The intrinsic mode confusion of empirical mode decomposition(EMD)and the ensemble empirical mode decomposition(EEMD)can only suppress mode confusion to a limited extent,as the white noise added by EE-MD cannot be fully neutralized,which compromises the completeness of the original signal.Additionally,both meth-ods fail to avoid interference from endpoint effects.Modal confusion and endpoint effects lead to distortions in the time-frequency analysis results obtained from the Hilbert transforms of EMD and EEMD.A complete ensemble empir-ical mode decomposition with adaptive noise and endpoint processing(EP-CEEMDAN)is proposed to address these issues.Simulation experiments were conducted to compare EMD,EEMD,and EP-CEEMDAN decomposition results on simulated vibration signals.Through multiscale permutation entropy detection and marginal spectral analysis,it was verified that EP-CEEMDAN has better control over endpoint effects and mode confusion,proving that EP-CEEM-DAN is a more effective adaptive algorithm than EMD and EEMD.Finally,EP-CEEMDAN was applied to the pro-cessing of measured non-stationary vibration signals,where adaptive white noise was added at the endpoints of the vi-bration signals during each stage of decomposition.The method successfully generated various intrinsic mode func-tions(IMF)by calculating a unique residual signal.The EP-CEEMDAN algorithm effectively suppresses IMF end-point divergence and modal confusion,while the time-frequency spectrum obtained through the Hilbert transform of-fers high resolution in both time and frequency domains.This result can be used for vibration feature recognition in non-stationary vibration signals.
维普
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