Skip to main content
SpringerLink
Log in

Application of Schur Filtering for Local Damage Detection in Gearboxes

  • Conference paper
Condition Monitoring of Machinery in Non-Stationary Operations

Abstract

Local damage (crack, pitting, spall, breakage, etc.) in gearboxes produces short in time (impulsive) and wideband in frequency, disturbance in vibration response. Detection of such cyclic changes is often very difficult due to presence of high level of noise, i.e. narrowband signal related to normal operation of gear-pair. Most of approaches available in literature propose two-stage methodology: firstly - signal enhancement related mostly to signal pre-filtering in order to extract so called signal of interest (SOI), and finally damage detection/recognition in time domain (spikes detection) or frequency domain (envelope analysis). In this paper we will follow this philosophy. In order to enhance local changes of signal statistics, an adaptive algorithm for vibration signal modelling is proposed in the paper. The discussed approach is based on the normalized exact least-square time-variant lattice filter (adaptive Schur filter). It is characterized by an extremely fast start-up performance, an excellent convergence behaviour, and a fast parameter tracking capability what makes this approach interesting. The method is well-adapted for analysis of the non-stationary time-series, so it seems to be very promising for diagnostics of gearbox working in time varying load/speed conditions.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Antoni, J.: Fast computation of the Kurtogram for the detection of transient faults. Mechanical Systems and Signal Processing 21(1), 108–124 (2007)

    Google Scholar 

  2. Antoni, J.: The spectral kurtosis: a useful tool for characterizing non-stationary signals. Mechanical Systems and Signal Processing 20(2), 282–307 (2006)

    Google Scholar 

  3. Barszcz, T., Randall, R.B.: Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine. Mechanical Systems and Signal Processing 23, 1352–1365 (2009)

    Article  Google Scholar 

  4. Combet, F., Gelman, L.: Optimal filtering of gear signals for early damage detection based on the spectral kurtosis. Mechanical Systems and Sig. Proc. 23(3), 652–668 (2009)

    Google Scholar 

  5. Barszcz, T., Jabłoński, A.: A Novel Method of Optimal Band Selection for Vibration Signal Demodulation. Mechanical Systems and Signal Processing 25(1), 431–451 (2011)

    Google Scholar 

  6. Lin, J., Zuo, M.: Gearbox fault diagnosis using adaptive wavelet filter. Mechanical Systems and Signal Processing 17(6), 1259–1269 (2003)

    Google Scholar 

  7. Peng, Z.K., Chu, F.L.: Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical Systems and Signal Processing 18(2), 199–221 (2004)

    Article  Google Scholar 

  8. Ricci, R., Pennacchi, P.: Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions. Mechanical Systems and Signal Processing 25, 821–838 (2011)

    Article  Google Scholar 

  9. Chaturvedi, G.K., Thomas, D.W.: Adaptive noise cancelling and condition monitoring. Journal of Sound and Vibration 76(3), 391–405 (1981)

    Article  Google Scholar 

  10. Lee, S.K., White, P.R.: The enhancement of impulsive noise and vibration signals for fault detection in rotating and reciprocating machinery. Journal of Sound and Vibration 217(3), 485–505 (1998)

    Article  Google Scholar 

  11. Antoni, J., Randall, R.B.: Unsupervised noise cancellation for vibration signals. Part I—evaluation of adaptive algorithms. Mech. Syst. and Sign. Proc. 18(1), 89–101 (2003)

    Google Scholar 

  12. Khemili, I., Chuchane, M.: Detection of rolling element bearing defects by adaptive filtering. European Journal of Mechanics A/Solids 24, 293–303 (2005)

    Article  MATH  Google Scholar 

  13. Barszcz, T.: 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)

    Article  MATH  Google Scholar 

  14. Makowski, R.A., Zimroz, R.: Adaptive Bearings Vibration Modelling for Diagnosis. In: Bouchachia, A. (ed.) ICAIS 2011. LNCS(LNAI), vol. 6943, pp. 248–259. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  15. Zimroz, R., Bartelmus, W.: Application of adaptive filtering for weak impulsive signal recovery for bearings local damage detection in complex mining mechanical systems working under condition of varying load. Solid State Phenomena 180, 250–257 (2012)

    Article  Google Scholar 

  16. Antoni, J., Bonnardot, F., Raad, A., Badaoui, M.: Cyclostationary Modelling of Rotating Machine Vibration Signals Mechanical Systems and Sig. Proc. 18, 1285–1314 (2004)

    Google Scholar 

  17. Antoni, J.: Cyclostationarity by examples. Mechanical Systems and Signal Processing 23(4), 987–1036 (2009)

    Article  Google Scholar 

  18. Zimroz, R., Bartelmus, W.: Gearbox condition estimation using cyclo-stationary properties of vibration signal. Key Engineering Mater. 413-414, 471–478 (2009)

    Article  Google Scholar 

  19. Baydar, N., Chen, Q., Ball, A., Kruger, U.: Detection of incipient tooth defect in helical gears using multivariate statistics. Mechanical Systems and Signal Processing 15(2), 303–321 (2001)

    Article  Google Scholar 

  20. Antoni, J.: Blind separation of vibration components: Principles and demonstrations. Mechanical Systems and Signal Processing 19, 1166–1180 (2005)

    Article  Google Scholar 

  21. Boustany, R., Antoni, J.: Blind extraction of a cyclostationary signal using reduced rank cyclic regression - A unifying approach. Mechanical Systems and Signal Processing 22, 520–541 (2008)

    Article  Google Scholar 

  22. Stewart, R.M.: Some useful data analysis techniques for gearbox diagnostics, Applications of time Series Analysis. ISVR, University of Southampton (1977)

    Google Scholar 

  23. Brie, D., Tomczak, M., Oehlmann, H., Richard, A.: Gear Crack Detection By Adaptive Amplitude And Phase Demodulation. Mechanical Systems and Signal Processing 11(1), 149–167 (1997)

    Article  Google Scholar 

  24. Gelman, L., Zimroz, R., et al.: Adaptive vibration condition monitoring technology for local tooth damage in gearboxes. Insight: Non-Destructive Testing and Condition Monitoring 47(8), 461–464 (2005)

    Article  Google Scholar 

  25. Samuel, P.D., Pines, D.J.: A review of vibration-based techniques for helicopter transmission diagnostics. Journal of Sound and Vibrat. 282(1-2), 475–508 (2005)

    Article  Google Scholar 

  26. Bartelmus, W., Zimroz, R.: Vibration condition monitoring of planetary gearbox under varying external load. Mech. Syst. and Signal Proc. 23(1), 246–257 (2009)

    Article  Google Scholar 

  27. Bartelmus, W., Zimroz, R.: A new feature for monitoring the condition of gearboxes in non-stationary operation conditions. Mech. Syst. and Signal Proc. 23(5), 1528–1534 (2009)

    Article  Google Scholar 

  28. Bartelmus, W., Chaari, F., Zimroz, R., Haddar, M.: Modelling of gearbox dynamics under time varying non-stationary operation for distributed fault detection and diagnosis. European Journal of Mechanics - A/Solids 29(4), 637–646 (2010)

    Article  Google Scholar 

  29. Combet, F., Zimroz, R.: A new method for the estimation of the instantaneous speed relative fluctuation in a vibration signal based on the short time scale transform. Mechanical Systems and Signal Processing 23, 1382–1392 (2009)

    Article  Google Scholar 

  30. Zarzycki, J.: Multidimentional Nonlinear Schur Parametrization of Non-Gausian Stochastic Signals, Part One: Statement of the problem. Multidimensional Systems and Signal Processing 15(3), 217–241 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  31. Lopatka, M., Adam, O., Laplanche, C., Zarzycki, J., Motsch, J.-F.: Effective Analysis of Non-Stationary Short-Time Signals Based on The Adaptive Schur Filter. In: IEEE/SP 13th Workshop on Statistical Signal Proc., pp. 251–256 (2005)

    Google Scholar 

  32. Lee, D.T.L., Morf, M., Friedlander, B.: Recursive least squares ladder estimation algorithms. IEEE Trans. Circuit and Systems 28(6), 627–641 (1981)

    MathSciNet  Google Scholar 

  33. Haykin, S.: Adaptive Filter Theory, 4th edn. Prentice-Hall (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Signal Theory Section, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, Wroclaw, Poland

    Ryszard Makowski

  2. Diagnostic and Vibro-Acoustics Science Laboratory, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, Wroclaw, Poland

    Radoslaw Zimroz

Authors
  1. Ryszard Makowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Radoslaw Zimroz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ryszard Makowski .

Editor information

Editors and Affiliations

  1. National School of Engineers of Sfax, Sfax, 3038, Tunisia

    Tahar Fakhfakh

  2. Wrocław University of Technology, pl. Teatralny 2, Wrocław, 50 051, Poland

    Walter Bartelmus

  3. National School of Engineers of Sfax, Sfax, 3038, Tunisia

    Fakher Chaari

  4. Wrocław University of Technology, pl. Teatralny 2, Wrocław, 50 051, Poland

    Radoslaw Zimroz

  5. National School of Engineers of Sfax, Sfax, 3038, Tunisia

    Mohamed Haddar

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag GmbH Berlin Heidelberg

About this paper

Cite this paper

Makowski, R., Zimroz, R. (2012). Application of Schur Filtering for Local Damage Detection in Gearboxes. In: Fakhfakh, T., Bartelmus, W., Chaari, F., Zimroz, R., Haddar, M. (eds) Condition Monitoring of Machinery in Non-Stationary Operations. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28768-8_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28768-8_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28767-1

  • Online ISBN: 978-3-642-28768-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation