The Hilbert-Huang Transform (HHT) was proposed by Huang et al.  as a method for the analysis of non-linear, non-stationary time series. This procedure requires the decomposition of the signal into intrinsic mode functions using a method called empirical mode decomposition. These functions represent the essential oscillatory modes contained in the original signal. Their characteristics ensure that a meaningful instantaneous frequency is obtained through the application of the Hilbert Transform. The Hilbert Transform is applied to each intrinsic mode function and the amplitude and instantaneous frequency for every time-step is computed. The resulting representation of the energy in terms of time and frequency is defined as the Hilbert Spectrum. In previous work  using the HHT for the analysis of storm waves it has been observed that the number of IMFs needed for the decomposition and the amount of energy associated to different IMFs differ from what has been observed for the analysis of waves under 'normal' sea conditions by other authors. In this work we explore in detail the effect that the sampling rate has in the empirical mode decomposition and in the Hilbert Spectrum for storm waves. The results show that the amount of energy associated to different IMFs varies with the sampling rate and also that the number of IMFs needed for the empirical mode decomposition changes with record length. Copyright © 2008 by ASME.
|Original language||English (US)|
|Title of host publication||Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE|
|State||Published - Dec 1 2008|