Structural Engineering and Mechanics

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A hybrid algorithm based on EEMD and EMD for multi-mode signal processing

  • Lin, Jeng-Wen (Department of Civil Engineering, Feng Chia University)
  • Received : 2011.01.26
  • Accepted : 2011.06.22
  • Published : 2011.09.25
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Abstract

This paper presents an efficient version of Hilbert-Huang transform for nonlinear non-stationary systems analyses. An ensemble empirical mode decomposition (EEMD) is introduced to alleviate the problem of mode mixing between intrinsic mode functions (IMFs) decomposed by EMD. Yet the problem has not been fully resolved when a signal of a similar scale resides in different IMF components. Instead of using a trial and error method to select the "best" outcome generated by EEMD, a hybrid algorithm based on EEMD and EMD is proposed for multi-mode signal processing. The developed approach comprises the steps from a bandpass filter design for regrouping modes of the IMFs obtained from EEMD, to the mode extraction using EMD, and to the assessment of each mode in the marginal spectrum. A simulated two-mode signal is tested to demonstrate the efficiency and robustness of the approach, showing average relative errors all equal to 1.46% for various noise levels added to the signal. The developed approach is also applied to a real bridge structure, showing more reliable results than the pure EMD. Discussions on the mode determination are offered to explain the connection between modegrouping form on the one hand, and mode-grouping performance on the other.

Keywords

References

  1. Bao, C., Hao, H., Li, Z.X. and Zhu, X. (2009), "Time-varying system identification using a newly improved HHT algorithm", Comput. Struct., 87(23-24), 1611-1623. https://doi.org/10.1016/j.compstruc.2009.08.016
  2. Chen, L., Li, X., Li, X.B. and Huang, Z.Y. (2009), "Signal extraction using ensemble empirical mode decomposition and sparsity in pipeline magnetic flux leakage nondestructive evaluation", Rev. Sci. Instrum., 80(2), 025105. https://doi.org/10.1063/1.3082021
  3. Etter, D.M. (1993), Engineering Problem Solving with MATLAB, Prentice-Hall.
  4. Flandrin, P., Rilling, G. and Goncalves, P. (2004), "Empirical mode decomposition as a filter bank", IEEE Signal Proc. Let., 11, 112-114. https://doi.org/10.1109/LSP.2003.821662
  5. Gledhill, R.J. (2003), "Methods for investigating conformational change in biomolecular simulations", Ph.D. Thesis, the University of Southampton, UK.
  6. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C. and Liu, H.H. (1998), "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis", Proc. R. Soc. London, 454, 903-995. https://doi.org/10.1098/rspa.1998.0193
  7. Huang, N.E., Shen, Z. and Long, S.R. (1999), "A new view of nonlinear water waves: the Hilbert spectrum", Annu. Rev. Fluid Mech., 31, 417-457. https://doi.org/10.1146/annurev.fluid.31.1.417
  8. Huang, N.E., Wu, M.C., Long, S.R., Shen, S.P., Qu, W., Gloersen, P. and Fan, K.L. (2003), "A confidence limit for the empirical mode decomposition and Hilbert spectral analysis", Proc. R. Soc. London, 459, 2317-2345. https://doi.org/10.1098/rspa.2003.1123
  9. Lei, Y. and Zuo, M.J. (2009), "Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs", Meas. Sci. Technol., 20(12), 1-12.
  10. Li, Y.Y., Niu, D.X., Qi, J.X. and Liu, D. (2008), "A novel hybrid power load forecasting method based on ensemble empirical mode decomposition", Power Syst. Technol., 32(8).
  11. Lin, J.W. (2010), "Mode-by-mode evaluation of structural systems using a bandpass-HHT filtering approach", Struct. Eng. Mech., 36(6), 697-714. https://doi.org/10.12989/sem.2010.36.6.697
  12. Peng, Z.K., Tse, P.W. and Chu, F.L. (2005), "A comparison study of improved Hilbert-Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing", Mech. Syst. Signal Pr., 19, 974-988. https://doi.org/10.1016/j.ymssp.2004.01.006
  13. Wu, Z. and Huang N.E. (2004), "A study of the characteristics of white noise using the empirical mode decomposition method", Proc. R. Soc. London, 460A, 1597-1611.
  14. Wu, T.Y. and Chung, Y.L. (2009), "Misalignment diagnosis of rotating machinery through vibration analysis via the hybrid EEMD and EMD approach", Smart Mater. Struct., 18(9), 095004. https://doi.org/10.1088/0964-1726/18/9/095004
  15. Wu, Z. HHT MATLAB Program. Website: http://rcada.ncu.edu.tw/research1_clip_program.htm.
  16. Wu, Z. and Huang, N.E. (2009), "Ensemble empirical mode decomposition: a noise assisted data analysis method", Adv. Adapt. Data An., 1(1), 1-41. https://doi.org/10.1142/S1793536909000047
  17. Xu, Y.L., Chen, S.W. and Zhang, R.C. (2003), "Modal identification of Di Wang Building under typhoon york using the Hilbert-Huang transform method", Struct. Des. Tall Spec. Build., 12, 21-47. https://doi.org/10.1002/tal.211
  18. Yeh, J.R., Lin, T.Y., Shieh, J.S., Chen, Y., Huang, N.E., Wu, Z. and Peng, C.K. (2008), "Investigating complex patterns of blocked intestinal artery blood pressure signals by empirical mode decomposition and linguistic analysis", J. Phys. Conf. Series, 96, 012153. https://doi.org/10.1088/1742-6596/96/1/012153
  19. Zhang, X., Lai, K.K. and Wang, S.Y. (2008), "A new approach for crude oil price analysis based on empirical mode decomposition", Energy Economics, 30(3), 905-918. https://doi.org/10.1016/j.eneco.2007.02.012
  20. Zhou, X., Zhao, H. and Jiang, T. (2009), "Adaptive analysis of optical fringe patterns using ensemble empirical mode decomposition algorithm", Optics Letters, 34(13), 2033-2035. https://doi.org/10.1364/OL.34.002033

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