Hilbert–Huang transform based signal analysis for the characterization of gas–liquid two-phase flow

Abstract This paper reports the application of the Hilbert–Huang Transform (HHT) to the dynamic characterization of gas–liquid two-phase flow in a horizontal pipeline. A differential pressure fluctuation signal of gas–liquid two-phase flow is adaptively decomposed into Intrinsic Mode Functions (IMFs) through the use of Empirical Mode Decomposition (EMD) methods. Based on the EMD, the associated time–frequency–energy distribution, i.e., the Hilbert spectrum, is obtained for the analysis of the differential pressure fluctuation signal and subsequent identification of its corresponding energy characteristics. The relationship between the energy distribution of the signal and the flow pattern is established. In order to assess the effectiveness of the approach, the results obtained using the HHT are compared with those from Fourier analysis and wavelet based methods. It is found that the extracted energy characteristics give a good indication of the dynamic state of the gas–liquid two-phase flow and thus can be used for flow pattern recognition. The proposed method is a useful tool for the in-depth understanding and subsequent quantitative characterization of gas–liquid two-phase flow.

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

[2]  A. Veltcheva.  Wave and Group Transformation by a Hilbert Spectrum , 2002 .

[3]  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.

[4]  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.

[5]  Maurizio Varanini,et al.  Empirical mode decomposition to approach the problem of detecting sources from a reduced number of mixtures , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[6]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[7]  N. Ellouze,et al.  Empirical mode decomposition of voiced speech signal , 2004, First International Symposium on Control, Communications and Signal Processing, 2004..

[8]  N. Huang,et al.  The Mechanism for Frequency Downshift in Nonlinear Wave Evolution , 1996 .

[9]  Boualem Boashash,et al.  Estimating and interpreting the instantaneous frequency of a signal. II. A/lgorithms and applications , 1992, Proc. IEEE.

[10]  F. Salzenstein,et al.  IF estimation using empirical mode decomposition and nonlinear Teager energy operator , 2004, First International Symposium on Control, Communications and Signal Processing, 2004..

[11]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[12]  J. A. Jendrzejczyk,et al.  Application of chaos theory in identification of two-phase flow patterns and transitions in a small, horizontal, rectangular channel , 1996 .

[13]  M. S. Woolfson,et al.  Application of empirical mode decomposition to heart rate variability analysis , 2001, Medical and Biological Engineering and Computing.

[14]  Boualem Boashash,et al.  Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals , 1992, Proc. IEEE.

[15]  Torsten Schlurmann,et al.  Spectral analysis of nonlinear water waves based on the Hilbert-Huang Transformation , 2002 .

[16]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Boualem Boashash,et al.  A unified approach to the STFT, TFDs, and instantaneous frequency , 1992, IEEE Trans. Signal Process..