A Fault Diagnosis Approach Based on Deep Belief Network and Its Application in Bearing Fault

  • Qiulin DanEmail author
  • Xuyu Liu
  • Yi Chai
  • Ke Zhang
  • Hao Li
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 528)


With the development of Industry 4.0, not only the equipment but also the operational conditions in industrial manufacturing are becoming more and more complex. It is necessary to diagnose failures, whose probability is now increasing violently. As a typical deep learning model, the Deep Belief Network (DBN) can be employed to extract features from the original data directly. Compared with traditional fault diagnosis methods, the DBN can get rid of the dependence on signal processing technology and diagnosis experience. In this paper, the fault diagnosis approach based on DBN is studied to identify the bearing failure. First of all, the basic principles of DBN and the steps of fault diagnosis are described. Then some key parameters of DBN which affect the fault identification performance are analyzed and determined according to the simulation experiments. The practicability of this method is verified by comparing with Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) at last.


Signal processing Fault diagnosis Deep Belief Network Feature extraction 


  1. 1.
    J. Xiong, Q. Zhang, Z. Li, J. Xiong, Q. Zhang, G. Sun et al., An information fusion fault diagnosis method based on dimensionless indicators with static discounting factor and KNN. IEEE Sens. J. 16(7), 2060–2069 (2016)CrossRefGoogle Scholar
  2. 2.
    Q. Zhang, L.T. Yang, Z. Chen, Deep computation model for unsupervised feature learning on big data. IEEE Trans. Serv. Comput. 9(1), 161–171 (2016)CrossRefGoogle Scholar
  3. 3.
    D. Yu, L. Deng, Deep learning and its applications to signal and information processing [exploratory DSP]. IEEE Signal Process. Mag. 28(1), 145–154 (2010)CrossRefGoogle Scholar
  4. 4.
    G. Hinton, L. Deng, D. Yu et al., Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process. Mag. 29(6), 82–97 (2012)CrossRefGoogle Scholar
  5. 5.
    P. Tamilselvan, P.F. Wang, Failure diagnosis using deep belief learning based health state classification. Reliab. Eng. Syst. Safety 115(7), 124–135 (2013)CrossRefGoogle Scholar
  6. 6.
    N. Liao, J. Tao, D. Yang, Fault diagnosis of double row conical rolling bearing based on deep learning and empirical mode decomposition. J. Hunan Univ. Sci. Technol. (Natural Science) 32(2), 70–77 (2017)Google Scholar
  7. 7.
    A. Tranvt, A. Ball, An approach to fault diagnosis of reciprocating compress or valves using Teager-Kaiser energy operator and deep belief networks. Expert Syst. Appl. 41(9), 4113–4122 (2014)CrossRefGoogle Scholar
  8. 8.
    W. Li, W. Shan, X.Q. Zeng, Bearing fault classification and recognition based on DBN. J. Vibr. Eng. 29(2), 340–347 (2016)Google Scholar
  9. 9.
    G. Zhao, Q. Ge, X. Liu et al., Research on fault feature extraction and diagnosis method based on DBN. Chinese J. Sci. Instr. 37(9), 1946–1953 (2016)Google Scholar
  10. 10.
    J. Liu, Y. Liu, X. Luo, Research progress of Boltzmann machine. J. Comput. Res. Develop. 51(1), 000001–16 (2014)CrossRefGoogle Scholar
  11. 11.
    H. Ren, Q. Jianfeng, Y. Chai et al., Research status and challenges of deep learning in the field of fault diagnosis. Control Dec. 32(8), 1345–1358 (2017)Google Scholar
  12. 12.
    H. Ren, Y. Chai, J. Qu et al., A novel adaptive fault detection methodology for complex system using deep belief networks and multiple models: a case study on cryogenic propellant loading system. Neurocomputing (2017)Google Scholar
  13. 13.
    H. Shao, H. Jiang, X. Zhang et al., Rolling bearing fault diagnosis using an optimization deep belief network. Measure. Sci. Technol. 26(11) (2015)Google Scholar
  14. 14.
    W. Shan, X. Zeng, Signal reconstruction and bearing fault recognition based on deep belief network. Electr. Design Eng. 24(4), 67–71 (2016)Google Scholar
  15. 15.
    X. Zhang, Z. Luan, X. Liu, A review of research on fault diagnosis of rolling bearings based on deep learning. Plant Mainten. Eng. 18, 130–133 (2017)Google Scholar
  16. 16.
    L. Guo, H. Gao, Y. Zhang, H. Huang, Research on bearing state recognition based on deep learning theory. J. Vib. Shock 35(12), 167–171 (2016)Google Scholar
  17. 17.
    S. Zhang, H. Yongtao, A. Jiang et al., Bearing fault diagnosis based on dual tree complex wavelet and deep belief network. China Mech. Eng. 28(5), 532–536 (2017)Google Scholar
  18. 18.
    J. Wei, X. Yang, J. Huang et al., Fault diagnosis of rolling bearing based on deep neural network. Comb. Mach. Tool Auto. Mach. Technol. 11, 88–91 (2017)Google Scholar
  19. 19.
    Y. Jia, Robust control with decoupling performance for steering and traction of 4WS vehicles under velocity-varying motion. IEEE Trans. Control Syst. Technol. 8(3), 554–569 (2000)Google Scholar
  20. 20.
    Y. Jia, Alternative proofs for improved LMI representations for the analysis and the design of continuous-time systems with polytopic type uncertainty: a predictive approach. IEEE Trans. Auto. Control 48(8), 1413–1416 (2003)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Qiulin Dan
    • 1
    • 2
    Email author
  • Xuyu Liu
    • 1
  • Yi Chai
    • 1
    • 2
  • Ke Zhang
    • 1
    • 2
  • Hao Li
    • 1
    • 2
  1. 1.School of AutomationChongqing UniversityChongqing CityChina
  2. 2.Key Laboratory of Complex System Safety and ControlMinistry of Education, Chongqing UniversityChongqing CityChina

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