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Model based local fault detection in helical gears

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Konferencja
Symposium “Vibrations In Physical Systems” (26 ; 04-08.05.2014 ; Będlewo koło Poznania ; Polska)
Języki publikacji
EN
Abstrakty
EN
In the paper the possibility of model based detection of local faults in helical gears is analysed. Presented methods allow early detection of anomalies in the time vibration signal that could be linked to the fatigue tooth damages like pitting and tooth fracture. They relies on calculation of different signal parameters for the consecutive meshes and allows for acquiring information about the disturbances of the meshing process for particular tooth pairs. They permit the observation of the energy density changes for the consecutive teeth (or tooth pairs) during the normal exploitation of the gearbox. All the described methods are based on analysis of the time signals. Contrary to the methods based on spectral analysis these methods allow for precise localisation of gear defects and linking them to the particular pinion or gear teeth. Additionally they could be used in the procedure of gear manufacturing quality assessment.
Rocznik
Tom
Strony
169--176
Opis fizyczny
Bibliogr. 30 poz., 1 rys., wykr.
Twórcy
autor
  • Institute of Vehicles, Warsaw University of Technology, Narbutta 84, 02-524 Warszawa
Bibliografia
  • 1. R.B. Randall, A new method of modelling gear faults, J. Mech. Des., 104 (1982) 259-267
  • 2. P.D. McFadden, Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration, J. Vib. Acoust. Stress Reliab. Des., 108 (1986) 165-170.
  • 3. P.D. McFadden, Determining the location of a fatigue crack in a gear from the phase of the change in the meshing vibration, Mech. Syst. Signal Process., 2(4) (1988) 403409.
  • 4. J. Mączak, S. Radkowski, Low-energy spectrum components as a symptom of failure, Mach. Dyn. Probl., 8 (1994) 4564.
  • 5. S. Radkowski, Vibroacoustic diagnostics of low energy failures, Institute for Sustainable Technologies, Radom 2002.
  • 6. R.B. Randall, Vibration-based condition monitoring: industrial, aerospace, and automotive applications, Wiley, Hoboken, N.J 2011.
  • 7. A. Pryor, D.G. Lewicki, D.G., M. Mosher, The Application of Time-Frequency Methods to HUMS, American Helicopter Society’s 57th Annual Forum, Washington D.C. 2001.
  • 8. B. Łazarz, H. Madej, A. Wilk, T. Figlus, G. Wojnar, Diagnozowanie złożonych przypadków uszkodzeń przekładni zębatych, Institute for Sustainable Technologies, Radom 2006.
  • 9. E.B. Halim, S. Shah, M.J. Zuo, Fault detection of gearbox from vibration signals using time-frequency domain averaging. Proc. of the 2006 American Control Conference, Minneapolis, Minnesota, USA, (2006) 4430-4435.
  • 10. W. Bartelmus, R. Zimroz, A new feature for monitoring the condition of gearboxes in non-stationary operating conditions, Mech. Syst. Signal Process., 23(5) (2009) 1528-1534.
  • 11. N. Baydar, A. Ball, Detection of gear failures in helical gears by using wavelet transforms, Proceedings of the 33rd International MATADOR Conference (2000) 171-176.
  • 12. J. Antoni, The spectral kurtosis: a useful tool for characterising non-stationary signals, Mech. Syst. Signal Process., 20(2) (2006) 282-307.
  • 13. J. Antoni, R.B. Randall, The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines, Mech. Syst. Signal Process., 20(2) (2006) 308-331.
  • 14. F. Combet, L. Gelman, Optimal filtering of gear signals for early damage detection based on the spectral kurtosis, Mech. Syst. Signal Process., 23,(3) (2009) 652-668.
  • 15. T. Barszcz, R.B. Randall, Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine, Mech. Syst. Signal Process., 23(4) (2009) 1352-1365.
  • 16. R. Filonik, J. Mączak, S. Radkowski, Simulation and modelling of low - energy tooth failure in a helical gearbox, Mach. Dyn. Probl., 26(2/3) 2002 89-104.
  • 17. J. Maczak, Diagnostyka lokalnych uszkodzeń w przekładniach zębatych, Institute for Sustainable Technologies, Radom 2013.
  • 18. R. Filonik, J. Mączak, S. Radkowski, Apparent interference method as a way of modeling the meshing process disturbances, Mach. Dyn. Probl., 19 (1998) 95-108.
  • 19. J. Mączak, S. Radkowski, Modeling and tooth crack growth simulation as a vibroacoustic signal disturbances in gears, Proceedings of the VII Polish-French Seminar of Mechanics, Warszawa 2001.
  • 20. R.B. Randall, Frequency analysis, Bruel & Kjaer, Copenhagen 1987.
  • 21. J. Mączak, Wykorzystanie zjawiska modulacji sygnału wibroakustycznego w diagnozowaniu przekładni o zębach śrubowych, PhD Dissertation, Warsaw University of Technology, 1988.
  • 22. M. Feldman, Hilbert transform in vibration analysis, Mech. Syst. Signal Process., 25(3) (2011) 735-802.
  • 23. H. M. Teager, Some observations on oral flow during phonation, IEEE Trans Acoust. Speech Signal Process, 28(5) (1980) 599-601.
  • 24. J. F. Kaiser, On a simple algorithm to calculate the energy’of a Signac, International Conference on Acoustics, Speech, and Signal Processing, ICASSP-90, (1990) 381-384.
  • 25. E. Kvedalen, Signal processing using the Teager energy operator and other nonlinear operators, Master Univ. Oslo Dep. Inform. 2003.
  • 26. D. Dimitriadis, A. Potamianos, P. Maragos, A Comparison of the Squared Energy and Teager-Kaiser Operators for Short-Term Energy Estimation in Additive Noise, IEEE Trans. Signal Process., 57(7) (2009) 2569-2581.
  • 27. J. Mączak, S. Radkowski, Use of envelope contact factor in fatigue crack diagnosis of helical Sears, Mach. Dyn. Probl., 26(2/3) (2002) 115-122.
  • 28. J. Mączak, A method of detection of local disturbances in dynamic response of diagnosed machine element, in Proceedings of the International Conference on Condition Monitoring 2005, King’s College Cambridge (2005) 219-228.
  • 29. J. Mączak, Local meshing plane as a source of diagnostic information for monitoring the evolution of gear faults, Proceedings of the 4rd World Congress on Engineering Asset Management (WCEAM 2009), Athens (2009) 661-670.
  • 30. J. Mączak, A. Roszczewski, Autonomous diagnostic unit for threat identification and risk minimization in technical systems, Diagnostyka, 36 (2005) 45-52.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bacb5672-2636-4a48-b0f5-edf2fc88e690
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