In the wind energy industry, gearbox failures are among the most costly and the most frequent, adding significantly to the operation and maintenance costs over the life cycle of the turbine. Despite significant improvements in the understanding of gear loads and dynamics, even to the point of establishing international standards for design and specifications of wind turbine gearboxes, these components generally fall short of reaching their 20 year design life. In a significant number of gearbox failures, the primary bearing on the low speed shaft experiences faults in its operation, such as misalignment and movement on the mounts. To investigate the topic of gear health management, a fault detection approach is applied to a test bed involving a spur gear double-reduction transmission, outfitted with a torque transducer and triaxial accelerometers on the bearing cases. The test bed is not a wind turbine gearbox – the gear arrangement is different and the gears are smaller compared to that of a typical wind turbine gearbox – but it does serve to test the modeling and fault detection methods proposed in this paper. Both baseline and faulted measurements are taken from the experimental set-up for data analysis. It is shown that the torque sensor provides an early indication of fault precursors, such as misalignment and decreased lubrication, while also maintaining the capacity to identify mature faults, such as chipped and missing gear teeth. The measurements are analyzed using statistical based methods – the Mahalanobis distance and Parzen discriminant analysis. These features for fault detection are then characterized at various operating speeds for each of the geartrain conditions of interest. An analytical model is created from first principles for verification of results and for simulation of the free and forced dynamics of the system.
How to Cite
gearbox, torque transducer, gear failure, failure detection
T. Burton, D. Sharpe, N. Jenkins, E. Bossanyi (2001). Wind Energy Handbook. Wiley.
Y. Fang, S. Shan, H. Chang, X. Chen, and W. Gao (2008). Parzen Discriminant Analysis. In: Proceedings – 19th International Conference on Pattern Recognition. Tampa, FL.
P.J.L. Fernandes (1996). Tooth Bending Fatigue Failures in Gears. Engineering Failure Analysis. Vol 3. No 3. pp. 219-225.
R.W. Hyers, K.L. McGowan, K.L. Sullivan, J.F. Manwell, and B.C. Syrett (2006, September). Condition Monitoring and Prognosis of Utility Scale Wind Turbines. Energy Materials, Vol. 1, No. 3. pp. 187-203.
R.A. Johnson and D.W. Wichern (2007). Applied Multivariate Statistical Analysis. Pearson Prentice Hall.
P.M. Ku (1994). Gear Failure Modes – Importance of Lubrication and Mechanics. ASLE Transactions. Vol. 19, Issue 3. July 1976. pp. 239-249.
Walt Musial, Sandy Butterfield, and Brian McNiff (2007, May), in Proceedings of European Wind Energy Conference, Milan, Italy.
E.J. Nestorides (1958). A Handbook on Torsional Vibration. CambridgeUniversityPress.
A. Papoulis and S.U. Pillai (2002). Probability . Random Variables and Stochastic Processes. McGraw-Hill.
R.G. Parker, S.M. Vijayakar, and T. Imajo (2000). Non-linear Dynamic Response of a Spur Gear Pair: Modelling and Experimental Comparisons. Journal of Sound and Vibration 237(3), pp. 435-455.
W.J. Staszewski, K. Worden and G.R. Tomlinson (1997). Time-Frequency Analysis in Gearbox Fault Detection using the Wigner-Ville Distribution and Pattern Recognition. Mechanical Systems and Signal Processing. Vol. 11, No. 5. pp. 673-692.
J. Wang, R. Li, and X. Peng (2003, May). Survey of Non-Linear Vibration of Gear Transmission Systems. Journal of Applied Mechanics. Vol. 56. No. 3.
S. Watson and J. Xiang (2006, May). Real-time Condition Monitoring of Offshore Wind Turbines. in Proceedings of European Wind Energy Conference, Athens Greece.
M. Xu and R.D. Marangoni (1994). Vibration analysis of a Motor-Flexible Coupling-Rotor System Subject to Misalignment and Unbalance, Part 1: Theoretical Model and Analysis. Journal of Sound and Vibration. 176(5), pp. 663-679.
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