Paper Title:
Improved EEMD Applied to Rotating Machinery Fault Diagnosis
  Abstract

Ensemble Empirical Mode Decomposition (EEMD) is a new noise-assisted data analysis (NADA) method. The effect of EEMD depends on two key parameters which are the amplitude of white noise and the ensemble times. However, the shortcoming of EEMD is that it lacks adaptability and reliability because these two key important parameters are obtained by experience and human intervention. An Improved Ensemble Empirical Mode Decomposition method is proposed in this paper, by adding white noise and ascertaining ensemble number adaptively. The criterion of adding white noise in Improved EEMD is established, by which a composite simulation signal could be adaptively and accurately decomposed into IMFs without mode mixing. The proposed method is applied to a gear fault detection of hot strip finishing mills. The result shows that Improved EEMD method successfully extracts the gear fault feature with high precise diagnosis results.

  Info
Periodical
Chapter
Chapter 1: Mechanic Manufacturing System and Automation
Edited by
Zhixiang Hou
Pages
154-159
DOI
10.4028/www.scientific.net/AMM.128-129.154
Citation
L. Chen, G. S. Tang, Y. Y. Zi, F. Fan, "Improved EEMD Applied to Rotating Machinery Fault Diagnosis", Applied Mechanics and Materials, Vols. 128-129, pp. 154-159, 2012
Online since
October 2011
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