Rolling bearing fault diagnosis method based on modified fourier mode decomposition and band entropy

LIU Junfeng; YU Xiang; WAN Haibo

doi:10.19693/j.issn.1673-3185.02359

Volume 17 Issue 2

Apr. 2022

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Article Navigation > Chinese Journal of Ship Research > 2022 > 17(2): 190-197

LIU J F, YU X, WAN H B. Rolling bearing fault diagnosis method based on modified fourier mode decomposition and band entropy[J]. Chinese Journal of Ship Research, 2022, 17(2): 190–197 doi: 10.19693/j.issn.1673-3185.02359

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Rolling bearing fault diagnosis method based on modified fourier mode decomposition and band entropy

doi: 10.19693/j.issn.1673-3185.02359

LIU Junfeng^1
,,
YU Xiang^{2
,
,},
WAN Haibo^2
,

1.
College of Power Engineering, Naval University of Engineering, Wuhan 430033, China
2.
College of Naval Architecture and Ocean Engineering, Naval University of Engineering, Wuhan 430033, China

Received Date: 2021-04-20
Rev Recd Date: 2021-08-03

Available Online: 2022-04-06

Publish Date: 2022-04-20

Abstract

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective In order to resolve the difficulty of extracting the fault features of rolling bearings under conditions of multiple components and strong background noise, this paper proposes a rolling bearing fault feature extraction method based on modified Fourier mode decomposition (MFMD) and frequency band entropy (FBE) analysis. In order to solve the problem of the boundary frequency offset and over-decomposition of the Fourier decomposition method (FDM) under strong background noise, a method for selecting sensitive frequency bands and mode components based on band entropy and the envelope spectrum is proposed. Methods First, the minimum band entropy value is selected as the central frequency of the sensitive band, while the boundary of the sensitive band is determined by FBE analysis. Second, the signal is decomposed by band-limited Fourier mode decomposition in the sensitive frequency band, and several mutually orthogonal Fourier intrinsic mode functions (FIMF) and their marginal Hilbert spectra are obtained. Next, sensitive FIMFs which can reflect fault characteristics are selected according to the regional dependency relationship between the FIMFs and the FBE of the original signal. Finally, the selected FIMFs are analyzed by envelope spectrum analysis to extract the fault features. Results The accurate diagnosis of bearing faults can be realized by applying this method to bearing simulation data and experimental data. Conclusions The results prove the effectiveness and superiority of the proposed method, which can provide technical support for the health evaluation of rolling bearings.
- rolling bearing,
- fault diagnosis,
- feature extraction,
- modified Fourier mode decomposition (MFMD),
- frequency band entropy

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References(18)

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