Skip to main content
SpringerLink
Log in

Bearing Fault Classification Using Wavelet Energy and Autoencoder

  • Conference paper
  • First Online:
Distributed Computing and Internet Technology (ICDCIT 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11969))

Abstract

Today’s modern industry has widely accepted the intelligent condition monitoring system to improve the industrial organization. As an effect, the data-driven-based fault diagnosis methods are designed by integrating signal processing techniques along with artificial intelligence methods. Various signal processing approaches have been proposed for feature extraction from vibration signals to construct the fault feature space, and thus, over the years, the feature space has increased rapidly. Also, the challenge is to identify the promising features from the space for improving diagnosis performance. Therefore, in this paper, wavelet energy is presented as an input feature set to the fault diagnosis system. In this paper, wavelet energy is utilized to represent the multiple faults for reducing the requirement of number features, and therefore, the complex task of feature extraction becomes simple. Further, the convolutional autoencoder has assisted in finding more distinguishing fault feature from wavelet energy to improve the diagnosis task using extreme learning machine. The proposed method testified using two vibration datasets, and decent results are achieved. The effect of autoencoder on fault diagnosis performance has been observed in comparison to principal component analysis (PCA). Also, the consequence has seen in the size of the extreme learning machine (ELM) architecture.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hossain, M.S., Muhammad, G.: Cloud-assisted industrial internet of things (IIoT) - enabled framework for health monitoring. Comput. Netw. 101, 192–202 (2016)

    Article  Google Scholar 

  2. Ren, L., Cheng, X., Wang, X., Cui, J., Zhang, L.: Multi-scale dense gate recurrent unit networks for bearing remaining useful life prediction. Future Gener. Comput. Syst. 94, 601–609 (2019)

    Article  Google Scholar 

  3. Kan, C., Yang, H., Kumara, S.: Parallel computing and network analytics for fast industrial internet-of-things (IIoT) machine information processing and condition monitoring. J. Manuf. Syst. 46, 282–293 (2018)

    Article  Google Scholar 

  4. Bellini, A., Filippetti, F., Tassoni, C., Capolino, G.A.: Advances in diagnostic techniques for induction machines. IEEE Trans. Ind. Electron. 55(12), 4109–4126 (2008)

    Article  Google Scholar 

  5. Henriquez, P., Alonso, J.B., Ferrer, M.A., Travieso, C.M.: Review of automatic fault diagnosis systems using audio and vibration signals. IEEE Trans. Syst. Man Cybern. Syst. 44(5), 642–652 (2014)

    Article  Google Scholar 

  6. Dai, X., Gao, Z.: From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis. IEEE Trans. Ind. Inf. 9(4), 2226–2238 (2013)

    Article  Google Scholar 

  7. Kan, M.S., Tan, A.C., Mathew, J.: A review on prognostic techniques for non-stationary and non-linear rotating systems. Mech. Syst. Sign. Process. 62, 1–20 (2015)

    Article  Google Scholar 

  8. Choudhary, A., Goyal, D., Shimi, S.L., Akula, A.: Condition monitoring and fault diagnosis of induction motors: a review. Arch. Comput. Methods Eng. 26(4), 1221–1238 (2018)

    Article  Google Scholar 

  9. El-Thalji, I., Jantunen, E.: A summary of fault modelling and predictive health monitoring of rolling element bearings. Mech. Syst. Sign. Process. 60, 252–272 (2015)

    Article  Google Scholar 

  10. Marichal, G., Artés, M., Prada, J.G., Casanova, O.: Extraction of rules for faulty bearing classification by a neuro-fuzzy approach. Mech. Syst. Sign. Process. 25(6), 2073–2082 (2011)

    Article  Google Scholar 

  11. Zhang, S., Mathew, J., Ma, L., Sun, Y.: Best basis-based intelligent machine fault diagnosis. Mech. Syst. Sign. Process. 19(2), 357–370 (2005)

    Article  Google Scholar 

  12. Kankar, P., Sharma, S.C., Harsha, S.: Fault diagnosis of ball bearings using continuous wavelet transform. Appl. Soft Comput. 11(2), 2300–2312 (2011)

    Article  Google Scholar 

  13. Kankar, P., Sharma, S.C., Harsha, S.P.: Rolling element bearing fault diagnosis using wavelet transform. Neurocomputing 74(10), 1638–1645 (2011)

    Article  Google Scholar 

  14. Udmale, S.S., Singh, S.K.: A mechanical data analysis using kurtogram and extreme learning machine. Neural Comput. Appl. 1–13 (2019). https://doi.org/10.1007/s00521-019-04398-0

  15. Soualhi, A., Medjaher, K., Zerhouni, N.: Bearing health monitoring based on Hilbert-Huang transform, support vector machine, and regression. IEEE Trans. Instrum. Meas. 64(1), 52–62 (2015)

    Article  Google Scholar 

  16. Liu, J., Wang, W., Golnaraghi, F.: An enhanced diagnostic scheme for bearing condition monitoring. IEEE Trans. Instrum. Meas. 59(2), 309–321 (2010)

    Article  Google Scholar 

  17. Udmale, S.S., Patil, S.S., Phalle, V.M., Singh, S.K.: A bearing vibration data analysis based on spectral kurtosis and ConvNet. Soft. Comput. 23(19), 1–19 (2019)

    Article  Google Scholar 

  18. Udmale, S.S., Singh, S.K., Singh, R., Sangaiah, A.K.: Multi-fault bearing classification using sensors and ConvNet-based transfer learning approach. IEEE Sens. J. 1–12 (2019). https://doi.org/10.1109/JSEN.2019.2947026

  19. Udmale, S.S., Singh, S.K., Bhirud, S.G.: A bearing data analysis based on kurtogram and deep learning sequence models. Measurement 145, 665–677 (2019)

    Article  Google Scholar 

  20. Tao, J., Liu, Y., Yang, D.: Bearing fault diagnosis based on deep belief network and multisensor information fusion. Shock Vibr. 2016, 9 (2016)

    Google Scholar 

  21. Chen, Z., Li, C., Sanchez, R.V.: Gearbox fault identification and classification with convolutional neural networks. Shock Vibr. 2015, 10 (2015)

    Google Scholar 

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

    Article  Google Scholar 

  23. Li, C., Sánchez, R.V., Zurita, G., Cerrada, M., Cabrera, D.: Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning. Sensors 16(6), 895 (2016)

    Article  Google Scholar 

  24. Chen, Z., Deng, S., Chen, X., Li, C., Sanchez, R.V., Qin, H.: Deep neural networks-based rolling bearing fault diagnosis. Microelectron. Reliab. 75, 327–333 (2017)

    Article  Google Scholar 

  25. Dhamande, L.S., Chaudhari, M.B.: Compound gear-bearing fault feature extraction using statistical features based on time-frequency method. Measurement 125, 63–77 (2018)

    Article  Google Scholar 

  26. Shao, H., Jiang, H., Li, X., Wu, S.: Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowl. Based Syst. 140, 1–14 (2018)

    Article  Google Scholar 

  27. Zhang, X., Yan, Q., Yang, J., Zhao, J., Shen, Y.: An assembly tightness detection method for bolt-jointed rotor with wavelet energy entropy. Measurement 136, 212–224 (2019)

    Article  Google Scholar 

  28. Pan, Y., Zhang, L., Wu, X., Zhang, K., Skibniewski, M.J.: Structural health monitoring and assessment using wavelet packet energy spectrum. Saf. Sci. 120, 652–665 (2019)

    Article  Google Scholar 

  29. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  30. Wang, W., Huang, Y., Wang, Y., Wang, L.: Generalized autoencoder: a neural network framework for dimensionality reduction. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 496–503, June 2014

    Google Scholar 

  31. Udmale, S.S., Singh, S.K.: Application of spectral kurtosis and improved extreme learning machine for bearing fault classification. IEEE Trans. Instrum. Meas. 68(11), 1–12 (2019)

    Article  Google Scholar 

  32. CWRU: Case Western Reserve University Bearing Data Center Website (2009). https://csegroups.case.edu/bearingdatacenter/home

  33. Smith, W.A., Randall, R.B.: Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study. Mech. Syst. Sig. Process. 64–65, 100–131 (2015)

    Article  Google Scholar 

  34. Huang, G.B., Chen, L., Siew, C.K.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Netw. 17(4), 879–892 (2006)

    Article  Google Scholar 

  35. Dong, S., Luo, T.: Bearing degradation process prediction based on the PCA and optimized LS-SVM model. Measurement 46(9), 3143–3152 (2013)

    Article  Google Scholar 

  36. Samanta, B., Al-Balushi, K.: Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech. Syst. Sig. Process. 17(2), 317–328 (2003)

    Article  Google Scholar 

Download references

Acknowledgement

Authors would like to acknowledge TEQIP-III and TEQIP-II (subcomponent 1.2.1) Centre of Excellence in Complex and Nonlinear Dynamical Systems (CoE-CNDS), VJTI, Mumbai-400019, Maharashtra, India for providing experimental environment.

Author information

Authors and Affiliations

  1. Department of Computer Engineering and IT, Veermata Jijabai Technological Institute (VJTI), Mumbai, 400019, Maharashtra, India

    Sandeep S. Udmale

  2. Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India

    Sandeep S. Udmale & Sanjay Kumar Singh

Authors
  1. Sandeep S. Udmale
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanjay Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sandeep S. Udmale .

Editor information

Editors and Affiliations

  1. Vietnam National University, Hanoi, Vietnam

    Dang Van Hung

  2. International Institute of Information Technology, Bangalore, India

    Meenakshi D´Souza

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Udmale, S.S., Singh, S.K. (2020). Bearing Fault Classification Using Wavelet Energy and Autoencoder. In: Hung, D., D´Souza, M. (eds) Distributed Computing and Internet Technology. ICDCIT 2020. Lecture Notes in Computer Science(), vol 11969. Springer, Cham. https://doi.org/10.1007/978-3-030-36987-3_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-36987-3_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36986-6

  • Online ISBN: 978-3-030-36987-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation