Journal of Failure Analysis and Prevention

, Volume 14, Issue 5, pp 623–636 | Cite as

A Comparative Study of the Effectiveness of Adaptive Filter Algorithms, Spectral Kurtosis and Linear Prediction in Detection of a Naturally Degraded Bearing in a Gearbox

  • Faris Elasha
  • Cristobal Ruiz-Carcel
  • David Mba
  • Pramesh Chandra
Technical Article---Peer-Reviewed

Abstract

Diagnosing bearing faults at the earliest stages is critical in avoiding future catastrophic failures. Many techniques have been developed and applied in diagnosing bearings faults; however, these traditional diagnostic techniques are not always successful when the bearing fault occurs in gearboxes where the vibration response is complex; under such circumstances, it may be necessary to separate the bearing signal from the complex signal. In this paper, an adaptive filter has been applied for the purpose of bearing signal separation. Four algorithms were compared to assess their effectiveness in diagnosing a bearing defect in a gearbox, least mean square (LMS), linear prediction, spectral kurtosis and fast block LMS. These algorithms were applied to decompose the measured vibration signal into deterministic and random parts with the latter containing the bearing signal. These techniques were applied to identify a bearing fault in a gearbox employed for an aircraft control system for which endurance tests were performed. The results show that the LMS algorithm is capable of detecting the bearing fault earlier in comparison with the other algorithms.

Keywords

Vibration Adaptive filter Signal separation Bearing diagnostics Gearbox 

References

  1. 1.
    R.B. Randall, J. Antoni, Rolling element bearing diagnostics A tutorial. Mech. Syst. Signal Process. 25(2), 485–520 (2011)CrossRefGoogle Scholar
  2. 2.
    E. B. Halim, S.L. Shah, M.J. Zuo, M.A.A. Choudhury, Fault detection of gearbox from vibration signals using time-frequency domain averaging. American Control Conference, 2006, 6 ppGoogle Scholar
  3. 3.
    C.K. Tan, P. Irving, D. Mba, A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears. Mech. Syst. Signal Process. 21(1), 208–233 (2007)CrossRefGoogle Scholar
  4. 4.
    M. Behzad, A.R. Bastami, D. Mba, A new model for estimating vibrations generated in the defective rolling element bearings. J. Vib. Acoust. Trans. ASME. 133(4), 041011 (2011)CrossRefGoogle Scholar
  5. 5.
    Z. Li, X. Yan, Z. Tian, C. Yuan, Z. Peng, L. Li, Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis. Measurement 46(1), 259–271 (2013)CrossRefGoogle Scholar
  6. 6.
    R.B. Randall, B. Tech, Cepstrum Analysis and Gearbox Fault Diagnosis, [Online], no. 2 edn, (2004). www.bksv.com/doc/233-80.pdf. Accessed 10 July 2012
  7. 7.
    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
  8. 8.
    P.D. McFadden, M.M. Toozhy, Application of synchronous averaging to vibration monitoring of rolling elements bearings. Mech. Syst. Signal Process. 14(6), 891–906 (2000)CrossRefGoogle Scholar
  9. 9.
    Z. Fu-Cheng, Research on online monitoring and diagnosis of bearing fault of wind turbine gearbox based on undecimated wavelet transformation. 2010 IEEE Youth Conference on Information Computing and Telecommunications (YC-ICT) (2010), pp. 251Google Scholar
  10. 10.
    I. Howard, A Review of Rolling Element Bearing Vibration Detection, Diagnosis and Prognosis. DSTO-RR-0013, Department of Defense, 1994Google Scholar
  11. 11.
    W.J. Wang, P.D. McFadden, Early detection of gear failure by vibration analysis I. Calculation of the time-frequency distribution. Mech. Syst. Signal Process. 7(3), 193–203 (1993)CrossRefGoogle Scholar
  12. 12.
    N. Sawalhi, R.B. Randall, H. Endo, 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 (2007)CrossRefGoogle Scholar
  13. 13.
    R.B. Randall, Detection and diagnosis of incipient bearing failure in helicopter gearboxes. Eng. Fail. Anal. 11(2), 177–190 (2004)CrossRefGoogle Scholar
  14. 14.
    P.D. McFadden, J.D. Smith, Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review. Tribol. Int. 17(1), 3–10 (1984)CrossRefGoogle Scholar
  15. 15.
    P.D. McFadden, A revised model for the extraction of periodic waveforms by time domain averaging. Mech. Syst. Signal Process. 1(1), 83–95 (1987)CrossRefGoogle Scholar
  16. 16.
    R.B. Randall, N. Sawalhi, M. Coats, A comparison of methods for separation of deterministic and random signals. Int. J. Cond. Monit. 1(1), 11 (2011)CrossRefGoogle Scholar
  17. 17.
    T. Barszcz, Decomposition of vibration signals into deterministic and nondeterministic components and its capabilities of fault detection and identification. Int. J. Appl. Math. Comput. Sci. 19(2), 327–335 (2009)CrossRefGoogle Scholar
  18. 18.
    M.S. Carney, J.A. Mann III, J. Gagliardi, Adaptive filtering of sound pressure signals for monitoring machinery in noisy environments. Appl. Acoust. 43(4), 333–351 (1994)CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    J. Antoni, R.B. Randall, Unsupervised noise cancellation for vibration signals: part I—evaluation of adaptive algorithms. Mech. Syst. Signal Process. 18(1), 89–101 (2004)CrossRefGoogle Scholar
  21. 21.
    E.H. Satorius, J.R. Zeidler, S.T. Alexander, Noise cancellation via linear prediction filtering. IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP ‘79. vol 4, (1979), pp. 937Google Scholar
  22. 22.
    N.V. Thakor, Y. Zhu, Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 38(8), 785–794 (1991)CrossRefGoogle Scholar
  23. 23.
    G.K. Chaturved, D.W. Thomas, Adaptive noise cancelling and condition monitoring. J. Sound Vib. 76(3), 391–405 (1981)CrossRefGoogle Scholar
  24. 24.
    D. Ho, R.B. Randall, Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signal. Mech. Syst. Signal Process. 14(5), 763–788 (2000)CrossRefGoogle Scholar
  25. 25.
    J. Antoni, R.B. Randall, Optimisation of SANC for Separating gear and bearing signals. Cond. Monit. Diagn. Eng. Manag. 1, 89–99 (2001)Google Scholar
  26. 26.
    B. Widrow, J.R. Glover Jr, J.M. McCool, J. Kaunitz, C.S. Williams, R.H. Hearn, J.R. Zeidler, J. Eugene Dong, R.C. Goodlin, Adaptive noise cancelling: principles and applications. Proc. IEEE 63(12), 1692–1716 (1975)CrossRefGoogle Scholar
  27. 27.
    H. Simon, Adaptive Filter Theory, 2nd edn. (Prentice-Hall International Inc, Englewood Cliffs, 1991)Google Scholar
  28. 28.
    J. Antoni, R. 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
  29. 29.
    R. Dwyer, Detection of non-Gaussian signals by frequency domain kurtosis estimation. IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP’83. vol. 8 (1983), pp. 607Google Scholar
  30. 30.
    S.C. Douglas, Introduction to Adaptive Filters (CRC Press, Boca Raton, 1999)Google Scholar
  31. 31.
    S.C. Douglas, M. Rupp, Convergence issues in the LMS adaptive filter, in The Digital Signal Processing Handbook, 2nd edn., ed. by V.K. Madisetti (CRC Press, Atlanta, 1999)Google Scholar
  32. 32.
    B. Widrow, J. McCool, M. Ball, The complex LMS algorithm. Proc. IEEE 63(4), 719–720 (1975)CrossRefGoogle Scholar
  33. 33.
    W.A. Gardner, Stationarizable random processes. IEEE Trans. Inf. Theory 24(1), 8–22 (1978)CrossRefGoogle Scholar
  34. 34.
    W.A. Gardner, L. Franks, Characterization of cyclostationary random signal processes. IEEE Trans. Infor. Theory 21(1), 4–14 (1975)CrossRefGoogle Scholar
  35. 35.
    J. Makhoul, Linear prediction: a tutorial review. Proc. IEEE 63(4), 561–580 (1975)CrossRefGoogle Scholar
  36. 36.
    G.U. Yule, On a method of investigating periodicities in disturbed series, with special reference to Wolfer’s sunspot numbers. Philos. Trans. R. Soc. Lond. 226, 267–298 (1927)CrossRefGoogle Scholar
  37. 37.
    R.B. Randall, Vibration-Based Condition Monitoring, 1st edn. (Wiley, Chichester, 2011)CrossRefGoogle Scholar
  38. 38.
    W. Wang, Autoregressive model-based diagnostics for gears and bearings. Insight Non Destr. Test. Cond. Monit. 50(8), 414–418 (2008)CrossRefGoogle Scholar
  39. 39.
    L. Ljung, in System Identification: Theory for the User, 2nd edn, (Pentrice-Hall, New Jersey, 1999), pp. 321–324Google Scholar
  40. 40.
    M. Dentino, J. McCool, B. Widrow, Adaptive filtering in the frequency domain. Proc. IEEE 66(12), 1658–1659 (1978)CrossRefGoogle Scholar
  41. 41.
    E.R. Ferrara, Fast implementations of LMS adaptive filters. IEEE Trans. Acoust. Speech Signal Process. 28(4), 474–475 (1980)CrossRefGoogle Scholar
  42. 42.
    J. Antoni, Fast computation of the kurtogram for the detection of transient faults. Mech. Syst. Signal Process. 21(1), 108–124 (2007)CrossRefGoogle Scholar
  43. 43.
    H. Li, Y. Zhang, H. Zheng, Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network. J. Mech. Sci. Technol. 23(10), 2780–2789 (2009)CrossRefGoogle Scholar

Copyright information

© ASM International 2014

Authors and Affiliations

  • Faris Elasha
    • 1
  • Cristobal Ruiz-Carcel
    • 1
  • David Mba
    • 1
  • Pramesh Chandra
    • 2
  1. 1.School of EngineeringCranfield UniversityBedfordUK
  2. 2.Moog Aircraft GroupWolverhamptonUK

Personalised recommendations