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Fault feature extraction of rolling element bearing based on EVMD

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Abstract

Aiming at the problem that the bearing fault signal is weak and usually interfered by the strong background noise, which makes the fault feature extraction very difficult, an enhanced variational mode decomposition (EVMD) technique is proposed. First, the autoregressive (AR) model was utilized to eliminate the stationary components in the signal in advance to reduce the noise interference and the maximum kurtosis of the residual signal was set as the target. Second, the maximum frequency-domain correlated kurtosis was adopted as the fitness value, and the decomposition modes K and quadratic penalty factor α in the VMD approach were adaptively selected by the whale optimization algorithm. Third, the reconstruction signal was acquired, then the enhanced envelope spectrum was employed to weaken the interference of irrelevant frequency components and the fault features of rolling element bearing could be extracted accurately. The results of simulation and experimental analysis show that the proposed algorithm can significantly reduce the noise interference and avoid the blindness selection of VMD parameters. The comparison with fix-parameter VMD and fast kurtogram approaches shows that the proposed technique can improve the effectiveness of defect signature extraction, which has a certain value for engineering application.

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Correspondence to Danchen Zhu.

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Technical Editor: Samuel da Silva.

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Zhu, D., Liu, G., He, W. et al. Fault feature extraction of rolling element bearing based on EVMD. J Braz. Soc. Mech. Sci. Eng. 43, 567 (2021). https://doi.org/10.1007/s40430-021-03295-9

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  • DOI: https://doi.org/10.1007/s40430-021-03295-9

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