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Article

Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning

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Faculty of Computing, Engineering and media, De Montfort University, Leicester, LE1 9BH, UK
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Faculty of Engineering, Environment and Computing, Coventry University, Coventry CV1 2JH, UK
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Department of Engineering and Applied Science, School of Water, Energy and Environment, Cranfield University, Bedfordshire, MK43 0AL, UK
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Department of Mechanical Engineering, University of Lagos, Nigeria 100213, West Africa
*
Author to whom correspondence should be addressed.
Energies 2019, 12(14), 2705; https://doi.org/10.3390/en12142705
Received: 9 June 2019 / Revised: 5 July 2019 / Accepted: 11 July 2019 / Published: 15 July 2019
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control)
Components of rotating machines, such as shafts, bearings and gears are subject to performance degradation, which if left unattended could lead to failure or breakdown of the entire system. 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 a combination of two supervised machine learning techniques; namely, the regression model and multilayer artificial neural network model, to predict the remaining useful life of rolling element bearings. Root mean square and Kurtosis were analyzed to define the bearing failure stages. The proposed methodology was validated through two case studies involving vibration measurements of an operational wind turbine gearbox and a split cylindrical roller bearing in a test rig. View Full-Text
Keywords: prognostics; vibration measurement; regression model; artificial neural network; rolling element bearing; remaining useful life prognostics; vibration measurement; regression model; artificial neural network; rolling element bearing; remaining useful life
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MDPI and ACS Style

Li, X.; Elasha, F.; Shanbr, S.; Mba, D. Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning. Energies 2019, 12, 2705. https://doi.org/10.3390/en12142705

AMA Style

Li X, Elasha F, Shanbr S, Mba D. Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning. Energies. 2019; 12(14):2705. https://doi.org/10.3390/en12142705

Chicago/Turabian Style

Li, Xiaochuan, Faris Elasha, Suliman Shanbr, and David Mba. 2019. "Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning" Energies 12, no. 14: 2705. https://doi.org/10.3390/en12142705

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