Advertisement

Trajectory Clustering for Vibration Detection in Aircraft Engines

  • Aurélien Hazan
  • Michel Verleysen
  • Marie Cottrell
  • Jérôme Lacaille
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6171)

Abstract

The automatic detection of the vibration signature of rotating parts of an aircraft engine is considered. This paper introduces an algorithm that takes into account the variation over time of the level of detection of orders, i.e. vibrations ate multiples of the rotating speed. The detection level over time at a specific order are gathered in a so-called trajectory. It is shown that clustering the trajectories to classify them into detected and non-detected orders improves the robustness to noise and other external conditions, compared to a traditional statistical signal detection by an hypothesis test. The algorithms are illustrated in real aircraft engine data.

Keywords

Mutual Information Vibration Signal Aircraft Engine Vibratory Signal Shaft Speed 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bladh, R.: Efficient predictions of the vibratory response of mistuned bladed disks by reduced order modeling. PhD thesis, University of Michigan (July 2001)Google Scholar
  2. 2.
    Braun, S.: Mechanical Signature Analysis: theory and Applications. Academic Press, New York (1986)Google Scholar
  3. 3.
    Boashash, B.: Time-frequency signal analysis and processing - A comprehensive reference. Elsevier, Amsterdam (2003)Google Scholar
  4. 4.
    Randall: State of art in monitoring rotating machinery - Part I. Sound and Vibration 38(3), 14–21 (2004)Google Scholar
  5. 5.
    Muszynska, A.: Rotordynamics. Taylor & Francis, Abington (2005)zbMATHGoogle Scholar
  6. 6.
    Lyon, R.: Machinery Noise and Diagnostics. Butterworths, Boston (1987)Google Scholar
  7. 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)CrossRefGoogle Scholar
  8. 8.
    Mallat, S.: Une exploration des signaux en ondelettes. Publications Ecole Polytechnique (2000)Google Scholar
  9. 9.
    Kay, S.: Fundamentals of statistical signal processing: detection theory. Prentice-Hall, Englewood Cliffs (1998)Google Scholar
  10. 10.
    Poor, H.: An introduction to signal detection and estimation, 2nd edn. Springer, Berlin (1994)zbMATHGoogle Scholar
  11. 11.
    Van Trees, H.: Detection, estimation, and modulation theory-Part 1. John Wiley and Sons, Chichester (2001)Google Scholar
  12. 12.
    Basseville, M., Nikiforov, I.V.: Detection of abrupt changes: theory and application. Prentice-Hall, Englewood Cliffs (1993)Google Scholar
  13. 13.
    Gertler, J.: Fault detection and diagnosis in engineering systems. CRC Press, Boca Raton (1998)Google Scholar
  14. 14.
    Tumer, I., Bajwa, A.: A survey of aircraft engine health monitoring systems. In: 35th Joint Propulsion Conference. AIAA (June 1999)Google Scholar
  15. 15.
    Jaw, L.C., Mattingly, J.D.: Aircraft Engine Controls: Design, System Analysis, and Health Monitoring. AIAA Education Series (2009)Google Scholar
  16. 16.
    Peng, Z.K., Chu, F.L., Tse, P.W.: Detection of the rubbing-caused impacts for rotor-stator fault diagnosis using reassigned scalogram. Mechanical Systems and Signal Processing 19(2), 391–409 (2005)CrossRefGoogle Scholar
  17. 17.
    Kharyton, V.: Fault detection of blades in blades ov an aviation engines in operation. PhD thesis, Ecole Centrale de Lyon (2009)Google Scholar
  18. 18.
    Orsagh, R., Sheldon, J., Klenke, C.: Prognostics/diagnostics for gas turbine engine bearings. In: Proceedings of IEEE Aerospace Conference (2003)Google Scholar
  19. 19.
    Wang, W., Ismail, F., Golnaraghi, M.: Assessment of gear damage monitoring techniques using vibration measurements. Mechanical Systems and Signal Processing 15(5), 905–922 (2001)CrossRefGoogle Scholar
  20. 20.
    Potter, R., Gribler, M.: Computed order tracking obsoletes older methods. In: Proceedings of SAE Noise and Vibration Conference, pp. 63–67 (1989)Google Scholar
  21. 21.
    Fyfe, K.R., Munck, E.D.S.: Analysis of computed order tracking. Mechanical Systems and Signal Processing 11(2), 187–205 (1997)CrossRefGoogle Scholar
  22. 22.
    Qian, S.: Gabor expansion for order tracking. Sound and Vibration 37(6), 18–22 (2003)Google Scholar
  23. 23.
    Vold, H., Leuridan, J.: Resolution order tracking at extreme slow rates, using Kalman tracking filters. In: Proc. SAE Noise and Vibration Conference, Traverse City, MI (1993)Google Scholar
  24. 24.
    Pan, M.C., Lin, Y.F.: Further exploration of Vold-Kalman-filtering order tracking with shaft-speed information-i: Theoretical part, numerical implementation and parameter investigations. Mechanical Systems and Signal Processing 20, 1134–1154 (2006)CrossRefGoogle Scholar
  25. 25.
    Basseville, M., Le Vey, G.: Analyse et surveillance vibratoire d’une machine en rotation. In: Bensoussan, A., Lions, J., Thoma, M., Wyner, A. (eds.) Analysis and Optimization of Systems. LNCIS, vol. 111. Springer, Heidelberg (1988)CrossRefGoogle Scholar
  26. 26.
    Ypma, A.: Learning methods for machine vibration analysis and health monitoring. PhD thesis, Technische Universiteit Delft (2001)Google Scholar
  27. 27.
    Staszewski, W., Worden, K.: Signal processing for damage detection. In: Staszewski, W., Boller, C., Tomlinson, G.R. (eds.) Health Monitoring of Aerospace Structures: Smart Sensor Technologies and Signal Processing. Wiley, Chichester (2004)Google Scholar
  28. 28.
    Feichtinger, H., Strohmer, T.: Gabor analysis and algorithms: theory and applications. Birkhäuser, Boston (1998)zbMATHGoogle Scholar
  29. 29.
    Kraskov, A., Stögbauer, H., Grassberger, P.: Estimating mutual information. Phys. Rev. E 69(6) (2004)Google Scholar
  30. 30.
    Søndergaard, P.: Finite Discrete Gabor Analysis. PhD thesis, Institut for Matematik - DTU (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Aurélien Hazan
    • 1
  • Michel Verleysen
    • 1
    • 2
  • Marie Cottrell
    • 1
  • Jérôme Lacaille
    • 3
  1. 1.SAMM, Université Paris 1ParisFrance
  2. 2.DICE, Université Catholique de LouvainLouvain-la-NeuveBelgium
  3. 3.SNECMA, Groupe SafranMoissy CramayelFrance

Personalised recommendations