Theoretical Analysis of Empirical Mode Decomposition

被引:57
作者
Ge, Hengqing [1 ]
Chen, Guibin [1 ]
Yu, Haichun [1 ]
Chen, Huabao [1 ]
An, Fengping [1 ]
机构
[1] Huaiyin Normal Univ, Sch Phys & Elect Elect Engn, Huaian 223001, Peoples R China
来源
SYMMETRY-BASEL | 2018年 / 10卷 / 11期
关键词
EMD; theoretical principle; oscillatory signal; convergence; HUANG SPECTRAL-ANALYSIS; SYSTEM-IDENTIFICATION; PERFORMANCE; EMD;
D O I
10.3390/sym10110623
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
This work suggests a theoretical principle about the oscillation signal decomposition, which is based on the requirement of a pure oscillation component, in which the mean zero is extracted from the signal. Using this principle, the validity and robustness of the empirical mode decomposition (EMD) method are first proved mathematically. This work also presents a modified version of EMD by the interpolation solution, which is able to improve the frequency decomposition of the signal. The result shows that it can provide a primary theoretical basis for the development of EMD. The simulation signal verifies the effectiveness of the EMD algorithm. At the same time, compared with the existing denoising algorithm, it has achieved good results in the denoising of rolling bearing fault signals. It contributes to the development and improvement of adaptive signal processing theory in the field of fault diagnosis. It provides practical value research results for the rapid development of adaptive technology in the field of fault diagnosis.
引用
收藏
页数:16
相关论文
共 27 条
[1]  
[Anonymous], ENTROPY SWITZ
[2]   Improved Dynamic Mode Decomposition and Its Application to Fault Diagnosis of Rolling Bearing [J].
Dang, Zhang ;
Lv, Yong ;
Li, Yourong ;
Wei, Guoqian .
SENSORS, 2018, 18 (06)
[3]   Performance and limitations of the Hilbert-Huang transformation (HHT) with an application to irregular water waves [J].
Dätig, M ;
Schlurmann, T .
OCEAN ENGINEERING, 2004, 31 (14-15) :1783-1834
[4]   Empirical mode decomposition:: An analytical approach for sifting process [J].
Deléchelle, E ;
Lemoine, J ;
Niang, O .
IEEE SIGNAL PROCESSING LETTERS, 2005, 12 (11) :764-767
[5]   Hilbert transform in vibration analysis [J].
Feldman, Michael .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2011, 25 (03) :735-802
[6]   Analytical basics of the EMD: Two harmonics decomposition [J].
Feldman, Michael .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2009, 23 (07) :2059-2071
[7]   Rotating machine fault diagnosis using empirical mode decomposition [J].
Gao, Q. ;
Duan, C. ;
Fan, H. ;
Meng, Q. .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2008, 22 (05) :1072-1081
[8]   Some Properties of an Empirical Mode Type Signal Decomposition Algorithm [J].
Hawley, Stephen D. ;
Atlas, Les E. ;
Chizeck, Howard J. .
IEEE SIGNAL PROCESSING LETTERS, 2010, 17 (01) :24-27
[9]   Local Integral Mean-Based Sifting for Empirical Mode Decomposition [J].
Hong, Hong ;
Wang, Xinlong ;
Tao, Zhiyong .
IEEE SIGNAL PROCESSING LETTERS, 2009, 16 (10) :841-844
[10]   A new view of nonlinear water waves: The Hilbert spectrum [J].
Huang, NE ;
Shen, Z ;
Long, SR .
ANNUAL REVIEW OF FLUID MECHANICS, 1999, 31 :417-457