Journal of Northeastern University(Natural Science) ›› 2021, Vol. 42 ›› Issue (8): 1105-1110.DOI: 10.12068/j.issn.1005-3026.2021.08.007

• Mechanical Engineering • Previous Articles     Next Articles

Fault Diagnosis Method of Rolling Bearing Based on VMD-DBN

REN Zhao-hui, YU Tian-zhuang, DING Dong, ZHOU Shi-hua   

  1. School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China.
  • Revised:2020-10-20 Accepted:2020-10-20 Published:2021-09-02
  • Contact: REN Zhao-hui
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Abstract: In order to identify the vibration signal features of faulty bearing, a feature extraction method of bearing vibration signals based on the variational mode decomposition (VMD) and deep belief network (DBN) is proposed. First, the signal is decomposed based on VMD and the parameters of each modal component are determined by the modal component spectrogram, thus several modal components being obtained. Then an unsupervised feature extraction method based on DBN, which has powerful feature extraction ability, is used to map the modal components obtained to one dimension, and the DBN features of each component are merged to form feature vectors and input into particle swarm optimization support vector machine (PSO-SVM) for fault diagnosis. Experimental verification and comparative analysis show the feasibility and superiority of the VMD-DBN method proposed.

Key words: rolling bearing; VMD(variational mode decomposition); DBN(deep belief network); feature extraction; fault diagnosis

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