Causes and classification of EMD mode mixing

Abstract

At present, the lack of insight into the problem of mode mixing in Empirical Mode Decomposition (EMD) hinders the development of solutions to the problem. Starting with the phenomenon that the EMD decomposition cannot be accomplished when the number of signal extrema is abnormal, the causes of mode mixing were investigated and the conclusion was reached that there are only two basic types of mode mixing. In light of this finding, the mechanisms of the three typical mode mixing solutions and their limitations were analyzed. It was found from the analysis process and results that the findings of this study regarding the causes and types of mode mixing were correct.

A Novel Median-Point Mode Decomposition Algorithm for Motor Rolling Bearing Fault Recognition

“…e proposed method MMD keeps the advantages of EMD and EEMD and avoid mixed mode, which makes it capable of capturing the features of the signal in motor rolling bearing fault accurately. [13][14][15][16][17][18] Data Availability e data used to support the findings of this study are available from the corresponding author upon request.…”

“…In order to overcome the above problems, ensemble empirical mode decomposition (EEMD) is studied as a new solution for mixed mode problem, which is through adding finite white noise to the investigated signal. However, the Gaussian white noise may make it difficult to determine an ensemble mean as the different iterations can generate different number of IMFs [15][16][17][18]. Furthermore, the EEMD method is hard to be self-adaptive as it requires an amplitude of noise and ensemble number as parameters.…”

Mode decomposition-based time-varying phase synchronization for fMRI

“…While MEMD is a powerful tool, it can suffer from a mode-mixing phenomena when analyzing real signals. This refers to when an IMF has components from multiple frequencies ( Xu et al, 2019 ), which occurs when oscillations with different time scales coexist in the same IMF or when the oscillations in the same time scale are assigned to different IMFs. Mode mixing can occur when the frequencies of the constituent signals are too close to one another or when the amplitude of the low frequency signal is too large ( Xu et al, 2019 ).…”

“…This refers to when an IMF has components from multiple frequencies ( Xu et al, 2019 ), which occurs when oscillations with different time scales coexist in the same IMF or when the oscillations in the same time scale are assigned to different IMFs. Mode mixing can occur when the frequencies of the constituent signals are too close to one another or when the amplitude of the low frequency signal is too large ( Xu et al, 2019 ). The issue of mode mixing hampers the application of EMD and MEMD ( Gao et al, 2008 ; ur Rehman and Mandic, 2011 ; Rilling and Flandrin, 2007 ; Wu and Huang, 2009 ; Xu et al, 2019 ), and to mitigate this issue noise-assisted MEMD (na-MEMD) was introduced ( Colominas et al, 2012 ; Wu and Huang, 2009 ; Yeh et al, 2010 ; Zhang et al, 2017 ).…”

A Rail Fault Diagnosis Method Based on Quartic C2 Hermite Improved Empirical Mode Decomposition Algorithm

“…Therefore, the research of EMD focuses on the suppression of the mode mixing and optimization of the sifting procedure. For mode mixing, scholars usually blame the existence of the intermittent signal, as the frequency components of the signal are too close and the amplitude of the low-frequency signal is too large [13,14]. The noise-assistant methods are widely used to settle the mode mixing problem.…”