Noise-assisted MEMD based relevant IMFs identification and EEG classification

Noise-assisted multivariate empirical mode decomposition(NA-MEMD) is suitable to analyze multichannel electroencephalography(EEG) signals of non-stationarity and non-linearity natures due to the fact that it can provide a highly localized time-frequency representation.For a finite set of multivariate intrinsic mode functions(IMFs) decomposed by NA-MEMD,it still raises the question on how to identify IMFs that contain the information of inertest in an efficient way,and conventional approaches address it by use of prior knowledge.In this work,a novel identification method of relevant IMFs without prior information was proposed based on NA-MEMD and Jensen-Shannon distance(JSD) measure.A criterion of effective factor based on JSD was applied to select significant IMF scales.At each decomposition scale,three kinds of JSDs associated with the effective factor were evaluated:between IMF components from data and themselves,between IMF components from noise and themselves,and between IMF components from data and noise.The efficacy of the proposed method has been demonstrated by both computer simulations and motor imagery EEG data from BCI competition IV datasets.

A New Diagnostic Method Applied to Gearbox Missing Gear Faults——LOD-ICA

With the increasingly stringent requirements for carbon emissions,countries have increased the scale of clean energy use in recent years.As an important new clean energy source,the ratio of wind power in energy utilization has been increasing.The horizontal axis wind turbine is the main form of wind power generation,which is subject to random wind loads during operation and is prone to various failures after a long period of operation,resulting in reduced power generation efficiency or even shutdown.In order to ensure stable external power transmission,it is necessary to perform fault diagnosis for wind turbines.However,the traditional time-frequency analysis method is defective.This paper proposes a new LOD-ICA method to realize the resolution of the vibration signals mode mixing problem incorporated the merits of both methods.The LOD-ICA method and the LOD method based on noise-assisted analysis decompose the same signal to produce different signal components.The feasibility of the LOD-ICA method was verified by comparing the correlation coefficients between each of the signal components generated by the two methods and the original signal.In the field of wind turbine fault diagnosis,the LOD-ICA method is employed to the fault characteristics of gearboxes to extract the fault signs of vibration signals,further demonstrated the superiority of the LOD-ICA method in processing vibration signals of rotating machinery.