Skip to main content
SpringerLink
Log in

Research on Bearing Variable Condition Fault Diagnosis Based on RDADNN

  • Original Research Article
  • Published:
Journal of Failure Analysis and Prevention Aims and scope Submit manuscript

Abstract

Due to the influence of working conditions, the data distribution of bearings is challenging to maintain consistency in practical engineering, which leads to the problem of low fault diagnosis accuracy of bearings under variable working conditions. Therefore, this paper proposes a bearing fault diagnosis method based on regularized domain adaptive deep neural network (RDADNN). Firstly, a wide convolutional neural network with an embedded squeeze and excitation block module is proposed to improve the source and target domain’s feature extraction effect. Then, the coral criterion is used to match the difference in data distribution between the source domain and target domain, and label regularization is used to improve the model’s generalization ability. Finally, the feasibility of RDADNN is verified by bearing a data set. The experimental results show that the proposed method can effectively realize the cross-domain fault diagnosis of bearings. It performs superior in six cross-domain scenarios in two sets of experiments and has good robustness and generalization.

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

Similar content being viewed by others

References

  1. Z. Jin, D. He, Z. Wei, Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN. Eng. Appl. Artif. Intell. 110, 104713 (2022)

    Article  Google Scholar 

  2. D. He, C. Liu, Z. Jin, R. Ma, Y. Chen, S. Shan, Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning. Energy. 239, 122108 (2022)

    Article  Google Scholar 

  3. Z. Lao, D. He, Z. Wei, Intelligent fault diagnosis for rail transit switch machine based on adaptive feature selection and improved LightGBM. Eng. Fall. Anal. 148, 107219 (2023)

    Article  Google Scholar 

  4. Z. Wei, D. He, Z. Jin, B. Liu, S. Shan, Y. Chen, J. Miao, Density-based affinity propagation tensor clustering for intelligent fault diagnosis of train bogie bearing. IEEE Trans. Intell. Transp. Syst. 4(6), 6053–6064 (2023)

    Article  Google Scholar 

  5. J. Huang, L. Cui, Tensor singular spectrum decomposition: multisensor denoising algorithm and application. IEEE Trans. Instrum. Meas. 72, 1–15 (2023)

    Google Scholar 

  6. W. Du, J. Zhou, Z. Wang, R. Li, J. Wang, Application of improved singular spectrum decomposition method for composite fault diagnosis of gear boxes. Sensors. 18, 3804 (2018)

    Article  Google Scholar 

  7. W. Xu, Y. Shen, Q. Jiang, Q. Zhu, F. Xu, Rolling bearing fault feature extraction via improved SSD and a singular-value energy autocorrelation coefficient spectrum. Meas. Sci. Technol. 33, 085112 (2022)

    Article  Google Scholar 

  8. Z. Jin, D. He, Z. Lao, Z. Wei, Early intelligent fault diagnosis of rotating machinery based on IWOA-VMD and DMKELM. Nonlinear Dyn. 111, 5287–5306 (2023)

    Article  Google Scholar 

  9. H. Li, T. Liu, X. Wu, Q. Chen, An optimized VMD method and its applications in bearing fault diagnosis. Measurement. 166, 108185 (2020)

    Article  Google Scholar 

  10. A. Dibaj, R. Hassannejad, M.B. Ettefagh, M.B. Ehghaghi, Incipient fault diagnosis of bearings based on parameter-optimized VMD and envelope spectrum weighted kurtosis index with a new sensitivity assessment threshold. ISA. Trans. 114, 413–433 (2021)

    Article  Google Scholar 

  11. T. Mian, A. Choudhary, S. Fatima, Vibration and infrared thermography based multiple fault diagnosis of bearing using deep learning. Nondestruct. Test. Eval. 38, 275–296 (2023)

    Article  Google Scholar 

  12. X. Wang, D. Mao, X. Li, Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network. Measurement. 173, 108518 (2021)

    Article  Google Scholar 

  13. D. Wang, Q. Guo, Y. Song, S. Gao, Y. Li, Application of multiscale learning neural network based on CNN in bearing fault diagnosis. J. Signal Process. Syst. 91, 1205–1217 (2019)

    Article  Google Scholar 

  14. Z. Guo, M. Yang, X. Huang, Bearing fault diagnosis based on speed signal and CNN model. Energy Rep. 8, 904–913 (2022)

    Article  Google Scholar 

  15. T. Jin, C. Yan, C. Chen, Z. Yang, H. Tian, S. Wang, Light neural network with fewer parameters based on CNN for fault diagnosis of rotating machinery. Measurement. 181, 109639 (2021)

    Article  Google Scholar 

  16. M.R. Bhuiyan, J. Uddin, Deep transfer learning models for industrial fault diagnosis using vibration and acoustic sensors data: a review. Vibration. 6, 218–238 (2023)

    Article  Google Scholar 

  17. H. Zhao, X. Yang, B. Chen, H. Chen, Bearing fault diagnosis using transfer learning and optimized deep belief network. Meas. Sci. Technol. 33, 065009 (2022)

    Article  Google Scholar 

  18. Z. Wu, H. Jiang, K. Zhao, X. Li, An adaptive deep transfer learning method for bearing fault diagnosis. Measurement. 151, 107227 (2020)

    Article  Google Scholar 

  19. J. Li, M. Lin, Y. Li, X. Wang, Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions. Energy. 254, 124358 (2022)

    Article  Google Scholar 

  20. X. Shao, C.S. Kim, Unsupervised domain adaptive 1D-CNN for fault diagnosis of bearing. Sensors. 22, 4156 (2022)

    Article  Google Scholar 

  21. B. Wang, Y. Wei, S. Liu, D. Zhao, X. Liu, Unsupervised joint subdomain adaptation network for fault diagnosis. IEEE Sens. J. 22, 8891–8903 (2022)

    Article  Google Scholar 

  22. H. Wang, J. Xu, R. Yan, R.X. Gao, A new intelligent bearing fault diagnosis method using SDP representation and SE-CNN. IEEE Trans. Instrum. Meas. 69, 2377–2389 (2019)

    Article  Google Scholar 

  23. X. Zhang, C. He, Y. Lu, B. Chen, L. Zhu, L. Zhang, Fault diagnosis for small samples based on attention mechanism. Measurement. 187, 110242 (2022)

    Article  Google Scholar 

  24. B. Wang, B. Wang, Y. Ning, A novel transfer learning fault diagnosis method for rolling bearing based on feature correlation matching. Meas. Sci. Technol. 33, 125006 (2022)

    Article  Google Scholar 

Download references

Acknowledgments

The research was supported by the Project of Improving the Basic Ability of Scientific Research of Young and Middle-aged Teachers in Guangxi Universities [grant number 2022KY1134], Natural Science Foundation of Guangxi Transport Vocational and Technical College [grant number JZY2020KAZ16] and Innovation Project of Guangxi Graduate Education [grant number YCBZ2023039].

Author information

Authors and Affiliations

  1. School of Mechanical Engineering of Guangxi University, Nanning, China

    Zhenzhen Jin

  2. Guangxi Transport Vocational and Technical College, Nanning, China

    Yingqian Sun

Authors
  1. Zhenzhen Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yingqian Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yingqian Sun.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jin, Z., Sun, Y. Research on Bearing Variable Condition Fault Diagnosis Based on RDADNN. J Fail. Anal. and Preven. 23, 1663–1674 (2023). https://doi.org/10.1007/s11668-023-01713-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11668-023-01713-9

Keywords

Search

Navigation