{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T14:32:34Z","timestamp":1783002754072,"version":"3.54.5"},"reference-count":50,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T00:00:00Z","timestamp":1562889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s19143092","type":"journal-article","created":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T11:49:38Z","timestamp":1562932178000},"page":"3092","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":71,"title":["Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8323-6673","authenticated-orcid":false,"given":"Faris","family":"Elasha","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Environment &amp; Computing, Coventry University, Coventry CV1 5FB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suliman","family":"Shanbr","sequence":"additional","affiliation":[{"name":"School of Water, Energy and Environment, Cranfield University, Bedfordshire MK43 0AL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaochuan","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7278-4623","authenticated-orcid":false,"given":"David","family":"Mba","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK"},{"name":"Department of Mechanical Engineering, University of Nigeria, Nsukka 410001, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,12]]},"reference":[{"key":"ref_1","first-page":"657","article-title":"Wind turbine condition monitoring: Technical and commercial challenges","volume":"17","author":"Yang","year":"2013","journal-title":"Wind Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.apacoust.2017.01.005","article-title":"Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR","volume":"120","author":"Saidi","year":"2017","journal-title":"Appl. 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