Vibration Analysis and Time Series Prediction for Wind Turbine Gearbox Prognostics
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Published
Nov 1, 2020
Sajid Hussain
Hossam A. Gabbar
Abstract
Multiple premature failures of a gearbox in a wind turbine pose a high risk of increasing the operational and maintenance costs and decreasing the profit margins. Prognostics and health management (PHM) techniques are widely used to assess the current health condition of the gearbox and project it in future to predict premature failures. This paper proposes such techniques for predicting gearbox health condition index extracted from the vibration signals. The progression of the monitoring index is predicted using two different prediction techniques, adaptive neuro-fuzzy inference system (ANFIS) and nonlinear autoregressive model with exogenous inputs (NARX). The proposed prediction techniques are evaluated through sun-spot data-set and applied on vibration based health related monitoring index calculated through psychoacoustic phenomenon. A comparison is given for their prediction accuracy. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating features, the level of damage/degradation, and their progression.
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Keywords
Vibration Analysis, Time Series Prediction, Wind Turbine Gearbox Prognostics
References
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Andrew K.S. Jardine, Daming Lin, and Dragan Banjevic (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, vol. 20, no.7, pp. 1483–1510.
B. Samanta and C. Nataraj (2008). Prognostics of machine condition using soft computing. Robotics and Computer-Integrated Manufacturing, vol. 24, no. 6, pp. 816-823.
C. C. James., (2011). Condition monitoring techniques for wind turbines. Doctoral dissertation, Durham University, UK. http://etheses.dur.ac.uk/652/.
E. Zwicker, and H. Fastl (2009). Psychoacoustics - Facts and Models. Springer 2nd edition.
Edmundo G. de Souza e Silva, Luiz F.L. Legey and Edmundo A. de Souza e Silva (2010). Forecasting oil price trends using wavelets and hidden markov models. Energy Economics, vol. 32, pp. 1507–1519.
Eric Bechhoefer, Steve Clark, and David He (2010). A state-space model for vibration based prognostics. Annual Conference of the Prognostics and Health Management Society, October 10-16, Portland, Oregon USA.
F. Combet, L. Gelman (2009). Optimal filtering of gear signals for early damage detection based on the spectral kurtosis. Mechanical Systems and Signal Processing, vol. 23, no. 3, pp. 652-668.
Graps, A. (1995). An introduction to wavelets. IEEE Computational Science and Engineering, vol. 2, no. 2, PP. 50–61.
H. Link, W. LaCava, J. van Dam, B. McNiff, S. Sheng, R. Wallen, M. McDade, S. Lambert, S. Butterfield, and F. Oyague (2011). Gearbox reliability collaborative project report: Findings from Phase 1 and Phase 2 Testing. NREL/TP-5000-51885.
Hui Li, YupingZhang, and Haiqi Zheng (2011). Application of Hermitian wavelet to crack fault detection in gearbox. Mechanical Systems and Signal Processing, vol. 25, pp. 1353–1363.
Halima, E. B., Shoukat Choudhury, M. A. A., Shah, S. L., and Zuo, M. J. (2008). Time domain averaging across all scales: a novel method for detection of gearbox faults. Mechanical Systems and Signal Processing, vol. 22, no. 2, pp. 261-278.
Hiram Firpi and George Vachtsevanos (2008). Genetically programmed-based artificial feature-extraction applied to fault detection. Engineering Applications of Artificial Intelligence, vol. 21, no. 4, pp. 558-568.
J. Rafiee, M.A. Rafiee, P.W. Tse (2010). Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Systems with Applications, vol. 37, no. 6, pp. 4568-4579.
Jerome Antoni (2007). Fast computation of the kurtogram for the detection of transient faults. Journal of Mechanical systems and Signal Processing, vo. 21, no. 1, pp. 108-124.
L. Gelman, I. Petrunin, I. K. Jennions, and M. Walters (2012). Diagnostics of local tooth damage in gears by the wavelet technology. International Journal of Prognostic and Health Management, vol. 3, no. 2.
R. D. Patterson, K. Robinson, J. Holdsworth, D. McKeown, C.Zhang, and M. H. Allerhand (1992). Complex sounds and auditory images. Auditory physiology and perception, Proc. 9th International Symposium on Hearing, Pergamon, Oxford, pp. 123-177.
Richard Dupuis (2010). Application of oil debris monitoring for wind turbine gearbox prognostics and health management. Annual Conference of the Prognostics and Health Management Society, October 10-16, Portland, Oregon USA.
RWC Belgium World Data Center, Online sunspot data archive, SIDC, [Online]. Available: [Cited: 20th Dec, 2012]: http://sidc.oma.be/index.php3.
S. Seneff (1988). A Joint Synchrony/mean-rate Model of Auditory Speech Processing. Journal of Phonetics, vol. 16, no.1, pp. 55-76.
T. Lin, B. G. Horne, P. Tino, and C. L. Giles (1997). A delay damage model selection algorithm for NARX neural networks. IEEE Transactions on Signal Processing, vol. 45, no. 11, pp. 2719–2730.
Uros Lotric and Andrej Dobnikar (2005). Predicting time series using neural networks with wavelet-based denoising layers. Neural Computing and Applications, vol. 14, pp. 11–17.
V. Indira, R. Vasanthakumari and V. Sugumaran (2010). Minimum sample size determination of vibration signals in machine learning approach to fault diagnosis using power analysis. Expert Systems with Applications, vol. 37, no.12, pp. 8650-8658.
Wang W. (2007). An adaptive predictor for dynamic system forecasting. Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 809–23.
Wang WQ (2004), Golnaraghi MF, Ismail F. Prognosis of machine health conditionusing neuro-fuzzy systems. Mech Syst Signal Process. Vol. 18, pp. 813–31.
Yaguo Lei, Ming J. Zuo, Zhengjia He, and Yanyang Zi (2010). A multidimensional hybrid intelligent method for gear fault diagnosis. Expert Systems with Applications, vol. 37, no. 2, pp. 1419-1430.
Section
Technical Papers