并提取信号故障特征。与谱峭度(SK)和WEEMD-MSB分析结果进行对比,验证了该方法在轴承微弱故障特征提取方面的优越性。Considering the nonlinear and non-stationary characteristics of rolling bearing vibration signals,a method is put forward based on improved ensemble empirical mode decomposition(IEEMD)and modulation signal bispectrum(MSB).The vibration signals are decomposed into intrinsic mode functions(IMFs)at different frequencies by EEMD.The mean of the standardized ac-cumulated modes(MSAM)is taken as a novel criterion to divide IMFs into low-frequency and high-frequency IMFs.Subsequent-ly,the wavelet threshold denoising algorithm is applied to the high-frequency IMFs,which is then combined with the low-frequen-cy IMFs to generate the reconstructed signal.The MSB is used to extract modulation features by further suppressing residual ran-dom noise and deterministic interference components.The analysis results demonstrate that the method has high accuracy in fault feature extraction by comparing with Spectral kurtosis(SK)and WEEMD-MSB.

关 键 词：故障诊断 滚动轴承 改进经验模态分解 调制信号双谱分析 累计均值 

分 类 号：TH165.3[机械工程—机械制造及自动化] TH133.33