References
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Owner = {lkuhn},
School = {Technical University of Napoli},
Timestamp = {2009.05.21},
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Owner = {lkuhn},
Timestamp = {2009.05.21},
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Owner = {lkuhn},
Timestamp = {2009.05.21},
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@techreport{iso,
Author = {ISO},
Date-Modified = {2012-01-19 16:34:21 -0800},
Institution = {International Organization for Standardization},
Number = {ISO13381-1},
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Volume = {ISO/IEC Directives Part 2},
Year = {2004}}
@inproceedings{Schwabacher,
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Booktitle = {Proceedings of AAAI Fall Symposium},
Owner = {lkuhn},
Timestamp = {2009.05.21},
Title = {A Survey of Artificial Intelligence for Prognostics},
Year = {2007}}
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Booktitle = {1st ed. Hoboken},
Owner = {lkuhn},
Timestamp = {2009.05.21},
Title = {Intelligent Fault Diagnosis and Prognosis for Engineering Systems},
Year = {2006}}
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%%%%%%%%%%%%
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publisher={MDPI}
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@book{letcher2023wind,
title={Wind energy engineering: a handbook for onshore and offshore wind turbines},
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publisher={MDPI}
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publisher={Wiley Online Library}
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@book{tong2010fundamentals,
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@inproceedings{ragheb2010wind,
title={Wind turbine gearbox technologies},
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booktitle={1st International nuclear \& renewable energy conference (INREC)},
pages={1--8},
year={2010},
organization={IEEE}
}
@inproceedings{wagner2020introduction,
title={Introduction to wind energy systems},
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booktitle={EPJ Web of Conferences},
volume={246},
pages={00004},
year={2020},
organization={EDP Sciences}
}
@article{yang2013wind,
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year={2013},
publisher={Elsevier}
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title={Online wind turbine fault detection through automated SCADA data analysis},
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publisher={Wiley Online Library}
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@inproceedings{kim2011use,
title={Use of SCADA data for failure detection in wind turbines},
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booktitle={Energy sustainability},
volume={54686},
pages={2071--2079},
year={2011}
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%%%Table
@article{chen2013wind,
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journal={Expert Systems with Applications},
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year={2013},
publisher={Elsevier}
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journal={IET Renewable Power Generation},
volume={9},
number={5},
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year={2015},
publisher={Wiley Online Library}
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@article{elasha2019prognosis,
title={Prognosis of a wind turbine gearbox bearing using supervised machine learning},
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journal={Sensors},
volume={19},
number={14},
pages={3092},
year={2019},
publisher={MDPI}
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@inproceedings{cao2018remaining,
title={Remaining useful life prediction of wind turbine generator bearing based on EMD with an indicator},
author={Cao, Lixiao and Qian, Zheng and Pei, Yan},
booktitle={2018 Prognostics and System Health Management Conference (PHM-Chongqing)},
pages={375--379},
year={2018},
organization={IEEE}
}
@article{zhao2021feature,
title={Feature extraction for data-driven remaining useful life prediction of rolling bearings},
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journal={IEEE Transactions on Instrumentation and Measurement},
volume={70},
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year={2021},
publisher={IEEE}
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@article{merainani2022integrated,
title={An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples},
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journal={Renewable Energy},
volume={182},
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year={2022},
publisher={Elsevier}
}
@article{teng2016prognosis,
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journal={Energies},
volume={10},
number={1},
pages={32},
year={2016},
publisher={MDPI}
}
@article{herp2018bayesian,
title={Bayesian state prediction of wind turbine bearing failure},
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journal={Renewable Energy},
volume={116},
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@article{pan2019performance,
title={Performance degradation assessment of a wind turbine gearbox based on multi-sensor data fusion},
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title={Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques},
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journal={Wind Energy},
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@inproceedings{kramti2018direct,
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booktitle={2018 5th International Conference on Control, Decision and Information Technologies (CoDIT)},
pages={859--864},
year={2018},
organization={IEEE}
}
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year={2016},
organization={PMLR}
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year={2023},
publisher={Elsevier}
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@article{EDPDataset,
title={Dataset Wind Turbines},
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year={2023}
}
@book{tijms2003first,
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%%%%
@article{kramti2021neural,
title={A neural network approach for improved bearing prognostics of wind turbine generators},
author={Kramti, Sharaf Eddine and Ali, Jaouher Ben and Saidi, Lotfi and Sayadi, Mounir and Bouchouicha, Moez and Bechhoefer, Eric},
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volume={93},
number={2},
pages={20901},
year={2021},
publisher={EDP Sciences}
}
@article{garan2022data,
title={A data-centric machine learning methodology: Application on predictive maintenance of wind turbines},
author={Garan, Maryna and Tidriri, Khaoula and Kovalenko, Iaroslav},
journal={Energies},
volume={15},
number={3},
pages={826},
year={2022},
publisher={MDPI}
}
@article{li2022self,
title={A self-data-driven method for remaining useful life prediction of wind turbines considering continuously varying speeds},
author={Li, Naipeng and Xu, Pengcheng and Lei, Yaguo and Cai, Xiao and Kong, Detong},
journal={Mechanical Systems and Signal Processing},
volume={165},
pages={108315},
year={2022},
publisher={Elsevier}
}
@inproceedings{rajaoarisoa2024predictive,
title={Predictive maintenance model-based on multi-stage neural network systems for wind turbines},
author={Rajaoarisoa, Lala and Randrianandraina, Raubertin and Sayed-Mouchaweh, Moamar},
booktitle={2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)},
pages={1--7},
year={2024},
organization={IEEE}
}
@inproceedings{orozco2018diagnostic,
title={Diagnostic models for wind turbine gearbox components using scada time series data},
author={Orozco, Rafael and Sheng, Shuangwen and Phillips, Caleb},
booktitle={2018 IEEE International Conference on Prognostics and Health Management (ICPHM)},
pages={1--9},
year={2018},
organization={IEEE}
}