Time-Frequency Feature Extraction without Cross-Terms Based on Acoustic Signal in Rotor Malfunctions

Abstract:

An effective approach is presented to eliminate the cross-terms in Wigner distribution by ICA (independent component analysis) and EMD (empirical mode decomposition), through which the cross-terms caused by the uncorrelated mixing signals can be removed successfully. This method is used for time-varying signal analysis and is powerful in signal feature extraction, especially for joint time frequency resolution, which is demonstrated by numerical examples. To further understand the method and its application, a detailed analysis about abrupt unbalance experimental example is shown to explain the cause of malfunction as well as its occurrence and phenomenon. In addition, the proposed approach based upon independent component analysis, empirical mode decomposition method and wigner distribution allows the separation and analysis of the sources with nonlinear and non-stationary properties. In this method, the main conceptual innovations are the associated introduction of ‘source separation’ and ‘intrinsic mode functions’ based on the local properties of the mixed signals, which makes the instantaneous frequency meaningful; the method serves to illustrate the roles played by the nonlinear and non-stationary effects in the energy-time-frequency distribution. At the same time, the method can also be expanded and applied in other fields.

• Periodical: Key Engineering Materials (Volumes 293-294)
• Pages: 467-474
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.293-294.467
• Online since: September 2005
• Authors: F.Q. Wu, Guang Meng
• Keywords: Acoustic Signal, Empirical Mode Decomposition (EMD), Fault Diagnosis, ICA, Rotor

References

[1] M.G. Amin, W.J. Williams, IEEE Trans. Signal Process. 46 (10) (October 1998) 2796-2804.

[2] G.F. Boudreaux-Bartels, T.W. Parks, IEEE Trans. ASSP 34 (6) (June 1986) 442-451.

[3] D. KVonig, J.F. BVohme, Appl. Signal Process. 3 (1996) 54-64.

[4] Y. Lu, J.M. Morris, IEEE Signal Process. Mag. 16 (2) (March 1999) 68-80.

[5] M. Sun, S. Qian, X. Yan, S.B. Baumann, X. -G. Xia, R.E. Dahl, N.D. Ryan, R.J. Sclabassi, Localizing functional activity in the brain through time-frequency analysis and synthesis of the EEG, Proc. IEEE 84 (9) (September 1996) 1302-1311.

DOI: 10.1109/5.535248

[6] J.J. Wang, J. Zhou, IEEE Signal Process. Mag. 16 (2) (March 1999) 94-99.

[7] Proc. IEEE (Special issue on time-frequency analysis) 84(9) (September 1996).

[8] J.B. Wu, J. Chen and P. Zhong, IEE Electronics Letters, 2003, 39(5): 475-477.

[9] Igor Djurovi and LJubi a Stankovi , Signal Processing, Volume 84, Issue 3, March 2004, 631-643.

[10] Cheng Jun-sheng, Research and application of filter method based on EMD. The proceeding of the eighth national conference on vibration theory and application, (2003).

[11] J.F. Cardoso, Blind source separation: statistical principles, Proc. IEEE, 1998, 2009-(2025).

[12] A. Belouchrani, K. Abed Meraim, J.F. Cardoso and E. Moulines: A blind source separation technique using second order statistics, IEEE Trans. Signal Process., 1997, 45, 434-444.

DOI: 10.1109/78.554307

[13] N.E. Huang, et al, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc.R. Soc. Lond. A, 1998, 454: 903-995.

DOI: 10.1098/rspa.1998.0193

[14] BUNSE GERSTNER A., Byers R., and MEHRMANN V.: SIAM J. Matrix Anal. Applicat., 1993, 14, 927-949.

DOI: 10.1137/0614062