Paper Title:
Feature Extraction of Machine Vibration Using Lifting Wavelet Denoising and EMD and its Application in Fault Diagnosis
  Abstract

In order to reduce the random noise influence on empirical mode decomposition (EMD), the original data is adaptively denoised by lifting wavelet transform to strain mode aliasing, avoid the pseudo mode functions and improve the quality in EMD. The method is employed to analyze the rotor oil whirl vibration signal. Obtaining intrinsic mode functions (IMFs), the instantaneous frequency and amplitude can be calculated by Hilbert transform. Hilbert marginal spectrum can exactly provide the energy distribution of the signal with the change of instantaneous frequency. Thus, the characteristics information of the rotor oil whirl vibration signal can be extracted effectively. Experimental result demonstrate the validity of the proposed method.

  Info
Periodical
Advanced Materials Research (Volumes 152-153)
Edited by
Zhengyi Jiang, Jingtao Han and Xianghua Liu
Pages
383-386
DOI
10.4028/www.scientific.net/AMR.152-153.383
Citation
F. L. Wang, S. L. Duan, H. T. Gao, "Feature Extraction of Machine Vibration Using Lifting Wavelet Denoising and EMD and its Application in Fault Diagnosis", Advanced Materials Research, Vols. 152-153, pp. 383-386, 2011
Online since
October 2010
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