Extreme-point weighted mode decomposition

Abstract empirical mode decomposition (EMD) is an effective method for nonlinear and nonstationary signal analysis. In this paper a new signal decomposition method termed extreme-point weighted mode decomposition (EWMD) is proposed for improving the accuracy of EMD. In EWMD method, an newly intrinsic mode function (IMF) with physically meaning is defined to overcome the drawback of EMD that constructs mean curve by interpolating local extreme-points. Based on that, a new mean curve is constructed by using the weighting values of adjacent extreme-points for sifting process. Also a new IMF criterion closely related to its definition is established. We have deeply studied and compared the proposed method with EMD method by analyzing synthetic and mechanical vibration signals and the results show the superiority of proposed method in IMF accuracy, decomposition capability and orthogonality.

[1]  Haiyang Pan,et al.  Adaptive parameterless empirical wavelet transform based time-frequency analysis method and its application to rotor rubbing fault diagnosis , 2017, Signal Process..

[2]  I. Osorio,et al.  Intrinsic time-scale decomposition: time–frequency–energy analysis and real-time filtering of non-stationary signals , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[3]  I. Daubechies,et al.  Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool , 2011 .

[4]  Valérie Perrier,et al.  The Monogenic Synchrosqueezed Wavelet Transform: A tool for the Decomposition/Demodulation of AM-FM images , 2012, ArXiv.

[5]  Thomas Y. Hou,et al.  Convergence of a data-driven time-frequency analysis method , 2013, ArXiv.

[6]  Yuesheng Xu,et al.  A B-spline approach for empirical mode decompositions , 2006, Adv. Comput. Math..

[7]  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.

[8]  T. Hou,et al.  Data-driven time-frequency analysis , 2012, 1202.5621.

[9]  Yi Liu,et al.  Hilbert-Huang Transform and the Application , 2020, 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS).

[10]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[11]  Da-Chao Lin,et al.  Elimination of end effects in empirical mode decomposition by mirror image coupled with support vector regression , 2012 .

[12]  Jinde Zheng,et al.  A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy , 2013 .

[13]  Jihong Yan,et al.  Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis , 2014, Signal Process..

[14]  Gabriel Rilling,et al.  One or Two Frequencies? The Empirical Mode Decomposition Answers , 2008, IEEE Transactions on Signal Processing.

[15]  Norden E. Huang,et al.  A review on Hilbert‐Huang transform: Method and its applications to geophysical studies , 2008 .

[16]  Joshua R. Smith,et al.  The local mean decomposition and its application to EEG perception data , 2005, Journal of The Royal Society Interface.

[17]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[18]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[19]  Bruce W. Suter,et al.  Instantaneous frequency estimation based on synchrosqueezing wavelet transform , 2017, Signal Process..

[20]  G.G.S Pegram,et al.  Empirical mode decomposition using rational splines: an application to rainfall time series , 2008, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.