A Normal Behavior-Based Condition Monitoring Method for Wind Turbine Main Bearing Using Dual Attention Mechanism and Bi-LSTM
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Christian Tutivén & Yolanda Vidal & Andres Insuasty & Lorena Campoverde-Vilela & Wilson Achicanoy, 2022. "Early Fault Diagnosis Strategy for WT Main Bearings Based on SCADA Data and One-Class SVM," Energies, MDPI, vol. 15(12), pages 1-16, June.
- Jarosław Brodny & Magdalena Tutak & Peter Bindzár, 2021. "Assessing the Level of Renewable Energy Development in the European Union Member States. A 10-Year Perspective," Energies, MDPI, vol. 14(13), pages 1-38, June.
- Xiang, Ling & Yang, Xin & Hu, Aijun & Su, Hao & Wang, Penghe, 2022. "Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks," Applied Energy, Elsevier, vol. 305(C).
- Shaoqian Pei & Hui Qin & Liqiang Yao & Yongqi Liu & Chao Wang & Jianzhong Zhou, 2020. "Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network," Energies, MDPI, vol. 13(16), pages 1-23, August.
- Xiaocong Xiao & Jianxun Liu & Deshun Liu & Yufei Tang & Fan Zhang, 2022. "Condition Monitoring of Wind Turbine Main Bearing Based on Multivariate Time Series Forecasting," Energies, MDPI, vol. 15(5), pages 1-23, March.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Junshuai Yan & Yongqian Liu & Xiaoying Ren & Li Li, 2023. "Wind Turbine Gearbox Condition Monitoring Using Hybrid Attentions and Spatio-Temporal BiConvLSTM Network," Energies, MDPI, vol. 16(19), pages 1-22, September.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Alessandro Murgia & Robbert Verbeke & Elena Tsiporkova & Ludovico Terzi & Davide Astolfi, 2023. "Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis," Energies, MDPI, vol. 16(2), pages 1-20, January.
- Chen Zhang & Tao Yang, 2023. "Anomaly Detection for Wind Turbines Using Long Short-Term Memory-Based Variational Autoencoder Wasserstein Generation Adversarial Network under Semi-Supervised Training," Energies, MDPI, vol. 16(19), pages 1-18, October.
- Katarzyna Chudy-Laskowska & Tomasz Pisula, 2022. "An Analysis of the Use of Energy from Conventional Fossil Fuels and Green Renewable Energy in the Context of the European Union’s Planned Energy Transformation," Energies, MDPI, vol. 15(19), pages 1-23, October.
- Li, Wenxue & Liu, Fei, 2023. "Financing green resource generation in sub-saharan Africa: Does financial integration matter?," Resources Policy, Elsevier, vol. 85(PB).
- Xiyong Zhao & Yanzhou Li & Yongli Chen & Xi Qiao, 2022. "A Method of Cyanobacterial Concentrations Prediction Using Multispectral Images," Sustainability, MDPI, vol. 14(19), pages 1-15, October.
- Wang, Anqi & Pei, Yan & Qian, Zheng & Zareipour, Hamidreza & Jing, Bo & An, Jiayi, 2022. "A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification," Applied Energy, Elsevier, vol. 321(C).
- Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
- Zheng Yuan & Baohua Wen & Cheng He & Jin Zhou & Zhonghua Zhou & Feng Xu, 2022. "Application of Multi-Criteria Decision-Making Analysis to Rural Spatial Sustainability Evaluation: A Systematic Review," IJERPH, MDPI, vol. 19(11), pages 1-31, May.
- Kamil Makieła & Błażej Mazur & Jakub Głowacki, 2022. "The Impact of Renewable Energy Supply on Economic Growth and Productivity," Energies, MDPI, vol. 15(13), pages 1-13, June.
- Bartłomiej Mroczek & Paweł Pijarski, 2022. "Machine Learning in Operating of Low Voltage Future Grid," Energies, MDPI, vol. 15(15), pages 1-30, July.
- Maximilian Gasser & Simon Pezzutto & Wolfram Sparber & Eric Wilczynski, 2022. "Public Research and Development Funding for Renewable Energy Technologies in Europe: A Cross-Country Analysis," Sustainability, MDPI, vol. 14(9), pages 1-28, May.
- Erik Möllerström, 2022. "Energy—History and Time Trends: Special Issue Editorial," Energies, MDPI, vol. 15(15), pages 1-3, July.
- Feng, Chenlong & Liu, Chao & Jiang, Dongxiang, 2023. "Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning," Renewable Energy, Elsevier, vol. 206(C), pages 309-323.
- Lv, Yunlong & Hu, Qin & Xu, Hang & Lin, Huiyao & Wu, Yufan, 2024. "An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model," Energy, Elsevier, vol. 293(C).
- Phong B. Dao, 2023. "On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data," Energies, MDPI, vol. 16(5), pages 1-17, March.
- Liu, Dayong, 2023. "Does green finance and natural resources agglomeration have potential for green economic growth? Evidence from Asian perspective," Resources Policy, Elsevier, vol. 84(C).
- Alan Turnbull & Conor McKinnon & James Carrol & Alasdair McDonald, 2022. "On the Development of Offshore Wind Turbine Technology: An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market," Energies, MDPI, vol. 15(9), pages 1-20, April.
- Zhang, Rongxin & Aljumah, Ahmad Ibrahim & Ghardallou, Wafa & Li, Zeyun & Li, Jinhua & Cifuentes-Faura, Javier, 2023. "How economic development promotes the sustainability targets? Role of natural resources utilization," Resources Policy, Elsevier, vol. 85(PA).
- Anna Adamik & Michał Nowicki & Andrius Puksas, 2022. "Energy Oriented Concepts and Other SMART WORLD Trends as Game Changers of Co-Production—Reality or Future?," Energies, MDPI, vol. 15(11), pages 1-38, June.
- Li, Ding & Zhang, Yufei & Yang, Zheng & Jin, Yaohui & Xu, Yanyan, 2024. "Sensing anomaly of photovoltaic systems with sequential conditional variational autoencoder," Applied Energy, Elsevier, vol. 353(PA).
More about this item
Keywords
wind turbine; main bearing; condition monitoring; attention mechanism; Bi-LSTM;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8462-:d:970850. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.