Frequency Modulated Empirical Mode Decomposition Method

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Abstract:

Frequency modulation procedure is proposed to overcome the mode-mixing problem associated with the EMD method when processing signals with closely spaced frequencies. This procedure also provides the flexibility to start the realization of IMFs either from the high frequency end as does the original EMD or from the low frequency end when the signal contains unwanted high frequency components. The EMD procedure, under the circumstances, may behave as high pass, low pass or band pass/stop filters. The proposed method, assisted by the Hilbert-Huang transform on the governing equations, identifies the instantaneous stiffness and damping coefficients as functions of vibration amplitude of a nonlinear system. The effectiveness of the proposed method is verified by numerical simulation.

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Advanced Materials Research (Volumes 433-440)

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4776-4781

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January 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] N.E. Huang, et al, The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis, Proceedings of Royal Society of London, Series A 454(1998) 903-995.

DOI: 10.1098/rspa.1998.0193

Google Scholar

[2] G. Rilling and P. Flandrin, One or Two Frequencies? The Empirical Mode Decomposition Answers, IEEE Transactions on Signal Processing 56(1) (2008) 85-95.

DOI: 10.1109/tsp.2007.906771

Google Scholar

[3] Z. Wu and N. Huang, Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method, Advances In Adaptive Data Analysis 1(1) (2009) 1-41.

DOI: 10.1142/s1793536909000047

Google Scholar

[4] P. Flandrin, G. Rilling and P. Gonçalvés, Empirical Mode Decomposition as a Filter Bank, IEEE Signal Processing Letters 11(2) (2004) 112-114.

DOI: 10.1109/lsp.2003.821662

Google Scholar

[5] T. Tanaka, and D.P. Mandic, Complex Empirical Mode Decomposition, IEEE Signal Processing Letters 14(2) (2007) 101-104.

DOI: 10.1109/lsp.2006.882107

Google Scholar

[6] G. Rilling, P. Flandrin, P. Gonçalves and J. M. Lilly, Bivariate Empirical Mode Decomposition, IEEE Signal Processing Letters14(12) (2007) 936-939.

DOI: 10.1109/lsp.2007.904710

Google Scholar

[7] G. Rilling and P. Flandrin, Sampling Effects on the Empirical Mode Decomposition, Advances in Adaptive Data Analysis 1(1) (2009) 43–59.

DOI: 10.1142/s1793536909000023

Google Scholar

[8] N. E. Huang, M. Wu, S. Long, S. Shen, W. Qu, P. Gloersen and K. Fan, A confidence limit for the empirical mode decomposition and Hilbert spectral analysis, Proc. R. Soc. Lond. A 459 (2003) 2317–2345.

DOI: 10.1098/rspa.2003.1123

Google Scholar

[9] M. Feldman, Non-Linear System Vibration Analysis Using Hilbert Transform-1, Mechanical system and Signal Processing, 8(2) (1994) 119-127. Fig. 2 The Simulated Signal: Pre-defined and Identified Stiffness Coefficients and Damping Coefficients.

DOI: 10.1006/mssp.1994.1011

Google Scholar