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

Xiaochuan Li, Faris Elasha, Suliman Shanbr and David Mba
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Xiaochuan Li: Faculty of Computing, Engineering and media, De Montfort University, Leicester, LE1 9BH, UK
Faris Elasha: Faculty of Engineering, Environment and Computing, Coventry University, Coventry CV1 2JH, UK
Suliman Shanbr: Department of Engineering and Applied Science, School of Water, Energy and Environment, Cranfield University, Bedfordshire, MK43 0AL, UK
David Mba: Faculty of Computing, Engineering and media, De Montfort University, Leicester, LE1 9BH, UK

Energies, 2019, vol. 12, issue 14, 1-17

Abstract: 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.

Keywords: prognostics; vibration measurement; regression model; artificial neural network; rolling element bearing; remaining useful life (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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