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 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 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

A recurrent neural network based health indicator for remaining useful life prediction of bearings

2017 ◽  
Vol 240 ◽  
pp. 98-109 ◽  
Author(s):  
Liang Guo ◽  
Naipeng Li ◽  
Feng Jia ◽  
Yaguo Lei ◽  
Jing Lin
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Health Indicator ◽  
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

scholarly journals A Scheme with Acoustic Emission Hit Removal for the Remaining Useful Life Prediction of Concrete Structures

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7761
Author(s):  
Tuan-Khai Nguyen ◽  
Zahoor Ahmad ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Recurrent Neural Network ◽  
Life Prediction ◽  
Deep Neural Network ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Health Indicator ◽  
Removal Process ◽  
Useful Life

In this study, a scheme of remaining useful lifetime (RUL) prognosis from raw acoustic emission (AE) data is presented to predict the concrete structure’s failure before its occurrence, thus possibly prolong its service life and minimizing the risk of accidental damage. The deterioration process is portrayed by the health indicator (HI), which is automatically constructed from raw AE data with a deep neural network pretrained and fine-tuned by a stacked autoencoder deep neural network (SAE-DNN). For the deep neural network structure to perform a more accurate construction of health indicator lines, a hit removal process with a one-class support vector machine (OC-SVM), which has not been investigated in previous studies, is proposed to extract only the hits which matter the most to the portrait of deterioration. The new set of hits is then harnessed as the training labels for the deep neural network. After the completion of the health indicator line construction, health indicators are forwarded to a long short-term memory recurrent neural network (LSTM-RNN) for the training and validation of the remaining useful life prediction, as this structure is capable of capturing the long-term dependencies, even with a limited set of data. Our prediction result shows a significant improvement in comparison with a similar scheme but without the hit removal process and other methods, such as the gated recurrent unit recurrent neural network (GRU-RNN) and the simple recurrent neural network.

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