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

被引：0
|
作者：
Zhen D. [1 ]
Tian S.-N. [1 ]
Guo J.-C. [2 ,3 ]
Meng Z.-Z. [1 ]
Gu F.-S. [1 ,4 ]
机构：
[1] School of Mechanical Engineering, Hebei University of Technology, Tianjin
[2] Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin
[3] National Demonstration Center for Experimental and Electrical Engineering Education, Tianjin University of Technology, Tianjin
[4] Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield
来源：
Zhendong Gongcheng Xuebao/Journal of Vibration Engineering | 2023年 / 36卷 / 05期
关键词：
fault diagnosis; improved ensemble empirical mode decomposition; mean of the standardized accumulated modes（MSAM）; modulation signal bispectrum; rolling bearing;
D O I：
10.16385/j.cnki.issn.1004-4523.2023.05.029
中图分类号：
学科分类号：
摘要：
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. © 2023 Nanjing University of Aeronautics an Astronautics. All rights reserved.
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页码：1447 / 1456
页数：9
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