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

Author

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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
    Department of Mechanical Engineering, University of Lagos, Nigeria 100213, West Africa)

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.

Suggested Citation

  • Xiaochuan Li & Faris Elasha & Suliman Shanbr & David Mba, 2019. "Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning," Energies, MDPI, vol. 12(14), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2705-:d:248598
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    References listed on IDEAS

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    1. Elasha, Faris & Mba, David & Togneri, Michael & Masters, Ian & Teixeira, Joao Amaral, 2017. "A hybrid prognostic methodology for tidal turbine gearboxes," Renewable Energy, Elsevier, vol. 114(PB), pages 1051-1061.
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    Cited by:

    1. Isac Antônio dos Santos Areias & Luiz Eduardo Borges da Silva & Erik Leandro Bonaldi & Levy Ely de Lacerda de Oliveira & Germano Lambert-Torres & Vitor Almeida Bernardes, 2019. "Evaluation of Current Signature in Bearing Defects by Envelope Analysis of the Vibration in Induction Motors," Energies, MDPI, vol. 12(21), pages 1-15, October.
    2. Akilu Yunusa-Kaltungo & Ruifeng Cao, 2020. "Towards Developing an Automated Faults Characterisation Framework for Rotating Machines. Part 1: Rotor-Related Faults," Energies, MDPI, vol. 13(6), pages 1-20, March.
    3. Vikas Singh & Purushottam Gangsar & Rajkumar Porwal & A. Atulkar, 2023. "Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 931-960, March.
    4. Nagulapati, Vijay Mohan & Lee, Hyunjun & Jung, DaWoon & Brigljevic, Boris & Choi, Yunseok & Lim, Hankwon, 2021. "Capacity estimation of batteries: Influence of training dataset size and diversity on data driven prognostic models," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

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