Advertisement

An End-to-End Approach for Bearing Fault Diagnosis Based on a Deep Convolution Neural Network

  • Liang ChenEmail author
  • Yuxuan Zhuang
  • Jinghua Zhang
  • Jianming Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)

Abstract

Traditional methods for bearing fault diagnosis mostly utilized a shallow model like support vector machine (SVM) that required professional machinery skills and much of knowledge. Deep models like deep belief network (DBN) had shown its advantage in fault feature extraction without prior knowledge. In this paper, an end-to-end approach based on deep convolution neural network (DCNN) is presented. The approach embodying the idea of end to end diagnosis has only one simple and elegant convolution neural network and don’t need any exquisite hierarchical structure that was used in the traditional methods. The samples of time-domain signals are inputted into the proposed model without any frequency transformation, and the approach can diagnosis bearing fault types and fault sizes simultaneously as output. Experimental researches had shown that the approach has the advantages such as a simple structure, less iteration and real-time, while its accuracy on the diagnosis of fault types and fault sizes can still be guaranteed.

Keywords

Fault diagnosis Deep convolution neural network Bearing End to end approach 

References

  1. 1.
    Venkatsubramanian, V., Rengaswamy, R., Yin, K., et al.: A review of process fault detection and diagnosis Part I: quantitative model-based methods. Comput. Chem. Eng. 27, 293–311 (2003)CrossRefGoogle Scholar
  2. 2.
    Su, Z., Tang, B., Liu, Z., et al.: Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine. Neurocomputing 157, 208–222 (2015)CrossRefGoogle Scholar
  3. 3.
    Chen, Z., Li, C., Sanchez, R.: Gearbox fault identification and classification with convolutional neural networks. Shock Vibr. 2015, 1–10 (2015)Google Scholar
  4. 4.
    Li, C., Sanchez, R., Zurita, G., et al.: Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing 168, 119–127 (2015)CrossRefGoogle Scholar
  5. 5.
    Li, P., Kong, F., He, Q., et al.: Multiscale slope feature extraction for rotating machinery fault diagnosis using wavelet analysis. Meas. J. Int. Meas. Confederation 46, 497–505 (2013)CrossRefGoogle Scholar
  6. 6.
    Ye, Z., Yang, C.G., Zhang, J., et al.: Fault diagnosis of railway rolling bearing based on wavelet analysis and FCM. Int. J. Digit. Content Technol Appl. 5, 47–58 (2011)Google Scholar
  7. 7.
    Eslamloueyan, R.: Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee-Eastman process. Appl. Soft Comput. J. 11, 1407–1415 (2011)CrossRefGoogle Scholar
  8. 8.
    Zhu, K., Song, X., Xue, D.: A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm. Measurement 47, 669–675 (2014)CrossRefGoogle Scholar
  9. 9.
    Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)CrossRefGoogle Scholar
  10. 10.
    Jia, F., Lei, Y., Lin, J., et al.: Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech. Syst. Signal Process. 72–73, 303–315 (2016)CrossRefGoogle Scholar
  11. 11.
    Gan, M., Wang, C., Zhu, C.: Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mech. Syst. Signal Process. 72–73, 92–104 (2016)CrossRefGoogle Scholar
  12. 12.
    Guo, X., Chen, L., Shen, C.: Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement 93, 490–502 (2016)CrossRefGoogle Scholar
  13. 13.
    Barua, A., Khorasani, K.: Hierarchical fault diagnosis and fuzzy rule-based reasoning for satellites formation flight. IEEE Trans. Aerosp. Electron. Syst. 47, 2435–2456 (2011)CrossRefGoogle Scholar
  14. 14.
    Zhou, S., Lin, L., Xu, J.M.: Conditional fault diagnosis of hierarchical hypercubes. Int. J. Comput. Math. 89, 2152–2164 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Gu, Z.J., Wang, C.: A hierarchical model of network fault diagnosis. In: International Conference on Convergence Computer Technology, pp. 128–131. IEEE Computer Society, Washington (2012)Google Scholar
  16. 16.
    Hu, B., She, J., Yokoyama, R.: Hierarchical fault diagnosis for power systems based on equivalent-input-disturbance approach. IEEE Trans. Industr. Electron. 60, 3529–3538 (2013)CrossRefGoogle Scholar
  17. 17.
    Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., et al.: Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Liang Chen
    • 1
    • 2
    Email author
  • Yuxuan Zhuang
    • 1
  • Jinghua Zhang
    • 1
  • Jianming Wang
    • 2
  1. 1.School of Mechanical and Electric EngineeringSoochow UniversitySuzhouChina
  2. 2.Post-Doctoral Research CenterSuzhou Asia-Pacific Metals Co. LTDSuzhouChina

Personalised recommendations