Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning

Faris Elasha, Suliman Shanbr, Xiaochuan Li, David Mba

    Research output: Contribution to journalArticlepeer-review

    51 Citations (Scopus)
    56 Downloads (Pure)


    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.
    Original languageEnglish
    Article number3092
    Number of pages17
    Issue number14
    Publication statusPublished - 12 Jul 2019

    Bibliographical note

    © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (


    • Artificial neural network
    • High-speed shaft bearing
    • Prognosis
    • Regression
    • Remaining useful life
    • Vibration measurement
    • Wind turbine

    ASJC Scopus subject areas

    • Analytical Chemistry
    • Atomic and Molecular Physics, and Optics
    • Biochemistry
    • Instrumentation
    • Electrical and Electronic Engineering


    Dive into the research topics of 'Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning'. Together they form a unique fingerprint.

    Cite this