SpringerLink
Log in

The MFBD: a novel weak features extraction method for rotating machinery

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

For the fault diagnosis of rotating machinery, the demodulation algorithm of the monitoring signals plays a key role in fault feature extraction. Especially for weak fault features extraction, existing single narrow band demodulation methods have worse performance under low signal to noise ratio condition. According to the mechanism of rotating machinery, both narrow and broad frequency band modulated signals exist simultaneously. Therefore, weak fault features can be obtained through demodulation of multiple narrow frequency bands rather than only one resonance narrow band. In this study, a novel weak feature extraction method is proposed based on used as a good filtermultiple frequency bands demodulation. The superiority of the proposed method is corroborated by simulation analysis and applications of a centrifugal pump and a propeller. By simulation analysis, the proposed multiple frequency bands demodulation (MFBD) method has better demodulation performance than Fast Kurtogram, Autogram and Fast-SC for weak modulation features. The applications results suggested that the proposed MFBD provided a clearer characteristic frequency identification than Fast Kurtogram, Autogram and Fast-SC, especially in weak modulation condition. Therefore, the proposed MFBD method provides a reliable basis for weak fault signal extraction, which shows good engineering significance for fault diagnosis of rotating machinery and passive detection of propeller.

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
EUR 32.99 /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

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

Similar content being viewed by others

References

  1. Moshrefzadeh A, Fasana A (2018) The Autogram: an effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis. Mech Syst Sign. Proc. 105:294–318

    Article  Google Scholar 

  2. An Xueli, Zeng Hongtao, Li Chaoshun (2016) “Demodulation analysis based on adaptive local iterative filtering for bearing fault diagnosis measurement. Journal of the International Measurement Confederation. 94:554–60

    Article  Google Scholar 

  3. Antoni J (2016) The infogram: entropic evidence of the signature of repetitive transients. Mech Syst Signal Process 74:73–94

    Article  Google Scholar 

  4. Antoni J (2017) Fast computation of the spectral correlation. Mech Syst Signal Process 92:248–277

    Article  Google Scholar 

  5. Antoni J (2006) The spectral kurtosis: a useful tool for characterising non-stationary signals. Mech Syst Signal Process 20(2):282–307

    Article  Google Scholar 

  6. Antoni J (2007) Cyclic spectral analysis in practice. Mech Syst Signal Process 21(2):597–630

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Antoni J (2009) Cyclostationarity by examples. Mech Syst Signal Process 23(4):987–1036

    Article  Google Scholar 

  9. Bianchi D, Mayrhofer E, Gröschl M, Betz G, Vernes A (2015) Wavelet packet transform for detection of single events in acoustic emission signals. Mech Syst Signal Process 64–65:441–451

    Article  Google Scholar 

  10. Boungou D, Guillet F, El Badaoui M, Lyonnet P, Rosario T (2015) Fatigue damage detection using Cyclostationarity. Mech Syst Signal Process 58–59:128–142

    Article  Google Scholar 

  11. Dwyer RF, Dwyer RF (1990) A technique for improving detection and estimation of signals contaminated by under ice noise a ). J Acoust Soc Am 124(1983):124–130

    Google Scholar 

  12. Elforjani M, Bechhoefer E (2018) Analysis of extremely modulated faulty wind turbine data using spectral kurtosis and signal intensity estimator. Renew. Energy 127:258–268

    Article  Google Scholar 

  13. Fuentes-garcía Marta, Maci Gabriel (2018) “Evaluation of diagnosis methods in pca-based multivariate statistical process control. Chemometr. Intell. Lab. Syst. 172:194–210

    Article  Google Scholar 

  14. Gardner WA, Napolitano A, Paura L (2006) Cyclostationarity: half a century of research. Sign. Proc. 86(4):639–697

    Article  Google Scholar 

  15. Lei Y, Lin J, He Z, Zi Y (2011) Application of an improved kurtogram method for fault diagnosis of rolling element bearings. Mech Syst Sign. Proc. 25(5):1738–1749

    Article  Google Scholar 

  16. Li S, Chu N, Yan P, Dazhuan Wu, Antoni J (2019) Cyclostationary approach to detect flow-induced effects on vibration signals from centrifugal pumps. Mech Syst Sign. Proc. 114:275–289

    Article  Google Scholar 

  17. Li W, Zhu Z, Jiang F, Zhou G, Chen G (2015) Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method. Mech Syst Sign. Proc. 50–51:414–426

    Article  Google Scholar 

  18. Li Y, Wang L, Guan J (2017) A spectrum detection approach for bearing fault signal based on spectral kurtosis. Shock Vib 2017:1–9

    Google Scholar 

  19. McNeill SI (2016) Decomposing a signal into short-time narrow-banded modes. J Sound Vib 373:325–339

    Article  Google Scholar 

  20. Song Y, Liu J, Chu N, Peng Wu, Dazhuan Wu (2019) A novel demodulation method for rotating machinery based on time-frequency analysis and principal component analysis. J Sound Vib 442:645–656

    Article  Google Scholar 

  21. Wang H, Chen J, Dong G (2014) Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform. Mech Syst Signal Process 48(1–2):103–119

    Article  Google Scholar 

  22. Wang X, Luo Z, Hongwei Hu, Mao H (2016) Extraction of weak crack signals based on sparse code shrinkage combined with wavelet packet filtering. Appl Acoust 112:53–58

    Article  Google Scholar 

  23. Wang Y, **ang J, Markert R, Liang M (2016) Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: a review with applications. Mech Syst Signal Process 66–67:679–698

    Article  Google Scholar 

  24. Wang Yi, Guanghua Xu, Liang L, Jiang K (2015) Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis. Mech Syst Sign. Proc. 54:259–276

    Article  Google Scholar 

  25. Yonghao Miao, Ming Zhao, **g Lin. 2017. Improvement of Kurtosis-Guided-Grams via Gini Index for Bearing Fault Feature Identification Feature Identification. Measurement Science and Technology.

Download references

Acknowledgements

The authors gratefully acknowledge the financial supports of the National Key R&D Plan of China (No. 2016YFF0203300). Authors would like to thank Professor Jéróme Antoni and Ali Moshrefzadeh for sharing the Fast Kurtogram, Autogram and Fast-SC codes publicly.

Author information

Authors and Affiliations

  1. School of Thermal Engineering, Shandong Jianzhu University, **an, 250101, Shandong, China

    Yongxing Song & Linhua Zhang

  2. Key Laboratory of High-Efficiency and Clean Mechanical Manufacture, National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, **an, 250061, Shandong, China

    **gting Liu

  3. State Key Laboratory of Fluid Power and Mechatronic Systems, Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou, 310027, China

    Dazhuan Wu

Authors
  1. Yongxing Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. **gting Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dazhuan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Linhua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongxing Song.

Additional information

Technical Editor: Wallace Moreira Bessa.

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

Song, Y., Liu, J., Wu, D. et al. The MFBD: a novel weak features extraction method for rotating machinery. J Braz. Soc. Mech. Sci. Eng. 43, 547 (2021). https://doi.org/10.1007/s40430-021-03259-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40430-021-03259-z

Keywords

Search

Navigation