Skip to main content
SpringerLink
Log in

A rub fault recognition method based on generative adversarial nets

  • Original Article
  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

Faced with the problem of valid data shortage data in practical. There's not enough data to train classifiers which can be satisfied to detect impact-rubbing faults in rotary machine. Bedsides, the large number of noises in working enviroment make the useful signal contaminated. Based on this problem, this paper proposes a rubbing fault recognition method based on a generative adversarial nets named deep convolution generative adversarial nets (DCGAN), which is based on a deep convolutional network frame with generation and discrimination models. The acquired signal is processed by time frequency analysis further to get spectrogram. The DCGAN can perform feature conversion and map it to the potential feature subspace to obtain more robust features. The results illustrate that the proposed method can achieve a much more excellent recognition effect. Thus, the proposed DCGAN model is an effective way to recognize impact-rubbing fault in the practical.

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

Similar content being viewed by others

References

  1. J. Li, A. Deng, Y. Yang, X. Cheng, D. Liu and L. Zhao, A new iterative near-field coherent subspace method for rub-impact fault localization using AE technique, Journal of Mechanical Science and Technology, 31 (5) (2017) 2035–2045.

    Article  Google Scholar 

  2. A. Deng, H. Cao, H. Tong, L. Zhao, K. Qin and X. Yan, Recognition of acoustic emission signal based on the algorithms of TDNN and GMM, Applied Mathematics & Information Sciences, 8 (2) (2014) 907–916.

    Article  Google Scholar 

  3. A. Deng, H. Tong, J. Tang, H. Cao, K. Qin and X. Yan, Study on location algorithms of beamforming based on MVDR, Applied Mathematics & Information Sciences, 7 (6) (2013) 2455–2466.

    Article  Google Scholar 

  4. A. D. Deng, X. D. Zhang, J. N. Tang, L. Zhao and K. Qin, Localization of acoustic emission source based on chaotic neural networks, Applied Mathematics & Information Sciences, 6 (3) (2012) 713–719.

    Google Scholar 

  5. S. Al-Dossary, R. I. R. Hamzah and D. Mba, Observations of changes in acoustic emission waveform for varying seeded defect sizes in a rolling element bearing, Applied Acoustics, 70 (1) (2009) 58–81.

    Article  Google Scholar 

  6. Q. Chen et al., Prediction of the transient energy response for complex vibro-acoustic systems, Journal of Mechanical Science and Technology, 33 (2) (2019) 495–504.

    Article  Google Scholar 

  7. A. Morhain and D. Mba, Bearing defect diagnosis and acoustic emission, ARCHIVE Proceedings of the Institution of Mechanical Engineers Part J. Journal of Engineering Tribology, 217 (4) (2003) 257–272.

    Article  Google Scholar 

  8. C. Li, J. Valente de Oliveira, M. Cerrada, D. Cabrera, R. V. Sánchez and G. Zurita, A systematic review of fuzzy formalisms for bearing fault diagnosis, IEEE Transactions on Fuzzy Systems (2018).

    Google Scholar 

  9. C. Li et al., A comparison of fuzzy clustering algorithms for bearing fault diagnosis, Journal of Intelligent & Fuzzy Systems, 34 (6) (2018) 3565–3580.

    Article  Google Scholar 

  10. J. H. Park, J. U. Gu and N. S. Choi, Acoustic emission characteristics of methacrylate-based composite and silorane-based composite during dental restoration according to a variety of C-factor, Journal of Mechanical Science and Technology, 31 (9) (2017) 4067–4072.

    Article  Google Scholar 

  11. L. Hang, Statistical Learning Method, Tsinghua University Press (2012) 17–18.

    Google Scholar 

  12. I. J. Goodfellow et al., Generative Adversarial Nets, Advances in Neural Information Processing Systems (2014) 2672–2680.

    Google Scholar 

  13. N. Killoran et al., Generating and designing DNA with deep generative models, NIPS 2017 Computational Biology Workshop, arXiv preprint arXiv:1712.06148 (2017).

    Google Scholar 

  14. T. Schlegl et al., Unsupervised anomaly detection with generative adversarial networks to guide marker discovery, Information Processing in Medical Imaging (2017) 146–157.

    Chapter  Google Scholar 

  15. T. Kim et al., Learning to discover cross-domain relations with generative adversarial networks, arXiv preprint arXiv:1703. 05192 (2017).

    Google Scholar 

  16. A. Antoniou, A. Storkey and H. Edwards, Data augmentation generative adversarial networks, arXiv preprint arXiv:1711. 04340 (2017).

    Google Scholar 

  17. A. J. Bell and T. J. Sejnowski, An information-maximization approach to blind separation and blind deconvolution, Neural Computation, 7 (6) (1995) 1129–1159.

    Article  Google Scholar 

  18. Y. Yong et al., Mine Water Inrush Sources Online Discrimination Model Using Fluorescence Spectrum and CNN, IEEE Access, 6 (2018) 47828–47835.

    Article  Google Scholar 

  19. M. Arjovsky, S. Chintala and L. Bottou, Wasserstein gan, arXiv preprint arXiv:1701.07875 (2017).

    Google Scholar 

  20. S. Nowozin, B. Cseke and R. Tomioka, f-gan: Training generative neural samplers using variational divergence minimization, Advances in Neural Information Processing Systems (2016) 271–279.

    Google Scholar 

  21. A. Radford, L. Metz and S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, arXiv preprint arXiv:1511.06434 (2015).

    Google Scholar 

  22. J. Li et al., A new iterative near-field coherent subspace method for rub-impact fault localization using AE technique, Journal of Mechanical Science and Technology, 31 (5) (2017) 2035–2045.

    Article  Google Scholar 

  23. Z. Q. Chen, C. Li and R. V. Sanchez, Gearbox fault identification and classification with convolutional neural networks, Shock and Vibration, 15 (8) (2015) 18–28.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Power Engineering of China, University of Mining and Technology, Xuzhou, China

    Wei Wang, Weidong Liu & Wei Peng

  2. School of Information Engineering, Nanjing Audit University, Nanjing, China

    Jing Li

Authors
  1. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weidong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Weidong Liu or Jing Li.

Additional information

Conflict of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper.

Recommended by Editor No-cheol Park

Wang Wei is Ph.D. candidate at School of Information and Control Engineering, China University of Mining and Technology, China. He received the B.E. degree (2006) from Nanjing Institute of Technology and the M.E. degree (2010) from China University of Mining & Technology. His research interests are in Control Theory and Control Engineering. Mobile: +86 13952182087

Weidong Liu is an Associate Professor at School of Information and Control Engineering, China University of Mining and Technology, China. His research interests are in Acoustic Emission Signal Processing and Neural Networks. Mobile: +86 188 0520 6588

Jing Li received the B.A. degree (2005) from Hebei University of Engineering, Handan, the M.S. degrees (2008) from China University of Mining & Technology, and the Ph.D. degree from Southeast University (2017). She is currently working at Nanjing Audit University. Her main research interests include signal processing, sensor array technology, artificial intelligent and modeling as applied to AE signal recognition applications.

Wei Peng received the B.E. degree (2002) and the M.E.degree (2010) from China University of Mining & Technology. He is currently pursuing the Ph.D. degree at China University of Mining & Technology. His main research interests include signal processing, sensor array technology as applied to rotating machinery fault diagnosis application using AE technique.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, W., Liu, W., Li, J. et al. A rub fault recognition method based on generative adversarial nets. J Mech Sci Technol 34, 1389–1397 (2020). https://doi.org/10.1007/s12206-020-0302-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-020-0302-5

Keywords

Search

Navigation