Enhancing Wind Turbine Reliability through Proactive High Speed Bearing Prognosis Based on Adaptive Threshold and Gated Recurrent Unit Networks-Reference-Cited by-同舟云学术

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Enhancing Wind Turbine Reliability through Proactive High Speed Bearing Prognosis Based on Adaptive Threshold and Gated Recurrent Unit Networks

Published:2023-10-16 Issue: Volume: Page:
ISSN:
Container-title:IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society
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Author:

Dhiman Harsh S.¹,Bhanushali Dev¹,Su Chun-Lien²,Berghout Tarek³,Amirat Yassine⁴,Benbouzid Mohamed⁵

Affiliation:

1. Symbiosis Institute of Technology,Department of AI & ML,Pune,India

2. National Kaohsiung University of Science and Technology,Department of Electrical Engineering,Taiwan

3. University of Batna 2,Laboratory of Automation and Manufacturing Engineering,Algeria

4. L@bISEN,Department of Electrical Engineering,Brest,France

5. University of Brest,UMR CNRS 6027 IRDL,France

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10311571/10311610/10312064.pdf?arnumber=10312064

Reference16 articles.

1. Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction

2. Condition monitoring of wind turbines based on analysis of temperature-related parameters in supervisory control and data acquisition data

3. A review of irregular time series data handling with gated recurrent neural networks

4. Wind Turbine Condition Monitoring Based on Bagging Ensemble Strategy and KNN Algorithm

5. EMD-Kalman filter design for wind turbine condition monitoring

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders;Sensors;2024-05-18

2. Wind energy system fault classification and detection using deep convolutional neural network and particle swarm optimization‐extreme gradient boosting;IET Energy Systems Integration;2024-03-12

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