scholarly journals Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques

Wind Energy ◽  
2018 ◽  
Vol 22 (3) ◽  
pp. 360-375 ◽  
Author(s):  
James Carroll ◽  
Sofia Koukoura ◽  
Alasdair McDonald ◽  
Anastasis Charalambous ◽  
Stephan Weiss ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Machine Learning Techniques ◽  
Wind Turbine Gearbox ◽  
Learning Techniques ◽  
Useful Life

scholarly journals PCA based health indicator for remaining useful life prediction of wind turbine gearbox

2019 ◽  
Vol 29 ◽  
pp. 31-36
Author(s):  
Sabareesh G R ◽  
Hemanth Mithun Praveen ◽  
Divya Shah ◽  
Krishna Dutt Pandey ◽  
Vamsi I
Keyword(s):  
Wind Turbine ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Health Indicator ◽  
Wind Turbine Gearbox ◽  
Useful Life

scholarly journals Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3092 ◽  
Author(s):  
Elasha ◽  
Shanbr ◽  
Li ◽  
Mba
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
High Speed ◽  
Large Scale ◽  
Remaining Useful Life ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Accurate Estimation ◽  
Wind Turbine Gearbox ◽  
Vibration Measurements

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.

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

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2705 ◽  
Author(s):  
Xiaochuan Li ◽  
Faris Elasha ◽  
Suliman Shanbr ◽  
David Mba
Keyword(s):  
Machine Learning ◽  
Roller Bearing ◽  
Remaining Useful Life ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Accurate Estimation ◽  
Rolling Element Bearings ◽  
Wind Turbine Gearbox ◽  
Rolling Element ◽  
Useful Life

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.

scholarly journals Failure and Remaining Useful Life Prediction of Wind Turbine Gearboxes

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Sofia Koukoura
Keyword(s):  
Wind Turbine ◽  
Life Prediction ◽  
Expert Knowledge ◽  
Remaining Useful Life ◽  
Wind Turbine Gearbox ◽  
Life Estimation ◽  
Knowledge Based ◽  
Incipient Faults ◽  
Frame Work ◽  
Useful Life

The purpose of this project is to predict wind turbine gearbox incipient faults using a combination of condition monitoring data. It is expected to contribute in developing a robust frame-work for wind turbine gearbox component incipient failure prediction and remaining useful life estimation. It further pro-poses a solution on how to overcome the challenges of expert knowledge based systems using AI techniques. Wind turbine operation and maintenance decision making confidence can be therefore increased.

Hybrid method for remaining useful life prediction in wind turbine systems

2018 ◽  
Vol 116 ◽  
pp. 173-187 ◽  
Author(s):  
M.A. Djeziri ◽  
S. Benmoussa ◽  
R. Sanchez
Keyword(s):  
Wind Turbine ◽  
Hybrid Method ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Useful Life

A hybrid DBN-SOM-PF-based prognostic approach of remaining useful life for wind turbine gearbox

2020 ◽  
Vol 152 ◽  
pp. 138-154 ◽  
Author(s):  
Yubin Pan ◽  
Rongjing Hong ◽  
Jie Chen ◽  
Weiwei Wu
Keyword(s):  
Wind Turbine ◽  
Remaining Useful Life ◽  
Wind Turbine Gearbox ◽  
Useful Life

scholarly journals Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox

Energies ◽  
2016 ◽  
Vol 10 (1) ◽  
pp. 32 ◽  
Author(s):  
Wei Teng ◽  
Xiaolong Zhang ◽  
Yibing Liu ◽  
Andrew Kusiak ◽  
Zhiyong Ma
Keyword(s):  
Wind Turbine ◽  
Remaining Useful Life ◽  
Wind Turbine Gearbox ◽  
Useful Life
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