Fault diagnosis of rolling bearing based on parameter optimized VMD-FE-SVM

Konferenz: ISCTT 2021 - 6th International Conference on Information Science, Computer Technology and Transportation
26.11.2021 - 28.11.2021 in Xishuangbanna, China

Tagungsband: ISCTT 2021

Seiten: 7Sprache: EnglischTyp: PDF

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Yang, Yunfan; Xi, Caiping (Jiangsu University of Science and Technology, School of Electronics and Information, Zhenjiang, China)

In terms of the non-stationary and non-linear characteristics of rolling bearing vibration signals and the difficulty of feature extraction, a rolling bearing fault feature extraction method based on optimized parameter variational mode decomposition (VMD), fuzzy entropy (FE) and support vector machine (SVM) is proposed. The experimental results show that both VMD-FE and adaptive decomposition can effectively realize the fault diagnosis of rolling bearings. The recognition performance of the method proposed in this paper is slightly better than the feature extraction based on EMD, EEMD, CEEMD and fuzzy entropy, which provides a new idea for the diagnosis of bearing faults.