Improving empirical mode decomposition for vibration signal analysis

Abstract

In this paper, the empirical mode decomposition as a signal processing method has been studied to overcome one of its shortcomings. In the previous studies, some improvements have been made on the empirical mode decomposition and it has been applied for condition monitoring of mechanical systems. These improvements include elimination of mode mixing and restraining of end effect in empirical mode decomposition method. In this research, to increase the accuracy of empirical mode decomposition, a new local mean has been proposed in the sifting process. Through the proposed local mean, the overshoot and undershoot problems in defining the local mean of common algorithm are alleviated. Meanwhile, it is capable to separate the components with close frequencies. Through the analysis of simulated signals via the new algorithm, it is shown that the accuracy is improved. Finally, empirical mode decomposition-based fault diagnosis approach has been applied to a vibration signal obtained from a faulty gearbox. The results show that the proposed method can resolve the effects of damage in vibration signals better than the common empirical mode decomposition method and helps for the isolation and localization of the fault.

A Vibration Signal Denoising Method of Marine Atomic Gravimeter Based on Improved Variational Mode Decomposition

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Research on aeromagnetic data error analysis and processing of multi-rotor UAV based on variational mode decomposition algorithm

“…Especially in the case of low signal-to-noise ratio, the noise effect is worse. Therefore, researchers have proposed set empirical Mode decomposition (EEMD), improved Complete set Empirical Mode decomposition (ICEEMD) and partial set empirical Mode decomposition (PEEMD) to improve the modal mixing phenomenon to a certain extent, but they are based on empiricism and lack of mathematical basis ( Kopsinis and Mclaughlin, 2009 ; Wu and Huang, 2011 ; Rezaee and Osguei, 2016 ; Jia et al., 2021 ). In 2014, Dragomiretski and Zosso proposed Variational Mode Decomposition (VMD), which is based on three-dimensional variational constraint theory and uses non-recursive properties to simultaneously estimate multiple modes ( Dragomiretskiy and Zosso, 2014 ; Yang et al., 2020 ; Li et al., 2018a , Li et al., 2018b ; Wang et al., 2021 ).…”

Intelligent Fault Diagnosis of Rolling Bearing Using FCM Clustering of EMD-PWVD Vibration Images

“…Guo et al [14] proposed optimization algorithmbased EMD, which effectively extracted fault characteristics of bearing ring. Rezaee et al [15] proposed local mean method to eliminate modal mixing and endpoint effects of EMD. In addition to wavelet analysis and EMD, Wigner-Ville distribution (WVD) [16] is also an important quadratic TFA method, which has high time-frequency resolution, but it has cross term [17], which can not be directly used for TFA.…”