Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

The FERgram: A rolling bearing compound fault diagnosis based on maximal overlap discrete wavelet packet transform and fault energy ratio

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

Abstract

Compound fault features of the rolling bearing are difficult to separate and extract. To address this problem, the present paper proposed a diagnosis algorithm, namely FERgram, on the base of maximal overlap discrete wavelet packet transform (MODWPT) and fault energy ratio (FER). First, a group of frequency band signals are gained after MODWPT processing the initial vibration signal. Second, FER is chosen as the evaluation index, and then the FER values of each frequency band signal are calculated and used to generate FERgram. The frequency band signal with the maximum FER value containing plentiful fault information is chosen for envelope analysis. Finally, the fault type is determined by contrasting the prominent frequency component of the envelope spectrum with the fault feature frequency. The feasibility and superiority of the FERgram method are verified by four signals and four comparison methods. The results show that the FERgram method can effectively extract and accurately diagnose the compound fault of rolling bearing.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /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 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. M. N. Yasir and B. Koh, Data decomposition techniques with multi-scale permutation entropy calculations for bearing fault diagnosis, Sensors, 18 (4) (2018) 1278.

    Article  Google Scholar 

  2. H. D. Yuan, J. Chen and G. M. Dong, An improved initialization method of D-KSVD algorithm for bearing fault diagnosis, Journal of Mechanical Science & Technology, 31 (11) (2017) 5161–5172.

    Google Scholar 

  3. Y. J. Li, W. H. Zhang, Q. Xiong, D. B. Luo, G. M. Mei and T. Zhang, A rolling bearing fault diagnosis strategy based on improved multiscale permutation entropy and least squares SVM, Journal of Mechanical Science & Technology, 31 (6) (2017) 2711–2722.

    Article  Google Scholar 

  4. A. B. Ming, W. Zhang, Z. Y. Qin and F. L. Chu, Envelope calculation of the multi-component signal and its application to the deterministic component cancellation in bearing fault diagnosis, Mechanical Systems & Signal Processing, 50–51 (2015) 70–100.

    Article  Google Scholar 

  5. R. Peled, S. Braun and M. Zacksenhouse, A blind deconvolution separation of multiple sources, with application to bearing diagnostics, Mechanical Systems & Signal Process-ing, 19 (6) (2005) 1181–1195.

    Article  Google Scholar 

  6. H. Q. Wang, R. T. Li, G. Tang, H. F. Yuan, Q. L. Zhao and X. Cao, A compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition, PLoS ONE, 9 (10) (2014) e109166.

    Google Scholar 

  7. L. L. Cui, C. G. Wu, C. Q. Ma and H. Q. Wang, Diagnosis of roller bearings compound fault using underdetermined blind source separation algorithm based on null-space pursuit, Shock and Vibration, 2015 (2015) 1–8.

    Article  Google Scholar 

  8. A. B. Ming, F. L. Chu and W. Zhang, Compound fault features separation of rolling element bearing based on the wavelet decomposition and spectrum auto-correlation, Journal of Mechanical Engineering, 49 (3) (2013) 80–87.

    Article  Google Scholar 

  9. H. Li, H. Q. Zheng and L. W. Tang, Bearing multi-fault diagnosis based on improved morphological component analysis, Journal of Vibration & Shock, 31 (12) (2012) 135–140.

    Google Scholar 

  10. J. Antoni, The spectral kurtosis: A useful tool for characterising non-stationary signals, Mechanical Systems & Signal Processing, 20 (2) (2006) 282–307.

    Article  Google Scholar 

  11. J. Antoni and R. B. Randall, The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines, Mechanical Systems & Signal Processing, 20 (2) (2006) 308–331.

    Article  Google Scholar 

  12. J. Antoni, Fast computation of the kurtogram for the detection of transient faults, Mechanical Systems & Signal Processing, 21 (1) (2007) 108–124.

    Article  Google Scholar 

  13. Y. G. Lei, J. Lin, Z. J. He and Y. Y. Zi, Application of an improved kurtogram method for fault diagnosis of rolling element bearings, Mechanical Systems & Signal Processing, 25 (5) (2011) 1738–1749.

    Article  Google Scholar 

  14. D. Wang, P. W. Tse and K. L. Tsui, An enhanced Kurtogram method for fault diagnosis of rolling element bearings, Mechanical Systems & Signal Processing, 35 (1–2) (2013) 176–199.

    Article  Google Scholar 

  15. P. W. Tse and D. Wang, The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as “Two automatic vibration- based fault diagnostic methods using the novel sparsity measurement—Parts 1 and 2”, Mechanical Systems & Signal Processing, 40 (2) (2013) 499–519.

    Article  Google Scholar 

  16. P. W. Tse and D. Wang, The automatic selection of an optimal wavelet filter and its enhancement by the new sparsogram for bearing fault detection: Part 2 of the two related manuscripts that have a joint title as “Two automatic vibration- based fault diagnostic methods using the novel sparsity measurement—Parts 1 and 2”, Mechanical Systems & Signal Processing, 40 (2) (2013) 520–544.

    Article  Google Scholar 

  17. T. Barszcz and A. JabLonski, A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram, Mechanical Systems & Signal Processing, 25 (1) (2011) 431–451.

    Article  Google Scholar 

  18. B. Q. Chen, Z. S. Zhang, Y. Y. Zi, Z. J. He and C. Sun, Detecting of transient vibration signatures using an improved fast spatial-spectral ensemble kurtosis kurtogram and its applications to mechanical signature analysis of short duration data from rotating machinery, Mechanical Systems & Signal Processing, 40 (1) (2013) 1–37.

    Article  Google Scholar 

  19. G. Yu, C. N. Li and J. F. Zhang, A new statistical modeling and detection method for rolling element bearing faults based on alpha-stable distribution, Mechanical Systems & Signal Processing, 41 (1–2) (2013) 155–175.

    Article  Google Scholar 

  20. P. Borghesani, P. Pennacchi and S. Chatterton, The relationship between kurtosis- and envelope-based indexes for the diagnostic of rolling element bearings, Mechanical Systems & Signal Processing, 43 (1–2) (2014) 25–43.

    Article  Google Scholar 

  21. J. Antoni, The infogram: Entropic evidence of the signature of repetitive transients, Mechanical Systems & Signal Processing, 74 (2016) 73–94.

    Article  Google Scholar 

  22. S. T. Wan, X. Zhang and L. J. Dou, Shannon entropy of binary wavelet packet subbands and its application in bearing fault extraction, Entropy, 20 (4) (2018) 260.

    Article  Google Scholar 

  23. S. T. Wan and X. Zhang, Teager energy entropy ratio of wavelet packet transform and its application in bearing fault diagnosis, Entropy, 20 (5) (2018) 388.

    Article  Google Scholar 

  24. A. T. Walden, Wavelet analysis of discrete time series, European Congress of Mathematics (2001) 627–641.

    MATH  Google Scholar 

  25. L. Shen, X. J. Zhou, W. B. Zhang and Z. G. Zhang, Application of morphological demodulation in gear fault feature extraction, Journal of Zhejiang University, 44 (8) (2010) 1514–1518.

    Google Scholar 

  26. T. J. Wang, Z. P. Feng, R. J. Hao and F. L Chu, Fault diagnosis of rolling element bearings based on teager energy operator, Journal of Vibration & Shock, 31 (2) (2012) 1–5, 85.

    Google Scholar 

  27. R. B. Randall and J. Antoni, Rolling element bearing diagnostics— A tutorial, Mechanical Systems & Signal Processing, 25 (2) (2011) 485–520.

    Article  Google Scholar 

  28. H. B. Hong and M. Liang, Separation of fault features from a single-channel mechanical signal mixture using wavelet decomposition, Mechanical Systems & Signal Processing, 21 (5) (2007) 2025–2040.

    Article  Google Scholar 

  29. Bearing data center, Case Western Reserve University, https://doi.org/csegroups.case.edu/bearingdatacenter/home.

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, North China Electric Power University, Baoding, 071003, China

    Shuting Wan & Bo Peng

Authors
  1. Shuting Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bo Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Peng.

Additional information

Recommended by Associate Editor Sungsoo Na

Shuting Wan is a Professor at the School of Mechanical Engineering in North China Electric Power University. He obtained his Ph.D. from the North China Electric Power University, China, in 2006. His research interests include condition monitoring and fault diagnosis of power equipment.

Bo Peng is a Ph.D. student at the School of Mechanical Engineering in North China Electric Power University. His research interest is condition monitoring and fault diagnosis of power equipment.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wan, S., Peng, B. The FERgram: A rolling bearing compound fault diagnosis based on maximal overlap discrete wavelet packet transform and fault energy ratio. J Mech Sci Technol 33, 157–172 (2019). https://doi.org/10.1007/s12206-018-1216-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-018-1216-3

Keywords

Search

Navigation