Fault Diagnosis Method of Rolling Bearing Based on VMD-DBN

被引：0
|
作者：
Ren Z.-H. [1 ]
Yu T.-Z. [1 ]
Ding D. [1 ]
Zhou S.-H. [1 ]
机构：
[1] School of Mechanical Engineering & Automation, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2021年 / 42卷 / 08期
关键词：
DBN(deep belief network); Fault diagnosis; Feature extraction; Rolling bearing; VMD(variational mode decomposition);
D O I：
10.12068/j.issn.1005-3026.2021.08.007
中图分类号：
学科分类号：
摘要：
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. © 2021, Editorial Department of Journal of Northeastern University. All right reserved.
引用
收藏
页码：1105 / 1110
页数：5
相关论文
共 18 条
  • [1] Rai A, Upadhyay S H., A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings[J], Tribology International, 96, pp. 289-306, (2016)
  • [2] Xu Lin, Zheng Xiao-tong, Fu Bo, Et al., Fault diagnosis method of motor bearing based on improved GAN algorithm, Journal of Northeastern University (Natural Science), 40, 12, pp. 1679-1684, (2019)
  • [3] Huang N E, Shen Z, Long S R, Et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J/OL], Proceedings of the Royal Society A, 454, 1971, (1998)
  • [4] Li Z T, Li H., EMD and envelope spectrum based bearing fault detection[J], Advanced Materials Research, 459, pp. 233-237, (2012)
  • [5] Wen C, Zhou C D., Rolling bearing fault feature extraction based on SVD-EEMD, Applied Mechanics and Materials, 411, pp. 1067-1071, (2013)
  • [6] Ge J H, Niu T Y, Xu D, Et al., A rolling bearing fault diagnosis method based on EEMD-WSST signal reconstruction and multi-scale entropy[J], Entropy, 22, 3, pp. 290-317, (2020)
  • [7] Dragomiretskiy K, Zosso D., Variational mode decomposition, IEEE Transactions on Signal Processing, 62, 3, pp. 531-544, (2014)
  • [8] Ding J K, Xiao D M, Li X J., Gear fault diagnosis based on genetic mutation particle swarm optimization VMD and probabilistic neural network algorithm[J], IEEE Access, 8, pp. 18456-18474, (2017)
  • [9] An X, Pan L., Bearing fault diagnosis of a wind turbine based on variational mode decomposition and permutation entropy[J/OL], Journal of Risk and Reliability, 231, 2, (2017)
  • [10] Li H, Fan B, Jia R, Et al., Research on multi-domain fault diagnosis of gearbox of wind turbine based on adaptive variational mode decomposition and extreme learning machine algorithms, Energies, 13, 6, pp. 1375-1395, (2020)
12