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Bearing fault diagnosis method based on cyclic envelope empirical Fourier decomposition method

Published:2023-09-20 author:WANG Zhi-li, XIAO Han, YI Can-can, et al. Browse: 345 Check PDF documents

Bearing fault diagnosis method based on cyclic envelope empirical

Fourier decomposition method

WANG Zhi-li1,2,3, XIAO Han1,2,3, YI Can-can1,2,3, HUANG TAO1,2,3

(1.Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and

Technology, Wuhan 430081, China; 2.Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering,

Wuhan University of Science and Technology, Wuhan 430081, China; 3.Precision Manufacturing Institute, Wuhan University

of Science and Technology, Wuhan 430081, China)

Abstract: Aiming at the problems of over-decomposition and long operation time in Fourier decomposition method, an empirical Fourier decomposition method based on cyclic envelope (CEEFD)was proposed,and the algorithm was applied to the fault diagnosis of rolling bearings.Firstly, the signal was subjected to fast Fourier transform(FFT) to obtain the signal spectrum, and the Fourier spectrum was circularly enveloped to obtain the envelope curve, so as to reduce the number of useless extreme points and suppress the interference of noise on components. Then, the spectral envelope curve was divided into frequency bands by using the improved local max min segmentation technology. Finally, a zero-phase filter was constructed, and each frequency band was reconstructed by using the inverse fast Fourier transform (IFFT) to obtain a number of singlecomponent signals with instantaneous frequencies and physical significance.The simulation signal and the measured signal of rolling bearing were analyzed and compared with empirical mode decomposition (EMD), empirical wavelet transform (EWT), Fourier decomposition method (FDM), variational mode decomposition(VMD)and empirical Fourier decomposition (EFD).Experimental comparison and verification were carried out. The research results show that the single component obtained by CEEFD method contains more accurate fault feature information, which can be used for bearing fault diagnosis. Comparing with the above methods, it has higher accuracy and stronger antinoise interference ability, which verifies the effectiveness of CEEFD method.

Key words: rolling bearing; non-stationary signal; Fourier decomposition method(FDM); empirical Fourier decomposition based on cyclic envelope(CEEFD); fast Fourier transform(FFT); improved local max min segmentation technology

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