[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Log in

Fault Feature Extraction of Rolling Element Bearing under Complex Transmission Path Based on Multiband Signals Cross-Correlation Spectrum

  • Technical Article---Peer-Reviewed
  • Published:
Journal of Failure Analysis and Prevention Aims and scope Submit manuscript

Abstract

Aiming at the problem that bearing fault signals are influenced by complex transmission path and multiple structures of equipment, which results in strong interference components, a fault feature extraction approach for rolling element bearing based on multiband signals cross-correlation spectrum was proposed. First, the improved trend-line method was utilized to improve the calculation efficiency and the influence of the transmission path was removed. Second, the optimal and suboptimal analysis frequency bands were selected with the maximum energy ratio of the feature components as the objective function, which further suppresses the interference of irrelevant components and avoids the blindness selection of the analysis band. Finally, with the advantage of the cross-correlation spectrum, the optimal band signals were combined for analysis to enhance the fault signatures. The simulation signal and the measured bearing inner race and outer race defect signals were utilized for verification; with the help of comparisons, the results indicate that the method in this paper can effectively remove the influence of complex transmission path and accurately extract the bearing fault features from the strong background interference.

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

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. J. Antoni, Fast computation of the kurtogram for the detection of transient faults. Mech. Syst. Signal Process. 21(1), 108–124 (2007)

    Article  Google Scholar 

  2. Z. Liu, S. Yang, Y. Liu et al., Adaptive correlated Kurtogram and its applications in wheelset-bearing system fault diagnosis. Mech. Syst. Signal Process. 154, 107511 (2021)

    Article  Google Scholar 

  3. K. Zhang, Y. Xu, Z. Liao et al., A novel Fast Entrogram and its applications in rolling bearing fault diagnosis. Mech. Syst. Signal Process. 154, 107582 (2021)

    Article  Google Scholar 

  4. Y. Xu, Y. Deng, C. Ma et al., The Enfigram: a robust method for extracting repetitive transients in rolling bearing fault diagnosis. Mech. Syst. Signal Process. 158, 107779 (2021)

    Article  Google Scholar 

  5. L. Wang, Z. Liu, H. Cao et al., Subband averaging kurtogram with dual-tree complex wavelet packet transform for rotating machinery fault diagnosis. Mech. Syst. Signal Process. 142, 106755 (2020)

    Article  Google Scholar 

  6. K. Zhang, P. Chen, M. Yang et al., The Harmogram: a periodic impulses detection method and its application in bearing fault diagnosis. Mech. Syst. Signal Process. 165, 108374 (2022)

    Article  Google Scholar 

  7. Q. Liu, J. Yang, K. Zhang, An improved empirical wavelet transform and sensitive components selecting method for bearing fault. Measurement. 187, 110348 (2021)

    Article  Google Scholar 

  8. K. Yu, T.R. Lin, J. Tan et al., An adaptive sensitive frequency band selection method for empirical wavelet transform and its application in bearing fault diagnosis. Measurement. 134, 375–384 (2019)

    Article  Google Scholar 

  9. Y. Xu, Y. Cai et al., An enhanced bearing fault diagnosis method based on TVF-EMD and a high-order energy operator. Measure. Sci. Technol. (2018). https://doi.org/10.1088/1361-6501/aad499

    Article  Google Scholar 

  10. J. Chen, L. Cheng, H. Yu et al., Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis-Taguchi system. Int. J. Syst. Sci. 49(1), 147–159 (2018)

    Article  Google Scholar 

  11. J. Gu, Y. Peng, An improved complementary ensemble empirical mode decomposition method and its application in rolling bearing fault diagnosis. Digital Signal Process. 113, 103050 (2021)

    Article  Google Scholar 

  12. T. Han, Q. Liu, L. Zhang et al., Fault feature extraction of low speed roller bearing based on Teager energy operator and CEEMD. Measurement. 138, 400–408 (2019)

    Article  Google Scholar 

  13. J. Yuan, F. Ji, Y. Gao et al., Integrated ensemble noise-reconstructed empirical mode decomposition for mechanical fault detection. Mech. Syst. Signal Process. 104, 323–346 (2018)

    Article  Google Scholar 

  14. D. Wang, Y. Cai, L. Kwok et al., Making EEMD more effective in extracting bearing fault features for intelligent bearing fault diagnosis by using blind fault component separation. J. Intell. Fuzzy Syst. Appl. Eng. Technol. 34(6), 3429–3441 (2018)

    Google Scholar 

  15. X. Jiang, J. Wang, J. Shi et al., A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines. Mech. Syst. Signal Process. 116, 668–692 (2019)

    Article  Google Scholar 

  16. H. Li, Y. Xu, D. An et al., Application of a flat variational modal decomposition algorithm in fault diagnosis of rolling bearings. J. Low Frequency Noise Vib. Active Control. 39(2), 335–351 (2020)

    Article  Google Scholar 

  17. R. Gu, J. Chen, R. Hong et al., Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and Teager energy operator. Measurement. 149, 106941 (2020)

    Article  Google Scholar 

  18. D. Zhu, G. Liu, W. He et al., Fault feature extraction of rolling element bearing based on EVMD. J. Braz. Soc. Mech. Sci. Eng. 43(12), 1–14 (2021)

    Article  Google Scholar 

  19. Y. Miao, M. Zhao, J. Lin, Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition. ISA Trans. 84, 82–95 (2019)

    Article  Google Scholar 

  20. L. Wang, Z. Liu, Q. Miao et al., Complete ensemble local mean decomposition with adaptive noise and its application to fault diagnosis for rolling bearings. Mech. Syst. Signal Process. 106, 24–39 (2018)

    Article  Google Scholar 

  21. H. Li, T. Liu, X. Wu et al., A bearing fault diagnosis method based on enhanced singular value decomposition. IEEE Trans. Industr. Inf. 17(5), 3220–3230 (2020)

    Article  Google Scholar 

  22. B. Pang, G. Tang, Y. He et al., Weak fault diagnosis of rolling bearings based on singular spectrum decomposition, optimal Lucy-Richardson deconvolution and speed transform. Meas. Sci. Technol. 31(1), 015008 (2019)

    Article  Google Scholar 

  23. Y. Gao, M. Karimi, A.A. Kudreyko et al., Spare optimistic based on improved ADMM and the minimum entropy de-convolution for the early weak fault diagnosis of bearings in marine systems. ISA Trans. 78, 98–100 (2017)

    Article  Google Scholar 

  24. L. Zhang, B. Cai, G. Xiong, et al. Multistage fault feature extraction of consistent optimization for rolling bearings based on correlated kurtosis. Shock Vib. (2020)

  25. G.L. McDonald, Q. Zhao, Multipoint optimal minimum entropy deconvolution and convolution fix: application to vibration fault detection. Mech. Syst. Signal Process. 82, 461–477 (2017)

    Article  Google Scholar 

  26. Y. Lab, G. Ca, C. La, Research on bearing fault diagnosis based on spectrum characteristics under strong noise interference. Measurement. 169, 108509 (2020)

    Google Scholar 

  27. M. Buzzoni, J. Antoni, G. D’Elia, Blind deconvolution based on cyclostationarity maximization and its application to fault identification. J. Sound Vib. 432, 569–601 (2018)

    Article  Google Scholar 

  28. B. Zhang, Y. Miao, J. Lin et al., Adaptive maximum second-order cyclostationarity blind deconvolution and its application for locomotive bearing fault diagnosis. Mech. Syst. Signal Process. 158, 107736 (2021)

    Article  Google Scholar 

  29. Z. Wang, J. Zhou, W. Du et al., Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution. Mech. Syst. Signal Process. 162, 108018 (2022)

    Article  Google Scholar 

  30. R. Duan, Y. Liao, L. Yang et al., Minimum entropy morphological deconvolution and its application in bearing fault diagnosis. Measurement. 182, 109649 (2021)

    Article  Google Scholar 

  31. Y. Cheng, B. Chen, W. Zhang, Adaptive multipoint optimal minimum entropy deconvolution adjusted and application to fault diagnosis of rolling element bearings. IEEE Sens. J. 19(24), 12153–12164 (2019)

    Article  Google Scholar 

  32. D. Zhu, J. Chen, B. Yin, Fault feature extraction of rolling element bearing based on TPE-EVMD. Measurement. 183, 109880 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Danchen Zhu.

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

Zhu, D., Pan, Y. & Gao, W. Fault Feature Extraction of Rolling Element Bearing under Complex Transmission Path Based on Multiband Signals Cross-Correlation Spectrum. J Fail. Anal. and Preven. 22, 1164–1179 (2022). https://doi.org/10.1007/s11668-022-01406-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11668-022-01406-9

Keywords

Navigation