Skip to main content
SpringerLink
Log in

Urban Rail Train Wheel Fault Diagnosis Based on Improved EEMD

  • Conference paper
  • First Online:
Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019 (EITRT 2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 639))

  • 605 Accesses

Abstract

In order to accurately identify the types of wheel faults in urban rail trains, a method based on improved ensemble empirical mode decomposition (EEMD) and Hilbert transform is proposed. The improved EEMD decomposition of the acquired vibration signal obtains several intrinsic mode functions (IMFs), and the Hilbert transform is performed on the IMF component containing the main information components, and judged the type of failure of the train wheels according to the fault characteristic frequency of the Hilbert spectrum. The experimental results show that the method can be used to identify the fault types of urban rail train wheels effectively and accurately.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Sharma SC, Harsha SP (2014) EPFM Analysis of subsurface crack beneath a wheel flat using dynamic condition. Procedia Mater Sci 6:43–60

    Article  Google Scholar 

  2. Zhang Q, Gao J, Dong H et al (2018) WPD and DE/BBO-RBFNN for solution of rolling bearing fault diagnosis. Neurocomputing 312:27–33

    Article  Google Scholar 

  3. Shao H, Jiang H, Wang F et al (2017) Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet. ISA Trans 69:187–201 (in Chinese)

    Article  Google Scholar 

  4. Shao R, Hu W, Wang Y et al (2014) The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform. Measurement 54(6):118–132 (in Chinese)

    Article  Google Scholar 

  5. Liu Z, He Z, Guo W et al (2016) A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery. ISA Trans 61:211–220

    Article  Google Scholar 

  6. Pachori RB, Nishad A (2016) Cross-terms reduction in the Wigner-Ville distribution using tunable-Q wavelet transform. Sig Process 120:288–304 (in Chinese)

    Article  Google Scholar 

  7. Liu Y, Zhang J, Ma L (2016) A fault diagnosis approach for diesel engines based on self-adaptive WVD, improved FCBF and PECOC-RVM. Neurocomputing 177:600–611

    Article  Google Scholar 

  8. Wang L, Liu Z, Miao Q et al (2018) Time-frequency analysis based on ensemble local mean decomposition and fast Kurtogram for rotating machinery fault diagnosis. Mech Syst Sig Process 103:60–75

    Article  Google Scholar 

  9. Kopsinis Y, McLaughlin S (2009) Development of EMD-based denoising methods inspired by wavelet thresholding. IEEE Trans Sig Process 57:1351–1362

    Article  MathSciNet  Google Scholar 

  10. Wu Z, Huang NE et al (2009) Ensemble empirical mode decomposition: a noise assisted data analysis method. Adv Adaptive Data Anal 1:1–41 (in Chinese)

    Article  Google Scholar 

  11. Cheng G, Chen X, Li H et al (2016) Study on planetary gear fault diagnosis based on entropy feature fusion of ensemble empirical mode decomposition. Measurement 91:140–154

    Article  Google Scholar 

  12. 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 Sig Process 48:103–119

    Article  Google Scholar 

  13. Žvokelj M, Zupan S, Prebil I (2016) EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis. J Sound Vibr 370:394–423

    Article  Google Scholar 

  14. Huang NE, Wu Z (2009) A review on Hilbert–Huang transform: method and its applications to geophysical studies. Adv Adaptive Data Anal 1:1–23

    Article  Google Scholar 

  15. Cheng Y, Zhang Z, Zhang H, Qi Y (2018) Application of improved EMD algorithm in post-explosion shock wave processing. Electron Meas Technol (23) (in Chinese)

    Google Scholar 

  16. Shannon CEA (2001) A mathematical theory of communication. Acm Sigmobile Mob Comput Commun Rev 5(1):3–55

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work is supported by National Key R&D Program of China (2016YFB1200401).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Ning Fu & Kaijie Qian

  2. School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, China

    Zongyi Xing

Authors
  1. Ning Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kaijie Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zongyi Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zongyi Xing .

Editor information

Editors and Affiliations

  1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China

    Yong Qin

  2. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China

    Limin Jia

  3. National Innovation Center of High Speed Train, Qingdao, China

    Baoming Liu

  4. Beijing Jiaotong University, Beijing, China

    Zhigang Liu

  5. Beijing Jiaotong University, Beijing, China

    Lijun Diao

  6. School of Science, Engineering and Environment, University of Salford, Salford, UK

    Min An

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fu, N., Qian, K., Xing, Z. (2020). Urban Rail Train Wheel Fault Diagnosis Based on Improved EEMD. In: Qin, Y., Jia, L., Liu, B., Liu, Z., Diao, L., An, M. (eds) Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019. EITRT 2019. Lecture Notes in Electrical Engineering, vol 639. Springer, Singapore. https://doi.org/10.1007/978-981-15-2866-8_14

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-2866-8_14

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2865-1

  • Online ISBN: 978-981-15-2866-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation