An Approach for Feature Extraction and Diagnosis of Motor Rotor Bearing Based on Convolution Neural Network

  • Hao Wang
  • Dongsheng YangEmail author
  • Yongheng Pang
  • Ting Li
  • Bo Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11301)


The traditional rotor bearing fault diagnosis and analysis method is difficult to get the prior knowledge and experience, resulting in the low accuracy of fault diagnosis. In this paper, a method of fault feature extraction and diagnosis of rotor bearing based on convolution neural network is proposed. This method uses the chaotic characteristic of the vibration signal of the rotor bearing, uses the phase space reconstruction method to obtain the embedding dimension as the scale of the convolution neural network input composition, avoid the limitation of traditional frequency analysis method in the process of decomposition and transformation, the fault information can be extracted more comprehensively. In order to make full use of the advantages of the convolution neural network in the field of two-dimensional image analysis and improve the accuracy of the fault diagnosis model, a method of learning input form neural network based on convolution neural network for grayscale graph is proposed. The results of the simulation show the effectiveness of the method.


Bearing fault diagnosis Phase space reconstruction Convolution neural network Gray-scale image 


  1. 1.
    Tang, G.J., Pang, B., Liu, S.K.: Fault diagnosis of rolling bearings based on difference spectrum of singular value and stationary subspace analysis. J. Vib. Shock 34(11), 83–87 (2015)Google Scholar
  2. 2.
    Zhao, D.Z., Li, J.Y., Cheng, W.D., et al.: Rolling element bearing fault diagnosis based on generalized demodulation algorithm under variable rotational speed. J. Vib. Eng. 2017(5), 865–873 (2017)Google Scholar
  3. 3.
    Zhang, X.N., Zeng, Q.S., Wan, H.: Bearing fault diagnosis based on improved wavelet denoising and EMD method. Meas. Control Technol. 33(1), 23–26 (2014)Google Scholar
  4. 4.
    Qin, Y.: A new family of model-based impulsive wavelets and their sparse representation for rolling bearing fault diagnosis. IEEE Trans. Ind. Electron. 65(3), 2716–2726 (2017)CrossRefGoogle Scholar
  5. 5.
    Shao, H., Jiang, H., Li, X., et al.: Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowl.-Based Syst. (2017)Google Scholar
  6. 6.
    Yuan, J.H., Han, T., Tang, J., et al.: An approach to intelligent fault diagnosis of rolling bearing using wavelet time-frequency representations and CNN. Mach. Des. Res. 2017(2), 93–97 (2017)Google Scholar
  7. 7.
    He, M., He, D.: Deep learning based approach for bearing fault diagnosis. IEEE Trans. Ind. Appl. 53(3), 3057–3065 (2017)CrossRefGoogle Scholar
  8. 8.
    Oh, H., Jung, J.H., Jeon, B.C., et al.: Scalable and unsupervised feature engineering using vibration-imaging and deep learning for rotor system diagnosis. IEEE Trans. Ind. Electron. 65(4), 3539–3549 (2018)CrossRefGoogle Scholar
  9. 9.
    Zhang, W., Peng, G., Li, C., et al.: A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors 17(2), 425 (2017)CrossRefGoogle Scholar
  10. 10.
    Kim, H.S., Eykholt, R., Salas, J.D.: Nonlinear dynamics, delay times, and embedding windows. Phys. D-Nonlinear Phenom. 127(1–2), 48–60 (1999)CrossRefGoogle Scholar
  11. 11.
    Liu, Y.B., He, B., Liu, F., et al.: Comprehensive recognition of rolling bearing fault pattern and fault degrees based on two-layer similarity in phase space. J. Vib. Shock 36(4), 178–184 (2017)Google Scholar
  12. 12.
    He, K., Zhang, X., Ren, S., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)CrossRefGoogle Scholar
  13. 13.
    Chang, L., Deng, X.M., Zhou, M.Q., et al.: Convolutional neural networks in image understanding. Acta Autom. Sin. 42(9), 1300–1312 (2016)zbMATHGoogle Scholar
  14. 14.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: 25th International Conference on Neural Information Processing Systems. Curran Associates Inc., pp. 1097–1105. ACM, Lake Tahoe, Nevada (2012)Google Scholar
  15. 15.
    Lecun, Y., Boser, B., Denker, J.S., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)CrossRefGoogle Scholar
  16. 16.
    Case western reserve university bearings vibration dataset.

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hao Wang
    • 1
  • Dongsheng Yang
    • 1
    Email author
  • Yongheng Pang
    • 1
  • Ting Li
    • 1
  • Bo Hu
    • 2
  1. 1.Northeastern UniversityShenyangChina
  2. 2.State Grid Huludao Electric Power Supply CompanyHuludaoChina

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