Prognosis of defects for machines working under large variation of speed and load conditions is a topic still under development. Wind turbines are recent examples of such kind of machines that need reliable diagnosis methods. Vibration analysis can be of very limited use when the speed variation is too high. An effective angular resampling method can be very valuable as the first step of vibration signal processing but it is important to know what are the appropriate variables to be monitored. The authors present a statistical analysis method consisting of a linear model based on the parameters that characterize the system, in our case the variable speed and load, and the fault condition to which the system is subjected. With this method can be determined if the variable analyzed is significant, that is to say if are sensitive to these parameters and hence can detect the fault faster. The aim of implementing this method is to reduce the number of variables to be monitored, resulting in a savings not only in measuring equipment but also in times of processing and analyzing information. The results of vibration analysis of a test-bed working under large variation of speed and load are shown. Different tests with increasing level of defects are tried and the corresponding vibration is analyzed and modeled so an effective detection and prognosis can be done. Taking in to account such variation of speed and load for the vibration modeling can lead to a very sensitive detection of incipient defects.
How to Cite
T. Barszcz, R. B. Randall. (2009). Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine, Mechanical systems and signal processing, vol. 23, pp 1352-1365.
D. M. Blunt, J. A. Keller. (2006). Detection of a fatigue crack in a UH-60A planet gear carrier using vibration analysis, Mechanical systems and signal processing, vol. 20, pp 2095-2111.
F. Combet, R. Zimroz. (2009). 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, vol. 23, pp 1382-1397.
W. Bartelmus, R. Zimroz. (2009). Vibration condition monitoring of planetary gearbox under varying external load, Mechanical systems and signal processing, vol. 23, pp 246-257.
A. Davies. (1998). Handbook of condition monitoring techniques and methodology, Chapman and Hall, UK.
P. D. Samuel, D. J. Pines. (2005). A review of vibration-based techniques for helicopter transmission diagnostics, Journal of Sound and Vibration, vol. 282, pp 475-508.
P.D. McFadden. (1989). Interpolation techniques for time domain averaging of gear vibration, Mechanical Systems and Signal Processing, vol. 3 pp 87-97.
P.D. McFadden. (1991). A technique for calculating the time domain averages of the vibration of the individual planet gears and the sun gear in an epicyclic gearbox, Journal of Sound and Vibration, vol. 144 pp 163-172.
K.R. Fyfe, D.S. Munck. (1997). Analysis of computed order tracking, Mechanical Systems and Signal Processing, vol. 11 pp 187-205.
K.M. Bossley, R.J. Mckendrick, C.J. Harris, C. Mercer. (1999). Hybrid computed order tracking, Mechanical Systems and Signal Processing, vol. 13 pp 627-641.
F. Bonnardot, M. El Badaoui, R. B. Randall, J. Danière, F. Guillet. (2005). Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation), Mechanical Systems and Signal Processing, vol. 19 pp 766-785.
F. Combet, L. Gelman. (2007). An automated methodology for performing time synchronous averaging of a gearbox signal without speed sensor, Mechanical Systems and Signal Processing, vol. 21 pp 2590-2606.
C. J. Stander, P. S. Heyns. (2005). Instantaneous angular speed monitoring of gearboxes under non- cyclic stationary load conditions, Mechanical systems and signal processing, vol. 19 pp 817-835.
UpWind. (2006). “state of the art” report condition monitoring for wind turbines.
J. Stander, P. S. Heyns, W. Schoombie. (2002). Using vibration monitoring for local fault detection on gears operation under fluctuating load conditions, Mechanical systems and signal processing, vol. 16 pp 1005-1024.
C. J. Stander, P. S. Heyns. (2006). Transmission path phase compensation for gear monitoring under fluctuating load conditions, Mechanical systems and signal processing, vol. 20 pp 1511-1522.
Y. Zhan, V. Makis, A. K. S Jardine. (2006). Adaptive state detection of gearboxes under varying load conditions based on parametric modeling, Mechanical systems and signal processing, vol. 20 pp 188-221.
L. F. Villa, A. Reñones, J. R. Perán, L. J. Miguel. (2011). Angular resampling for vibration analysis in wind turbines under non-linear speed fluctuation, Mechanical systems and signal processing, Article in Press.
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