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Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning
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 moni...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679281/ https://www.ncbi.nlm.nih.gov/pubmed/31336974 http://dx.doi.org/10.3390/s19143092 |
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author | Elasha, Faris Shanbr, Suliman Li, Xiaochuan Mba, David |
author_facet | Elasha, Faris Shanbr, Suliman Li, Xiaochuan Mba, David |
author_sort | Elasha, Faris |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6679281 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66792812019-08-19 Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning Elasha, Faris Shanbr, Suliman Li, Xiaochuan Mba, David Sensors (Basel) Article 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. MDPI 2019-07-12 /pmc/articles/PMC6679281/ /pubmed/31336974 http://dx.doi.org/10.3390/s19143092 Text en © 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Elasha, Faris Shanbr, Suliman Li, Xiaochuan Mba, David Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning |
title | Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning |
title_full | Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning |
title_fullStr | Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning |
title_full_unstemmed | Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning |
title_short | Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning |
title_sort | prognosis of a wind turbine gearbox bearing using supervised machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679281/ https://www.ncbi.nlm.nih.gov/pubmed/31336974 http://dx.doi.org/10.3390/s19143092 |
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