Empirical mode decomposition based on Fourier transform and band-pass filter

Abstract A novel empirical mode decomposition strategy based on Fourier transform and band-pass filter techniques, contributing to efficient instantaneous vibration analyses, is developed in this study. Two key improvements are proposed. The first is associated with the adoption of a band-pass filter technique for intrinsic mode function sifting. The primary characteristic of decomposed components is that their bandwidths do not overlap in the frequency domain. The second improvement concerns an attempt to design narrowband constraints as the essential requirements for intrinsic mode function to make it physically meaningful. Because all decomposed components are generated with respect to their intrinsic narrow bandwidth and strict sifting from high to low frequencies successively, they are orthogonal to each other and are thus suitable for an instantaneous frequency analysis. The direct Hilbert spectrum is employed to illustrate the instantaneous time-frequency-energy distribution. Commendable agreement between the illustrations of the proposed direct Hilbert spectrum and the traditional Fourier spectrum was observed. This method provides robust identifications of vibration modes embedded in vibration processes, deemed to be an efficient means to obtain valuable instantaneous information.

[1]  Qiang Miao,et al.  A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery , 2018, Mechanical Systems and Signal Processing.

[2]  Haiping Wu,et al.  Smart wind speed forecasting using EWT decomposition, GWO evolutionary optimization, RELM learning and IEWT reconstruction , 2018 .

[3]  Joerg F. Hipp,et al.  Time-Frequency Analysis , 2014, Encyclopedia of Computational Neuroscience.

[4]  Boualem Boashash,et al.  A time-frequency based approach for generalized phase synchrony assessment in nonstationary multivariate signals , 2013, Digit. Signal Process..

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

[6]  M. Longuet-Higgins The statistical analysis of a random, moving surface , 1957, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[7]  Huajun Li,et al.  Weak-mode identification and time-series reconstruction from high-level noisy measured data of offshore structures , 2016 .

[8]  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.

[9]  M. Feldman Theoretical analysis and comparison of the Hilbert transform decomposition methods , 2008 .

[10]  Qiang Li,et al.  Empirical decomposition method for modeless component and its application to VIV analysis , 2015 .

[11]  Jing Yuan,et al.  Integrated ensemble noise-reconstructed empirical mode decomposition for mechanical fault detection , 2018 .

[12]  Juan Huan,et al.  Prediction of dissolved oxygen in aquaculture based on EEMD and LSSVM optimized by the Bayesian evidence framework , 2018, Comput. Electron. Agric..

[13]  Julius S. Bendat,et al.  Random Data: Analysis and Measurement Procedures, Fourth Edition , 2010 .

[14]  Danilo P. Mandic,et al.  Filter Bank Property of Multivariate Empirical Mode Decomposition , 2011, IEEE Transactions on Signal Processing.

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

[16]  Wu-Joan Kim,et al.  Effect of bidirectional internal flow on fluid–structure interaction dynamics of conveying marine riser model subject to shear current , 2012 .

[17]  Sylvain Meignen,et al.  A New Formulation for Empirical Mode Decomposition Based on Constrained Optimization , 2007, IEEE Signal Processing Letters.

[18]  M. Schwartz,et al.  Communication Systems and Techniques , 1996, IEEE Communications Magazine.

[19]  Hongde Qin,et al.  Frequency response estimation of floating structures by representation of retardation functions with complex exponentials , 2017 .

[20]  Khaled H. Hamed,et al.  Time-frequency analysis , 2003 .

[21]  J. Michael R. Graham,et al.  Multi-modal Vortex-Induced Vibrations of a vertical riser pipe subject to a uniform current profile , 2004 .

[22]  Cai Yi,et al.  Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings , 2018 .

[23]  Sung-Jin Choi,et al.  Estimation of slamming coefficients on local members of offshore wind turbine foundation (jacket type) under plunging breaker , 2017 .

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

[25]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1971 .

[26]  Guangyou Fang,et al.  Application of the EEMD method for distinction and suppression of motion-induced noise in grounded electrical source airborne TEM system , 2017 .

[27]  Yang Qi,et al.  Discrepancy study of modal parameters of a scale jacket-type supporting structure of 3.0-MW offshore wind turbine in water and in air , 2016 .

[28]  S. Rhee,et al.  Effect of traveling wave on the vortex-induced vibration of a long flexible pipe , 2019, Applied Ocean Research.

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

[30]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[31]  Kota Solomon Raju,et al.  Hurst based vibro-acoustic feature extraction of bearing using EMD and VMD , 2018 .