Fault feature extraction of rolling bearing based on optimized FEEMD and similarity measure

DOI:	10.11809/bqzbgcxb2025.03.031
Keywords:	rolling bearings; fault feature extraction; ensemble empirical mode decomposition; similarity; NGO
Abstract:	Aiming at the problem of inaccurate signal to noise separation in the Fast Ensemble Empirical Mode Decomposition (FEEMD) method, a rolling bearing fault feature extraction method based on optimized FEEMD and similarity measure is proposed. This method established an objective optimization function based on the minimum envelope entropy, and used the Northern Goshawk Optimization (NGO) algorithm to determine the model parameters of FEEMD. After that, the optimized FEEMD was used to decompose the rolling bearing vibration signal into multiple intrinsic mode function components and residual terms. The morphological fluctuation consistency deviation distance (MFCDD) index was used to screen the effective components for reconstruction. Finally, the reconstructed signal was demodulated by Hilbert envelope, and the fault feature extraction of rolling bearing was completed based on the demodulated envelope spectrum. The experimental results show that compared with the variational mode decomposition method, kurtosis component selection method and improved complete ensemble empirical mode decomposition combined with Hausdorff distance and kurtosis value, the proposed method improves the signal to noise ratio by 1.75 dB, 12.263 9 dB and 2.060 5 dB respectively, and the root mean square error is reduced by 0.007 8, 0.043 0 and 0.065 6 respectively. It can extract the fault characteristic frequency and its frequency doubling more clearly and comprehensively.
Issue:	Vol. 46 No. 3 (2025)
Published:	2025-03-31
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