Median ensemble empirical mode decomposition

Xun Lang, Naveed Ur Rehman, Yufeng Zhang*, Lei Xie*, Hongye Su

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Abstract

Ensemble empirical mode decomposition (EEMD) belongs to a class of noise-assisted EMD methods that are aimed at alleviating mode mixing caused by noise and signal intermittency. In this work, we propose a median ensembled version of EEMD (MEEMD) to help reduce the additional mode splitting problem of the original EEMD algorithm. That is achieved by replacing the mean operator with the median operator during the ensemble process. Our use of the median operator is motivated by a rigorous analysis of mode splitting rates for both EEMD and MEEMD. It is shown that EEMD comes with irremovable new mode splitting while the proposed method can greatly reduce this problem on a breakdown point of 50%. This work is verified by extensive numerical examples as well as industrial oscillation case in terms of reducing the mode splitting.

OriginalsprogEngelsk
Artikelnummer107686
TidsskriftSignal Processing
Vol/bind176
Antal sider8
ISSN0165-1684
DOI
StatusUdgivet - nov. 2020
Udgivet eksterntJa

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