Journal of Failure Analysis and Prevention

, Volume 14, Issue 5, pp 645–656 | Cite as

A Comparative Study of Various Methods of Gear Faults Diagnosis

  • Leila Nacib
  • Salah Saad
  • Saadi Sakhara
Technical Article---Peer-Reviewed


Investigating gear damages using vibration signal is a subject of a high interest, because gears vibration signals are complex and difficult to understand. A failure diagnosis of gearbox based on Fourier analysis of the vibration produced by speed reducers has shown its limits in terms of spectral resolution. In the present paper, a comparative study of the performances of various different methods of fault diagnosis of helicopter gearbox gear is carried out. The results are highlighted on the basis of real data recorded during a helicopter flight and have showed that cepstral analysis is most effective technique in detecting gearbox gear faults.


Time domain analysis Fast Fourier transforms (FFT) Amplitude modulation analysis Cepstral analysis Synchronous averaging technique Helicopter gears 



The authors gratefully acknowledge LAGIS: Laboratory of Computer Engineering and Signal, Lille University France for their technical support and for providing the facilities to conduct this work.


  1. 1.
    M. SidAhmed, Early detection of defects in gears by vibration analysis. Bulletin S.F.M Revue Francaice de Mecanique 4, 243–254 (1990)Google Scholar
  2. 2.
    G. Diwakar, Detection of gear fault using vibration analysis. Int. J. Emerg. Technol. Adv. Eng. 2(9), 2250–2459 (2012)Google Scholar
  3. 3.
    A.S. Sait, Y.I. Sharaf-Eldeen, A review of gearbox condition monitoring based on vibration analysis techniques diagnostics and prognostics (Conference Paper), in Conference Proceedings of the Society for Experimental Mechanics Series 5, 2011, pp. 307–324Google Scholar
  4. 4.
    A. Aherwar, S. Khalid, Vibration analysis techniques for gearbox diagnostic: a review. Int. J. Adv. Eng. Technol. E. 1(3), 101–113 (2012)Google Scholar
  5. 5.
    Z. Ghemari, S. Saad, Development of measurement precision of sensor vibration. J. Vib. Control. Published online before print May 30, 2012Google Scholar
  6. 6.
    P.D. Samuel, D.J. Pines, D.G. Lewicki, Comparison of stationary and non-stationary metrics for detecting faults in helicopter gearboxes. J. Am Helicopter Soc. 45(2), 125–136 (2000)CrossRefGoogle Scholar
  7. 7.
    D.R. Brillinger, John W. Tukey’s work on time series and spectrum analysis. Ann. Stat. 30(6), 1595–1618 (2002)CrossRefGoogle Scholar
  8. 8.
    Xiaohong. Yuan, Lilong. Cai, Variable amplitude Fourier series with its application in gearbox diagnosis—part I: principle and simulation. Mech. Syst. Signal Process. 19, 1055–1066 (2005)CrossRefGoogle Scholar
  9. 9.
    S.N. Engin, K. Gülez, Badi. Mr, Advanced signal processing techniques for faults diagnostics—a review. Math. Comput. Appl. 4(2), 121–136 (1999)Google Scholar
  10. 10.
    X. Fan, J.Z. Ming, Gearbox fault detection using hilbert and wavelet packet transform. Mech. Syst. Signal Process. 20(4), 966–982 (2006)CrossRefGoogle Scholar
  11. 11.
    H. Zheng, Z. Li, X. Chen, Gear fault diagnosis based on continuous wavelet transform. Mech. Syst. Signal Process. 16(2–3), 447 (2002)CrossRefGoogle Scholar
  12. 12.
    J. Lin, M.J. Zuo, Gear box fault diagnosis using adaptive wavelet filter. Mech. Syst. Signal Process. 17(6), 1259–1269 (2003)CrossRefGoogle Scholar
  13. 13.
    H. Firpi, G. Vachtsevanos, Genetically programmed-based artificial features extraction applied to fault detection. Eng. Appl. Artif. Intell. 21, 558–568 (2008)CrossRefGoogle Scholar
  14. 14.
    R. Ricci, P. Pennacchi, Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions. Mech. Syst. Signal Process. 25, 821–838 (2011)CrossRefGoogle Scholar
  15. 15.
    F.A. Galati, B.D. Forrester, S. Dey, Application of the generalized likelihood ratio algorithm to the detection of a bearing fault in a helicopter transmission (Conference Paper), in Proceedings of the 1st World Congress on Engineering Asset Management, WCEAM 2006, 2006, pp. 400–405Google Scholar
  16. 16.
    Y. Ai, Y. Wang, W. Liu, The application of the diagnoses technique of vibration on the failure analysis of gear and bearing in gearbox (Conference Paper). Appl. Mech. Mater. 86, 143–147 (2011)CrossRefGoogle Scholar
  17. 17.
    Y. Lei, M.J. Zuo, Gear crack level identification based on weighted K nearest neighbor classification algorithm. Mech. Syst. Signal Process. 23(5), 1535–1547 (2009)CrossRefGoogle Scholar
  18. 18.
    X. Zhao, M.J. Zuo, Z. Liu, M.R. Hoseini, Diagnosis of artificially created surface damage levels of planet gear teeth using ordinal ranking. Measurement 46(1), 132–144 (2013)CrossRefGoogle Scholar
  19. 19.
    R.B. Randall, Detection and diagnosis of incipient bearing failure in helicopter gearboxes. Eng. Fail. Anal. 11, 177–190 (2004)CrossRefGoogle Scholar
  20. 20.
    V. Girondin, Vibration-based fault detection of sharp bearing faults in helicopters (SAFEPROCESS, Mexico, 2012)Google Scholar
  21. 21.
    N.W. Bolander, C. Baker, Remaining useful life prediction of helicopter gearbox bearings via vibration diagnostics and physics-based prognostic modeling (Conference Paper), Failure Prevention: Implementation, Success Stories and Lessons Learned, in Proceedings of the 2009 Conference of the Society for Machinery Failure Prevention Technology, 2009, 43 pGoogle Scholar
  22. 22.
    P.D. Samuel, D.J. Pines, A review of vibration-based techniques for helicopter transmission diagnostics (Review). J. Sound Vib. 282(1–2), 475–508 (2005)CrossRefGoogle Scholar
  23. 23.
    P.D. Samuel, D.J. Pines, Constrained adaptive lifting and the CAL4 metric for helicopter transmission diagnostics. J. Sound Vib. 319(1–2), 698–718 (2009)CrossRefGoogle Scholar
  24. 24.
    A. Hood, D. Pines, Sun gear fault detection on an OH-58C helicopter transmission (Conference Paper). Annu. Forum Proc. AHS Int. 3, 1664–1690 (2011)Google Scholar
  25. 25.
    J. Liu, S. Wang, Y. Wang, Helicopter transmission system technology readiness assessment (Conference Paper). Appl. Mech. Mater. 86, 389–393 (2011)CrossRefGoogle Scholar
  26. 26.
    Yanxue. Wang, Zhengjia. He, Jiawei. Xiang, Yanyang. Zi, Application of local mean decomposition to the surveillance and diagnostics of low-speed helical gearbox. Mech. Mach. Theory 47, 62–73 (2012)CrossRefGoogle Scholar
  27. 27.
    C. Byington, M. Watson, H. Lee, M. Hollins, Sensor-level fusion to enhance health and usage monitoring systems (Conference Paper). Annu. Forum Proc. AHS Int. 2, 1476–1485 (2008)Google Scholar
  28. 28.
    B. Samanta, C. Nataraj, Prognostics of machine condition using energy based monitoring index and computational intelligence. J. Comput. Inf. Sci. Eng. 9(4), 1–6 (2009)CrossRefGoogle Scholar
  29. 29.
    N. Haloui, D. Chikouche, M. Benidir, Diagnosis of gear systems by spectral analysis of vibration signals. Int. J. Comput. Sci. Netw. Secur. 7(10), 285–293 (2007)Google Scholar
  30. 30.
    M. El-Badaoui, Contribution to the diagnosis of vibration reducing complex gear by cepstral analysis, PhD thesis, 1999Google Scholar
  31. 31.
    A. Kraker, Cepstrum analysis as a useful supplement to spectrum analysis for monitoring gear-box experimental stress analysis, in Proceedings of the 8th International Conference, Amsterdam, Netherlands, 1986, May 12–16, pp 181–190Google Scholar
  32. 32.
    P.D. McFadden, Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration. J. Vib. Acoust. Stress Reliab. Des. 108, 165–170 (1986)CrossRefGoogle Scholar
  33. 33.
    V.V. Polyshchuk et al., Gear fault detection with time frequency based parameter NP4. Int. J. Rotat. Mach. 8(1), 30–57 (2002)CrossRefGoogle Scholar
  34. 34.
    B.D. Forrester, Analysis of gear vibration in the time-frequency domain, in Current practices and trends in mechanical failure prevention, ed. by H.C. Pussy, S.C. Pussy (Willow Brook, Vibration Institute, 1990), pp. 225–234Google Scholar
  35. 35.
    B.D. Forrester, Advanced vibration analysis techniques for fault detection and diagnosis in geared transmission systems, Ph.D thesis, Swinburne University of technology, Australia, 1996Google Scholar
  36. 36.
    H. Endo, R.B. Randall, C. Gosselin, Differential diagnosis of pall vs. cracks in the gear tooth fillet region: experimental validation. Mech. Syst. Signal Process. 23, 636–651 (2009)CrossRefGoogle Scholar
  37. 37.
    L. Nacib et al., Detecting shaft misalignment in gearbox of helicopter using average synchronous analysis. Int. J. Eng. Technol. 12(06), 125 (2012)Google Scholar
  38. 38.
    L. Nacib et al., Detecting gear tooth cracks using cepstral analysis in gearbox of helicopters. Int. J. Adv. Eng. Technol. 5(2), 139–145 (2013)Google Scholar

Copyright information

© ASM International 2014

Authors and Affiliations

  1. 1.LAGIS: Laboratoire de Génie Informatique et SignalUniversité Lille 1LilleFrance
  2. 2.LSELM: Laboratoire des Systèmes ElectromécaniquesBadji-Mokhtar Annaba UniversityAnnabaAlgeria

Personalised recommendations