Journal of Failure Analysis and Prevention

, Volume 14, Issue 3, pp 363–371 | Cite as

Use of Spectral Kurtosis for Improving Signal to Noise Ratio of Acoustic Emission Signal from Defective Bearings

  • C. Ruiz-Cárcel
  • E. Hernani-Ros
  • Y. Cao
  • D. Mba
Technical Article---Peer-Reviewed


The use of acoustic emission (AE) to monitor the condition of roller bearings in rotating machinery is growing in popularity. This investigation is centered on the application of spectral kurtosis (SK) as a denoising tool able to enhance the bearing fault features from an AE signal. This methodology was applied to AE signals acquired from an experimental investigation where different size defects were seeded on a roller bearing. The results suggest that the signal to noise ratio can be significantly improved using SK.


Acoustic emission Signal to noise ratio Spectral kurtosis Roller bearings 



Financial support from the Marie Curie FP7-ITN project “Energy savings from smart operation of electrical, process and mechanical equipment-ENERGY-SMARTOPS,” Contract No: PITN-GA-2010-264940 is gratefully acknowledged.


  1. 1.
    G.K. Chaturvedi, D.W. Thomas, Bearing fault detection using adaptive noise cancelling. Trans. ASME J. Mech. Des 104(2), 280–289 (1982)CrossRefGoogle Scholar
  2. 2.
    D. Ho, R.B. Randall, Optimization of bearing diagnostic techniques using simulated and actual bearing fault signals. Mech. Syst. Signal Process. 14(5), 763–788 (2000)CrossRefGoogle Scholar
  3. 3.
    I. Khemili, M. Chouchane, Detection of rolling element bearing defects by adaptive filtering. Eur. J. Mech. A Solids 24(2), 293–303 (2005)CrossRefGoogle Scholar
  4. 4.
    V.N. Patel, N. Tandon, R.K. Pandey, Improving defect detection of rolling element bearings in the presence of external vibrations using adaptive noise cancellation and multiscale morphology. Proc. Inst. Mech. Eng. 226(2), 150–162 (2012)CrossRefGoogle Scholar
  5. 5.
    A. Moosavian, H. Ahmadi, A. Tabatabaeefar, Fault diagnosis of main engine journal bearing based on vibration analysis using Fisher linear discriminant, K-nearest neighbour and support vector machine. J. Vibrio Eng. 14(2), 894–906 (2012)Google Scholar
  6. 6.
    Y. Guo, T. Liu, J. Na, R. Fung, Envelope order tracking for fault detection in rolling element bearings. J. Sound Vib. 331(25), 5644–5654 (2012)Google Scholar
  7. 7.
    J. Antoni, The spectral kurtosis: a useful tool for characterising non-stationary signals. Mech. Syst. Signal Process. 20(2), 282–307 (2006)CrossRefGoogle Scholar
  8. 8.
    J. Antoni, R.B. Randall, The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech. Syst. Signal Process. 20(2), 308–331 (2006)CrossRefGoogle Scholar
  9. 9.
    F. Cong, J. Chen, G. Dong, Rolling bearing fault diagnosis based on spectral kurtosis in condition monitoring. COMADEM 2010Advances in Maintenance and Condition Diagnosis Technologies Towards Sustainable Society, Proceedings of 23rd International Congress Condition Monitoring and Diagnostic Engineering Management, (2010), p. 529Google Scholar
  10. 10.
    C.N. Komgom, N.W. Mureithi, A.A. Lakis, Application of time synchronous averaging, spectral kurtosis and support vector machines for bearing fault identification. Am. Soc. Mech. Eng. Press. Vessel. Pip. Div. (Publication) PVP 7, 137 (2008)Google Scholar
  11. 11.
    L. Shi, R.B. Randall, J. Antoni, Rolling element bearing fault detection using improved envelope analysis. IMechE Event Publ. 2, 301 (2004)Google Scholar
  12. 12.
    S. Al-Dossary, R.I.R. Hamzah, D. Mba, Observations of changes in acoustic emission waveform for varying seeded defect sizes in a rolling element bearing. Appl. Acoust. 70(1), 58–81 (2009)CrossRefGoogle Scholar
  13. 13.
    A.M. Al-Ghamd, D. Mba, A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mech. Syst. Signal Process. 20(7), 1537–1571 (2006)CrossRefGoogle Scholar
  14. 14.
    M. Behzad, A. AlandiHallaj, A.R. Bastami, B. Eftekharnejad, B. Charnley, D. Mba, Defect size estimation in rolling element bearings using vibration time waveform. Insight Non-Destr. Test. Cond. Monit. 51(8), 426–430 (2009)CrossRefGoogle Scholar
  15. 15.
    M. Elforjani, D. Mba, Accelerated natural fault diagnosis in slow speed bearings with acoustic emission. Eng. Fract. Mech. 77(1), 112–127 (2010)CrossRefGoogle Scholar
  16. 16.
    M. Elforjani, D. Mba, Detecting the onset, propagation and location of non-artificial defects in a slow rotating thrust bearing with acoustic emission. Insight Non-Destr. Test. Cond. Monit. 50(5), 264–268 (2008)CrossRefGoogle Scholar
  17. 17.
    M.W. Hawman, W.S. Galinaitis, Acoustic emission monitoring of rolling element bearings. Ultrasonics Symp. Proc. 2, 885 (1988)Google Scholar
  18. 18.
    B. Kilundu, X. Chiementin, J. Duez, D. Mba, Cyclostationarity of acoustic emissions (AE) for monitoring bearing defects. Mech. Syst. Signal Process. 25(6), 2061–2072 (2011)CrossRefGoogle Scholar
  19. 19.
    D. Mba, R.B.K.N. Rao, Development of acoustic emission technology for condition monitoring and diagnosis of rotating machines: bearings, pumps, gearboxes, engines, and rotating structures. Shock Vib. Digest 38(2), 3–16 (2006)CrossRefGoogle Scholar
  20. 20.
    J. Shiroishi, Y. Li, S. Liang, T. Kurfess, S. Danyluk, Bearing condition diagnostics via vibration and acoustic emission measurements. Mech. Syst. Signal Process. 11(5), 693–705 (1997)CrossRefGoogle Scholar
  21. 21.
    T. Yoshioka, A. Korenaga, H. Mano, T. Yamamoto, Diagnosis of rolling bearing by measuring time interval of AE generation. J. Tribol. 121(3), 468–472 (1999)CrossRefGoogle Scholar
  22. 22.
    K.R. Al-Balushi, A. Addali, B. Charnley, D. Mba, Energy index technique for detection of acoustic emissions associated with incipient bearing failures. Appl. Acoust. 71(9), 812–821 (2010)CrossRefGoogle Scholar
  23. 23.
    X. Chiementin, D. Mba, B. Charnley, S. Lignon, J.P. Dron, Effect of the denoising on acoustic emission signals. J. Vib. Acoust. Trans. ASME 132(3), 0310091–0310099 (2010)CrossRefGoogle Scholar
  24. 24.
    J. Couturier, D. Mba, Operational bearing parameters and acoustic emission generation. J. Vib. Acoust. ASME. 130(2), 024502 (2008)CrossRefGoogle Scholar
  25. 25.
    M. Elforjani, D. Mba, Condition monitoring of slow-speed shafts and bearings with acoustic emission. Strain 47(Suppl. 2), 350–363 (2011)CrossRefGoogle Scholar
  26. 26.
    M. Elforjani, D. Mba, Acoustic emissions observed from a naturally degrading slow speed bearing and shaft. COMADEM 2010Advances in Maintenance and Condition Diagnosis Technologies Towards Sustainable Society, Proceedings 23rd International Congress Condition Monitoring and Diagnostic Engineering Management, (2010), p. 355Google Scholar
  27. 27.
    M. Elforjani, D. Mba, Assessment of natural crack initiation and its propagation in slow speed bearings. Nondestr. Test. Eval. 24(3), 261–275 (2009)CrossRefGoogle Scholar
  28. 28.
    M. Elforjani, D. Mba, Monitoring the onset and propagation of natural degradation process in a slow speed rolling element bearing with acoustic emission. J. Vib. Acoust. Tran. ASME 130(4), 041013 (2008)CrossRefGoogle Scholar
  29. 29.
    M. Elforjani, D. Mba, Observations and location of acoustic emissions for a naturally degrading rolling element thrust bearing. J. Fail. Anal. Prev. 8(4), 370–385 (2008)CrossRefGoogle Scholar
  30. 30.
    D. Mba, The use of acoustic emission for estimation of bearing defect size. J. Fail. Anal. Prev. 8(2), 188–192 (2008)CrossRefGoogle Scholar
  31. 31.
    S.A. Mirhadizadeh, E.P. Moncholi, D. Mba, Influence of operational variables in a hydrodynamic bearing on the generation of acoustic emission. Tribol. Int. 43(9), 1760–1767 (2010)CrossRefGoogle Scholar
  32. 32.
    N. Tandon, B.C. Nakra, Comparison of vibration and acoustic measurement techniques for the condition monitoring of rolling element bearings. Tribol. Int. 25(3), 205–212 (1992)CrossRefGoogle Scholar
  33. 33.
    B. Eftekharnejad, M.R. Carrasco, B. Charnley, D. Mba, The application of spectral kurtosis on acoustic emission and vibrations from a defective bearing. Mech. Syst. Signal Process. 25(1), 266–284 (2011)CrossRefGoogle Scholar
  34. 34.
    J.R. Matthews, Acoustic emission (Gordon and Breach Science Publishers, New York, 1983), p. c1983Google Scholar
  35. 35.
    C.K. Tan, D. Mba, Limitation of acoustic emission for identifying seeded defects in gearboxes. J. Nondestr. Eval. 24(1), 11–28 (2005)CrossRefGoogle Scholar
  36. 36.
    J. Antoni, Fast computation of the kurtogram for the detection of transient faults. Mech. Syst. Signal Process. 21(1), 108–124 (2007)CrossRefGoogle Scholar
  37. 37.
    J. Antoni, Jérôme Antoni Personal Page, Available at: Accessed 24 July 2012
  38. 38.
    T. Williams, X. Ribadeneira, S. Billington, T. Kurfess, Rolling element bearing diagnostics in run-to-failure lifetime testing. Mech. Syst. Signal Process. 15(5), 979–993 (2001)CrossRefGoogle Scholar

Copyright information

© ASM International 2014

Authors and Affiliations

  • C. Ruiz-Cárcel
    • 1
  • E. Hernani-Ros
    • 1
  • Y. Cao
    • 1
  • D. Mba
    • 1
  1. 1.School of EngineeringCranfield UniversityCranfieldUK

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