Enhanced adaptive empirical Fourier decomposition based rolling bearing fault diagnosis method

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (15) : 287-299.

CAO Shijun1,2, ZHENG Jinde1,2, PAN Haiyang1,2, TONG Jinyu1,2, LIU Qingyun1,2

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Abstract

Adaptive empirical Fourier decomposition (AEFD) is a recently proposed method for non-stationary signal decomposition. An enhanced adaptive empirical Fourier decomposition (EAEFD) method is proposed based on spectrum envelope detection to solve the problem of dividing boundary set setting of AEFD, which is based on the fast Fourier transform, and the optimal number of decomposition modes is selected by minimizing envelope entropy. The maximum envelopment technology is used to segment the Fourier spectrum, and a reasonable segmentation boundary is obtained. Finally, the inverse fast Fourier transform is used to reconstruct the signals in each interval. EAEFD can adaptively decompose a complex signal into the sum of several single component signals whose instantaneous frequencies have physical significance. The EAEFD method is compared with the EWT, EMD, LCD and AEFD methods by analyzing the simulation and rolling bearing vibration signals. The results show that EAEFD can diagnose fault features effectively and has higher accuracy than the compared methods.
Key words: Adaptive Empirical Fourier Decomposition; Envelope entropy; empirical mode decomposition; rolling bearing; fault diagnosis

Key words

Adaptive Empirical Fourier Decomposition / Envelope entropy / empirical mode decomposition / rolling bearing / fault diagnosis

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CAO Shijun1,2, ZHENG Jinde1,2, PAN Haiyang1,2, TONG Jinyu1,2, LIU Qingyun1,2. Enhanced adaptive empirical Fourier decomposition based rolling bearing fault diagnosis method[J]. Journal of Vibration and Shock, 2022, 41(15): 287-299

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