Skip to main content
SpringerLink
Log in

A novel bearing fault diagnosis method using deep residual learning network

  • 1200: Machine Vision Theory and Applications for Cyber Physical Systems
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Bearing fault diagnosis is a serious problem on which researchers have focused to ensure the reliability and availability of rotating machinery. Knowledge-based methods are capable of providing promising solution to bearing diagnosis problem with high accuracy performance thanks to effectively processing collected sensor and actuator data. Deep learning (DL) has the advantage of ignoring feature extraction and providing accurate diagnosis among the machine learning algorithms. In order to address this issue, in this paper, a novel DL based model is presented for fault detection and classification of motor bearing. In this work, first, time domain signals are converted to images by a proposed signal-to-image conversion approach. Then, the converted gray-scale images are fed into a novel deep residual learning (DRL) network structured to learn end-to-end mapping between images and health condition of the motor bearing. The performance of the proposed DRL network is evaluated on a commonly used real vibration dataset provided by Case Western Reserve University (CWRU). Experimental results obtained for 10 different health condition demonstrate encouraging and outperforming performance with an average accuracy of \(99.98\%\) compared to the state-of-art knowledge-based bearing fault diagnosis methods.

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

Similar content being viewed by others

References

  1. Baraldi P, Cannarile F, Di Maio F, Zio E (2016) Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions. Eng Appl Artif Intell 56:1–13

    Article  Google Scholar 

  2. Bishop CM et al (1995) Neural networks for pattern recognition. Oxford University Press

  3. Bjorck N, Gomes CP, Selman B, Weinberger KQ (2018) Understanding batch normalization. In: Adv Neural Inf Process Syst pp. 7694–7705

  4. Chen XW, Lin X (2014) Big data deep learning: challenges and perspectives. IEEE Access 2:514–525

    Article  Google Scholar 

  5. Chen Z, Mauricio A, Li W, Gryllias K (2020) A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks. Mech Syst Signal Process 140:106683

    Article  Google Scholar 

  6. Cho HC, Knowles J, Fadali MS, Lee KS (2009) Fault detection and isolation of induction motors using recurrent neural networks and dynamic bayesian modeling. IEEE Trans Control Syst Technol 18(2):430–437

    Article  Google Scholar 

  7. Dhamande LS, Chaudhari MB (2016) Bearing fault diagnosis based on statistical feature extraction in time and frequency domain and neural network. Int J Veh Struct Syst 8(4):229

    Google Scholar 

  8. Ding X, He Q (2017) Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis. IEEE Trans Instrum Meas 66(8):1926–1935

    Article  Google Scholar 

  9. Eren L (2017) Bearing fault detection by one-dimensional convolutional neural networks. Math Probl Eng

  10. Gan M, Wang C et al (2016) 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 72:92–104

    Article  Google Scholar 

  11. Gao Z, Cecati C, Ding SX (2015) A survey of fault diagnosis and fault-tolerant techniques-part i: Fault diagnosis with model-based and signal-based approaches. IEEE Trans Ind Electron 62(6):3757–3767

    Article  Google Scholar 

  12. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics pp. 249–256

  13. Guo L, Lei Y, Xing S, Yan T, Li N (2019) Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Trans Ind Electron 66(9):7316–7325

    Article  Google Scholar 

  14. Guo L, Li N, Jia F, Lei Y, Lin J (2017) A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing 240:98–109

    Article  Google Scholar 

  15. Guo X, Chen L, Shen C (2016) Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement 93:490–502

    Article  Google Scholar 

  16. Han T, Jiang D, Wang N (2016) The fault feature extraction of rolling bearing based on emd and difference spectrum of singular value. Shock and vibration

  17. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc IEEE Conf Comput Vis Pattern Recognit pp. 770–778

  18. He XH, Wang D, Li YF, Zhou CH (2016) A novel bearing fault diagnosis method based on gaussian restricted boltzmann machine. Math Prob Eng

  19. Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167

  20. Janssens O, Slavkovikj V, Vervisch B, Stockman K, Loccufier M, Verstockt S, Van de Walle R, Van Hoecke S (2016) Convolutional neural network based fault detection for rotating machinery. J Sound Vib 377:331–345

    Article  Google Scholar 

  21. Jia F, Lei Y, Guo L, Lin J, Xing S (2018) A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines. Neurocomputing 272:619–628

    Article  Google Scholar 

  22. Kang M, Kim J, Kim JM (2015) Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm. Inf Sci 294:423–438

    Article  MathSciNet  Google Scholar 

  23. Khorram A, Khalooei M, Rezghi M (2021) End-to-end cnn+ lstm deep learning approach for bearing fault diagnosis. Appl Intell 51(2):736–751

    Article  Google Scholar 

  24. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Article  Google Scholar 

  25. Lei Y, Jia F, Lin J, Xing S, Ding SX (2016) An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans Ind Electron 63(5):3137–3147

    Article  Google Scholar 

  26. Liu ZH, Lu BL, Wei HL, Chen L, Li XH, Rätsch M (2019) Deep adversarial domain adaptation model for bearing fault diagnosis. IEEE Trans Syst Man Cybern Syst

  27. Loparo K (2012) Case western reserve university bearing data center

  28. Pons-Llinares J, Antonino-Daviu JA, Riera-Guasp M, Lee SB, Kang TJ, Yang C (2014) Advanced induction motor rotor fault diagnosis via continuous and discrete time-frequency tools. IEEE Trans Ind Electron 62(3):1791–1802

    Article  Google Scholar 

  29. Ripley BD, Hjort N (1996) Pattern recognition and neural networks. Cambridge University Press

  30. Shao H, Jiang H, Zhang X, Niu M (2015) Rolling bearing fault diagnosis using an optimization deep belief network. Meas Sci Technol 26(11):115002

    Article  Google Scholar 

  31. Soualhi A, Medjaher K, Zerhouni N (2014) Bearing health monitoring based on hilbert-huang transform, support vector machine, and regression. IEEE Trans Instrum Meas 64(1):52–62

    Article  Google Scholar 

  32. Venables WN, Ripley BD (2013) Modern applied statistics with S-PLUS. Springer Science & Business Media

  33. Wang Z, Zhang Q, Xiong J, Xiao M, Sun G, He J (2017) Fault diagnosis of a rolling bearing using wavelet packet denoising and random forests. IEEE Sensors J 17(17):5581–5588

    Article  Google Scholar 

  34. Wen L, Gao L, Li X (2019) A new deep transfer learning based on sparse auto-encoder for fault diagnosis. IEEE Trans Syst Man Cybern Syst 49(1):136–144

    Article  Google Scholar 

  35. Wen L, Li X, Gao L, Zhang Y (2018) A new convolutional neural network-based data-driven fault diagnosis method. IEEE Trans Ind Electron 65(7):5990–5998

    Article  Google Scholar 

  36. Xia M, Li T, Xu L, Liu L, De Silva CW (2018) Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks. IEEE/ASME Trans Mechatron 23(1):101–110

    Article  Google Scholar 

  37. Xu Y, Li Z, Wang S, Li W, Sarkodie-Gyan T, Feng S (2021) A hybrid deep-learning model for fault diagnosis of rolling bearings. Measurement 169:108502

    Article  Google Scholar 

  38. Yan X, Jia M, Zhang W, Zhu L (2018) Fault diagnosis of rolling element bearing using a new optimal scale morphology analysis method. ISA Trans 73:165–180

    Article  Google Scholar 

  39. Zhang X, Liang Y, Zhou J et al (2015) A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized svm. Measurement 69:164–179

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Karadeniz Technical University, Trabzon, 61080, Turkey

    Selen Ayas

  2. Department of Electrical and Electronics Engineering, Karadeniz Technical University, Trabzon, 61080, Turkey

    Mustafa Sinasi Ayas

Authors
  1. Selen Ayas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mustafa Sinasi Ayas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Selen Ayas.

Additional information

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

Ayas, S., Ayas, M.S. A novel bearing fault diagnosis method using deep residual learning network. Multimed Tools Appl 81, 22407–22423 (2022). https://doi.org/10.1007/s11042-021-11617-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11617-1

Keywords

Search

Navigation