Skip to main content
SpringerLink
Log in

Application of Maximum Correlated Kurtosis Deconvolution on Rolling Element Bearing Fault Diagnosis

  • Conference paper
  • First Online:
Engineering Asset Management - Systems, Professional Practices and Certification

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

Maximum correlated kurtosis deconvolution (MCKD) searches for an optimal set of filter coefficients to enhance the periodic impulses by introducing correlation to kurtosis. This method can realize the feature extraction and the diagnosis of rolling element bearing’s faults by improving signal to noise ratio (SNR) of signal. In order to obtain a better result, how to select the important parameters of MCKD is discussed in this chapter. After selecting proper parameters, this method is applied to both simulated and experimental data. The result of simulated data shows that this method has potentials in fault diagnosis of rolling element bearing. The experimental data from an accelerated life test of rolling element bearing are used for validation, which shows that this method can successfully detect the incipient fault.

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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight 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

References

  1. Randall RB, Antoni J (2011) Rolling element bearing diagnostics—a tutorial. Mech Syst Signal Process 25(2):485–520

    Article  Google Scholar 

  2. Park C-S, Choi Y-C, Kim Y-H (2013) Early fault detection in automotive ball bearings using the minimum variance cepstrum. Mech Syst Signal Process 38(2):534–548

    Google Scholar 

  3. 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 

  4. Antoni J, Randall RB (2006) The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech Syst Signal Process 20(2):308–331

    Article  Google Scholar 

  5. Dong G, Chen J (2012) Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings. Mech Syst Signal Process 33:212–236

    Article  MathSciNet  Google Scholar 

  6. Ming Y, Chen J, Dong G (2011) Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum. Mech Syst Signal Process 25(5):1773–1785

    Article  Google Scholar 

  7. Cong F et al (2013) Short-time matrix series based singular value decomposition for rolling bearing fault diagnosis. Mech Syst Signal Process 34(1–2):218–230

    Article  Google Scholar 

  8. Li J, Chen X, He Z (2013) Adaptive stochastic resonance method for impact signal detection based on sliding window. Mech Syst Signal Process 36(2):240–255

    Article  MathSciNet  Google Scholar 

  9. Qiang L et al (2007) Engineering signal processing based on adaptive step-changed stochastic resonance. Mech Syst Signal Process 21(5):2267–2279

    Article  MathSciNet  Google Scholar 

  10. Wiggins RA (1978) Minimum entropy deconvolution. Geoexploration 16(1–2):21–35

    Article  Google Scholar 

  11. Endo H, Randall RB (2007) Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter. Mech Syst Signal Process 21(2):906–919

    Article  Google Scholar 

  12. Sawalhi N, Randall RB, Endo H (2007) The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mech Syst Signal Process 21(6):2616–2633

    Article  Google Scholar 

  13. McDonald GL, Zhao Q, Zuo MJ (2012) Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection. Mech Syst Signal Process 33:237–255

    Article  Google Scholar 

  14. McFadden PD, Smith JD (1984) Model for the vibration produced by a single point defect in a rolling element bearing. J Sound Vib 96(1):69–82

    Article  Google Scholar 

Download references

Acknowledgments

Support for this work from Natural Science Foundation of China (Approved Grant: 51035007 and 51105243) is gratefully acknowledged. The authors would also like to appreciate the support of Hangzhou Bearing Test and Research Center (HBRC) on the experiment.

Author information

Authors and Affiliations

  1. State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China

    Haitao Zhou, Jin Chen & Guangming Dong

Authors
  1. Haitao Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guangming Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haitao Zhou .

Editor information

Editors and Affiliations

  1. Systems Engineering, City University of Hong Kong, Hong Kong, China

    Peter W. Tse

  2. Asset Institute, Brisbane, Australia

    Joseph Mathew

  3. Community, Construction Professionals' Development Centre, Hong Kong, China

    King Wong

  4. Engineering Doctorate Society, Hong Kong, China

    Rocky Lam

  5. Kam Yuen (Group) International Limited, Hong Kong, China

    C.N. Ko

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhou, H., Chen, J., Dong, G. (2015). Application of Maximum Correlated Kurtosis Deconvolution on Rolling Element Bearing Fault Diagnosis. In: Tse, P., Mathew, J., Wong, K., Lam, R., Ko, C. (eds) Engineering Asset Management - Systems, Professional Practices and Certification. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-09507-3_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-09507-3_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09506-6

  • Online ISBN: 978-3-319-09507-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation