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Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning

1
Faculty of Engineering, Environment & Computing, Coventry University, Coventry CV1 5FB, UK
2
School of Water, Energy and Environment, Cranfield University, Bedfordshire MK43 0AL, UK
3
Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK
4
Department of Mechanical Engineering, University of Nigeria, Nsukka 410001, Nigeria
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3092; https://doi.org/10.3390/s19143092
Received: 20 June 2019 / Revised: 9 July 2019 / Accepted: 9 July 2019 / Published: 12 July 2019
(This article belongs to the Section Physical Sensors)
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Abstract

Deployment of large-scale wind turbines requires sophisticated operation and maintenance strategies to ensure the devices are safe, profitable and cost-effective. Prognostics aims to predict the remaining useful life (RUL) of physical systems based on condition measurements. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes to combine two supervised machine learning techniques, namely, regression model and multilayer artificial neural network model, to predict the RUL of an operational wind turbine gearbox using vibration measurements. Root Mean Square (RMS), Kurtosis (KU) and Energy Index (EI) were analysed to define the bearing failure stages. The proposed methodology was evaluated through a case study involving vibration measurements of a high-speed shaft bearing used in a wind turbine gearbox. View Full-Text
Keywords: wind turbine; vibration measurement; regression; artificial neural network; high-speed shaft bearing; prognosis; remaining useful life wind turbine; vibration measurement; regression; artificial neural network; high-speed shaft bearing; prognosis; remaining useful life
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Elasha, F.; Shanbr, S.; Li, X.; Mba, D. Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning. Sensors 2019, 19, 3092.

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