Application of the EEMD method to rotor fault diagnosis of rotating machinery

Empirical mode decomposition (EMD) is a self-adaptive analysis method for nonlinear and non-stationary signals. It may decompose a complicated signal into a collection of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. The EMD method has attracted considerable attention and been widely applied to fault diagnosis of rotating machinery recently. However, it cannot reveal the signal characteristic information accurately because of the problem of mode mixing. To alleviate the mode mixing problem occurring in EMD, ensemble empirical mode decomposition (EEMD) is presented. With EEMD, the components with truly physical meaning can be extracted from the signal. Utilizing the advantage of EEMD, this paper proposes a new EEMD-based method for fault diagnosis of rotating machinery. First, a simulation signal is used to test the performance of the method based on EEMD. Then, the proposed method is applied to rub-impact fault diagnosis of a power generator and early rub-impact fault diagnosis of a heavy oil catalytic cracking machine set. Finally, by comparing its application results with those of the EMD method, the superiority of the proposed method based on EEMD is demonstrated in extracting fault characteristic information of rotating machinery.

[1]  Jiang Hongkai,et al.  A sliding window feature extraction method for rotating machinery based on the lifting scheme , 2007 .

[2]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[3]  F. Chu,et al.  Experimental observation of nonlinear vibrations in a rub-impact rotor system , 2005 .

[4]  Liangsheng Qu,et al.  Diagnosis of subharmonic faults of large rotating machinery based on EMD , 2009 .

[5]  Yang Yu,et al.  The application of energy operator demodulation approach based on EMD in machinery fault diagnosis , 2007 .

[6]  Yu Yang,et al.  Local rub-impact fault diagnosis of the rotor systems based on EMD , 2009 .

[7]  S. Loutridis.  Instantaneous energy density as a feature for gear fault detection , 2006 .

[8]  S. J. Loutridis,et al.  Damage detection in gear systems using empirical mode decomposition , 2004 .

[9]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[10]  Hong Fan,et al.  Rotating machine fault diagnosis using empirical mode decomposition , 2008 .

[11]  K. Loparo,et al.  Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling : A method for bearing prognostics , 2007 .

[12]  Yaguo Lei,et al.  New clustering algorithm-based fault diagnosis using compensation distance evaluation technique , 2008 .

[13]  Yaguo Lei,et al.  A new approach to intelligent fault diagnosis of rotating machinery , 2008, Expert Syst. Appl..

[14]  V. Rai,et al.  Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform , 2007 .

[15]  Qiao Hu,et al.  Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs , 2007 .

[16]  Dejie Yu,et al.  Application of frequency family separation method based upon EMD and local Hilbert energy spectrum method to gear fault diagnosis , 2008 .

[17]  Yaguo Lei,et al.  Application of a Novel Hybrid Intelligent Method to Compound Fault Diagnosis of Locomotive Roller Bearings , 2008 .

[18]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[19]  N. Huang,et al.  A study of the characteristics of white noise using the empirical mode decomposition method , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[20]  Yuesheng Xu,et al.  Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum , 2006 .

[21]  Gabriel Rilling,et al.  EMD Equivalent Filter Banks, from Interpretation to Applications , 2005 .