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Meccanica

, Volume 48, Issue 6, pp 1399–1413 | Cite as

Analysis of gear rattle by means of a wavelet-based signal processing procedure

  • Renato Brancati
  • Ernesto Rocca
  • Sergio Savino
  • Flavio Farroni
Article

Abstract

In the paper a wavelet-based signals processing technique for the experimental detection of the gear rattle produced by the teeth impacts in lightly loaded gears is proposed.

The Discrete Wavelet Transform is used to decompose the signal of the angular relative motion of an helical gear pair, and the wavelet decomposition details are adopted to analyze the dynamic behavior under rattle condition. In particular the procedure permits to evaluate the quality of the impacts between the teeth, discriminating between the two different sides of teeth contacts when there is a double-sided rattle condition. This technique enables, moreover, to define new indices for metrics of gear rattle especially useful in order to conduct comparisons for different operative conditions.

Some examples of application of the proposed technique are reported in the paper adopting experimental signals acquired by two high resolution incremental encoders on a specific gear pair test rig. The experimental investigations regard comparative analyses with respect to the speed fluctuations amplitude, the rattle frequency and the lubrication regime.

Keywords

Gears Rattle Wavelet analysis Transmission error Lubrication 

References

  1. 1.
    Wang Y, Manoj R, Zhao WJ (2001) Gear rattle modeling and analysis for automotive manual transmissions. Proc Inst Mech Eng, Part D, J Automob Eng 215:241–258 CrossRefGoogle Scholar
  2. 2.
    Singh R, Xie H, Comparin RJ (1989) Analysis of automotive neutral gear rattle. J Sound Vib 131(2):177–196 ADSCrossRefGoogle Scholar
  3. 3.
    Dogan SN, Ryborz J, Bertsche B (2006) Design of low-noise manual automotive transmissions. Proc Inst Mech Eng, Proc Part K, J Multi-Body Dyn 220:79–95 Google Scholar
  4. 4.
    Rocca E, Russo R (2011) Theoretical and experimental investigation into the influence of the periodic backlash fluctuations on the gear rattle. J Sound Vib 330:4738–4752 ADSCrossRefGoogle Scholar
  5. 5.
    Brancati R, Rocca E, Russo R (2007) An analysis of the automotive driveline dynamic behaviour focusing on the influence of the oil squeeze effect on the idle rattle phenomenon. J Sound Vib 303(3–5):858–872 ADSCrossRefGoogle Scholar
  6. 6.
    Sbarbati F, Grasso C, Martorelli M, Liccardo P, Tosato L, Uberti M, Malusardi M (2004) Gear rattler optimisation. In: FISITA 2004, World automotive congress, Barcelona, Spain, pp 23–27 Google Scholar
  7. 7.
    Smith JD (1987) Gear transmission error accuracy with small rotary encoders. Proc Inst Mech Eng, Part C, J Mech Eng Sci 201:133–135 CrossRefGoogle Scholar
  8. 8.
    Russo R, Brancati R, Rocca E (2009) Experimental investigations about the influence of oil lubricant between teeth on the gear rattle phenomenon. J Sound Vib 321:647–661 ADSCrossRefGoogle Scholar
  9. 9.
    Staszewski WJ, Tomlinson GR (1994) Application of the wavelet transform to fault detection in a spur gear. Mech Syst Signal Process 8(3):289–307 ADSCrossRefGoogle Scholar
  10. 10.
    Wang WJ, McFadden PD (1996) Application of wavelets to gearbox vibration signals for fault detection. J Sound Vib 192(5):927–939 ADSCrossRefGoogle Scholar
  11. 11.
    Boulahbal D, Farid Golnaraghi M, Ismail F (1999) Amplitude and phase wavelet maps for the detection of cracks in geared systems. Mech Syst Signal Process 13(3):423–436 ADSCrossRefGoogle Scholar
  12. 12.
    Lin J, Zuo MJ (2003) Gearbox fault diagnosis using adaptive wavelet filter. Mech Syst Signal Process 17(6):1259–1269 ADSCrossRefGoogle Scholar
  13. 13.
    Djebala A, Ouelaa N, Benchaabane C, Laefer D (2012) Application of the wavelet multi-resolution analysis and Hilbert transform for the prediction of gear tooth defects. Meccanica 47(7):1601–1612 CrossRefGoogle Scholar
  14. 14.
    Rocca E, Russo R, Savino S (2010) On the recognition of anomalies in gears by means of the discrete wavelet transform. In: Proceedings of the 12th international mini conference on vehicle system dynamics, identification and anomalies, VSDIA 2010, Budapest Google Scholar
  15. 15.
    Bosi A (2007) A wavelet-based methodology for monitoring thermoelastic structures. Meccanica 42:477–485 MATHCrossRefGoogle Scholar
  16. 16.
    Djebala A, Ouelaa N, Hamzaoui N (2008) Detection of rolling bearing defects using discrete wavelet analysis. Meccanica 43:339–348 MATHCrossRefGoogle Scholar
  17. 17.
    Wang WJ (2001) Wavelets for detecting mechanical faults with high sensitivity. Mech Syst Signal Process 15:685–696 ADSCrossRefGoogle Scholar
  18. 18.
    Dalpiaz G, Rivola A, Rubini R (2000) Effectiveness and sensitivity of vibration processing techniques for local fault detection in gears. Mech Syst Signal Process 14(3):387–412 ADSCrossRefGoogle Scholar
  19. 19.
    Crowther AR, Janello C, Singh R (2007) Quantification of clearance-induced impulsive sources in a torsional system. J Sound Vib 307:428–451 ADSCrossRefGoogle Scholar
  20. 20.
    Mallat S (1999) A wavelet tour of signal processing. Academic Press, San Diego MATHGoogle Scholar
  21. 21.
    Meyer Y (1989) In: Combes JM et al. (eds) Wavelets. Springer, Berlin Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Renato Brancati
    • 1
  • Ernesto Rocca
    • 1
  • Sergio Savino
    • 1
  • Flavio Farroni
    • 1
  1. 1.Department of Mechanics and EnergeticsUniversity “Federico II”NaplesItaly

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