Adaptive Empirical Fourier Decomposition Based Mechanical Fault Diagnosis Method

doi:10.3901/JME.2020.09.125

Abstract

Abstract: A novel non-stationary signal analysis method termed adaptive empirical Fourier decomposition (AEFD) is proposed to overcome the deficiencies of Fourier transform, empirical mode decomposition (EMD) and Fourier decomposition method (FDM) in non-stationary signal analysis. AEFD is based on fast Fourier transform and by grouping and reconstructing the coefficients of fast Fourier transform, it can adaptively decompose a given non-stationary signal into several Fourier intrinsic mode functions (FIMF) with instantaneous frequency of physical significance. The decomposition orthogonality and accuracy of AEFD are also studied. The proposed method is compared with EMD, FDM and variational mode decomposition in detail through simulation signal analysis and the results have verified the superiority of AEFD. Finally, AEFD method is applied to the diagnosis of rotor system with local rubbing and rolling bearing with local fault to verify its effectiveness and improve the accuracy of fault diagnosis. The experimental data analysis results show that compared with EMD, AEFD can effectively identify the fault location and get a higher diagnostic accuracy than the methods mentioned above.

Key words: non-stationary signal, empirical mode decomposition, variational mode decomposition, adaptive empirical Fourier decomposition, fault diagnosis

CLC Number:

TN911
TH165

ZHENG Jinde, PAN Haiyang, CHENG Junsheng, BAO Jiahan, LIU Qingyun, DING Keqin. Adaptive Empirical Fourier Decomposition Based Mechanical Fault Diagnosis Method[J]. Journal of Mechanical Engineering, 2020, 56(9): 125-136.

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