Skip to main content
SpringerLink
Log in

Bearing fault identification based on convolutional neural network by different input modes

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

Convolutional neural networks (CNNs) have been applied to the field of fault diagnosis as one of the most widely used deep learning architectures. Different input modes of CNN for bearing fault identification were analyzed by researchers to improve recognition accuracy, such as time-domain diagram, grayscale diagram, short-time Fourier transform diagram, and continuous wavelet transform diagram. However, for the data with small sample size and high background noise, the performance of the CNN is constrained. In this paper, one CNN input mode for bearing fault recognition is proposed based on time-domain color feature diagram (TDCF) through adding red color to diagrams. The method significantly enhanced the fault characteristics of the signal, which is beneficial to the CNN extraction of bearing fault features. Convolution visualization illustrates the effectiveness of the proposed method that provides more bearing fault recognition information. Different sample size and color rate were analyzed by bearing vibration data with high noise. The results showed that the bearing fault identification method based on CNN with 0.4 TDCF obtained a highest fault identification accuracy compared with other input mode methods. The feasibility of the proposed method has been validated, which also provides one reference for other faults identification and pattern recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Nandi S, Toliyat HA, Li X (2005) Condition monitoring and fault diagnosis of electrical motors: a review. IEEE Trans Energy Convers 20(4):719–729

    Article  Google Scholar 

  2. Gharesi N, Arefi MM, Ebrahimi Z, Razavi-Far R, Saif M, Zarei J (2018) Analyzing the vibration signals for bearing defects diagnosis using the combination of SGWT feature extraction and SVM. IFAC-PapersOnLine 51(24):221–227

    Article  Google Scholar 

  3. Dunli L, Qian N, Xiaomin Y (2018) Fault diagnosis of rolling bearing based on knn-naive bayesian algorithm. Comput Meas Control 26(6):21–27

    Google Scholar 

  4. Pliego Marugán Alberto, García Márquez Fausto Pedro, Pinar Perez Jesus María, Diego Ruiz-Hernández (2018) A survey of artificial neural network in wind energy systems. Appl Energy 228:1822–1836

    Article  Google Scholar 

  5. Liu F, Lin G, Shen C (2015) Crf learning with cnn features for image segmentation. Pattern Recogn 48(10):2983–2992

    Article  Google Scholar 

  6. Fayek HM, Lech M, Cavedon L (2017) Evaluating deep learning architectures for speech emotion recognition. Neural Netw 92:60–68

    Article  Google Scholar 

  7. Wu S, Wang D (2019) Effect of subject’s age and gender on face recognition results. J Vis Commun Image R 60:116–122

    Article  Google Scholar 

  8. Tamilselvan P, Wang P (2013) Failure diagnosis using deep belief learning based health state classification. Reliab Eng Syst Saf 115(Jul):124–135

    Article  Google Scholar 

  9. Zhang W, Peng G, Li C (2017) Rolling element bearings fault intelligent diagnosis based on convolutional neural networks using raw sensing signal. In: Advances in intelligent information hiding and multimedia signal processing. Springer, Cham, pp 77–84

  10. Lu C, Wang Z, Zhou B (2017) Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification. Adv Eng Inform 32:139–151

    Article  Google Scholar 

  11. Chang Y, Chen J, Qu C, Pan T (2020) Intelligent fault diagnosis of wind turbines via a deep learning network using parallel convolution layers with multi-scale kernels. Renew Energ 153:205–213

    Article  Google Scholar 

  12. Hoang D, Kang H (2019) Rolling element bearing fault diagnosis using convolutional neural network and vibration image. Cogn Syst Res 53:42–50

    Article  Google Scholar 

  13. Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25(2):1097–1105

    Google Scholar 

  14. Zhang Y, Xing K, Bai R, Sun D, Meng Z (2020) An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image. Measurement 157:107667

    Article  Google Scholar 

  15. Zhu Z, Peng G, Chen Y, Gao H (2019) A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis. Neurocomputing 323:62–75

    Article  Google Scholar 

  16. Islam MMM, Kim J (2019) Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network. Comput Ind 106:142–153

    Article  Google Scholar 

  17. Lecun Y, Boser B, Denker J, Henderson D, Howard R, Hubbard W et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551

    Article  Google Scholar 

  18. Ji Z, Sun Y, Yu Y, Guo J, Pang Y (2018) Semantic softmax loss for zero-shot learning. Neurocomputing 316:369–375

    Article  Google Scholar 

  19. Zhang J, Sun Y, Guo L, Gao H, Hong X, Song H (2020) A new bearing fault diagnosis method based on modified convolutional neural networks. Chin J Aeronaut 33(2):439–447

    Article  Google Scholar 

  20. Loparo KA (2013) Bearing data center. Case Western Reserve University

  21. Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 2921–2929

Download references

Acknowledgement

The authors gratefully acknowledge the financial support by the National Key Research and Development Program of China (Grant No. 2018YFC0810500).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, 100083, China

    Tian Han, ZhiXin Tian & Zhongjun Yin

  2. LKC Faculty of Engineering, University Tunku Abdul Rahman, Sungai Long Campus, Cheras, 43000, Kajang, Selangor, Malaysia

    Andy C. C. Tan

Authors
  1. Tian Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. ZhiXin Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhongjun Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andy C. C. Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tian Han.

Additional information

Technical Editor: Wallace Moreira Bessa, D.Sc.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, T., Tian, Z., Yin, Z. et al. Bearing fault identification based on convolutional neural network by different input modes. J Braz. Soc. Mech. Sci. Eng. 42, 474 (2020). https://doi.org/10.1007/s40430-020-02561-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40430-020-02561-6

Keywords

Search

Navigation