Some remarks on the well-posedness of the EMD algorithm

Drakakis, Konstantinos; Tsakalozos, Nikolaos; Rickard, Scott

doi:10.1049/cp.2009.1726

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…This approach requires less information about the input signals than the WD so it is easier to adapt to all types of signals without any previous knowledge and has been successfully used in several applications [8]. However, in some cases, this algorithm can present a mixing of frequency contents in the different IMF, where a signal of similar scale is in different parts of the decomposition [9]. This was the reason to improve the algorithm with the socalled Ensemble Empirical Mode Decomposition (EEMD) which takes advantage of the statistical properties of white noise when added to the original signal in an iterative process to obtain an adequate separation of the frequency sub-bands [10].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Empirical Mode Decomposition for the denoising of partial discharges measured in UHF

Rojas-Moreno

Robles

Martínez-Tarifa

et al. 2016

2016 IEEE International Conference on Dielectrics (ICD)

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Monitoring dielectric insulation systems is important for the maintenance of electric assets.In open-air substations, this task can be done with antennas because they cover wide areas and detect the electromagnetic emissions of one of the most common deterioration processes: partial discharges (PD). This detection can be affected by radio-frequency interferences that contaminate the measurements and may lead to errors in the diagnosis. Thus, it is necessary to apply denoising techniques to recover the pulsed signal of this PD activity. The aim of this paper is to propose an adaptive and automatic algorithm named Ensemble Empirical Mode Decomposition(EEMD) which consists in the decomposition of the signal in several Intrinsic Mode Functions (IMF) that will be used to reject UHF interferences superimposed to PD pulses.

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Section: Introductionmentioning

confidence: 99%