Rolling bearing fault diagnosis based on VMD reconstruction and DCS demodulation

Dong Zhen, Dongkai Li, Guojin Feng, Hao Zhang, Fengshou Gu

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

As a major component of rotating machinery, rolling bearings are prone to failure because they usually work in harsh environment and are subjected to heavy cyclic loads. Meanwhile, the fault characteristics of bearings are easily submerged by noise and difficult to extract. To solve this problem, a fault diagnosis method based on variational mode decomposition (VMD) and degree of cyclostationarity (DCS) demodulation is proposed. First, the sparsity-based reconstruction factor can distinguish the sensitivity of VMD modes, and it is used to reconstruct all VMD modes to denoise the signal. Secondly, taking the advantage that DCS demodulation analysis can obtain more useful information, it is applied to the reconstructed signal to extract the fault characteristic frequencies. Finally, simulation studies show the effectiveness of combining VMD and DCS in fault diagnosis, and the advantages of the proposed method are verified through experiments with rolling bearing inner race, outer race and compound faults.

Original languageEnglish
Pages (from-to)205-225
Number of pages21
JournalInternational Journal of Hydromechatronics
Volume5
Issue number3
Early online date2 Aug 2022
DOIs
Publication statusPublished - 2 Aug 2022

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