An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm
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- Yunfan Yang & Caiping Xi & Ke Feng, 2022. "Rolling Bearing Fault Diagnosis Based on MFDFA-SPS and ELM," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-17, July.
- Renström, Niklas & Bangalore, Pramod & Highcock, Edmund, 2020. "System-wide anomaly detection in wind turbines using deep autoencoders," Renewable Energy, Elsevier, vol. 157(C), pages 647-659.
- Conor McKinnon & James Carroll & Alasdair McDonald & Sofia Koukoura & David Infield & Conaill Soraghan, 2020. "Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data," Energies, MDPI, vol. 13(19), pages 1-19, October.
- Kerman López de Calle & Susana Ferreiro & Constantino Roldán-Paraponiaris & Alain Ulazia, 2019. "A Context-Aware Oil Debris-Based Health Indicator for Wind Turbine Gearbox Condition Monitoring," Energies, MDPI, vol. 12(17), pages 1-19, September.
- Lei Fu & Yanding Wei & Sheng Fang & Xiaojun Zhou & Junqiang Lou, 2017. "Condition Monitoring for Roller Bearings of Wind Turbines Based on Health Evaluation under Variable Operating States," Energies, MDPI, vol. 10(10), pages 1-21, October.
- Le Zhang & Qiang Yang, 2020. "Investigation of the Design and Fault Prediction Method for an Abrasive Particle Sensor Used in Wind Turbine Gearbox," Energies, MDPI, vol. 13(2), pages 1-13, January.
- Cristian Velandia-Cardenas & Yolanda Vidal & Francesc Pozo, 2021. "Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data," Energies, MDPI, vol. 14(6), pages 1-26, March.
- Chen, Bin & Xie, Lei & Li, Yongzhan & Gao, Baocheng, 2020. "Acoustical damage detection of wind turbine yaw system using Bayesian network," Renewable Energy, Elsevier, vol. 160(C), pages 1364-1372.
- Zhaoyan Zhang & Shaoke Wang & Peiguang Wang & Ping Jiang & Hang Zhou, 2022. "Research on Fault Early Warning of Wind Turbine Based on IPSO-DBN," Energies, MDPI, vol. 15(23), pages 1-18, November.
- Qu, Fuming & Liu, Jinhai & Zhu, Hongfei & Zhou, Bowen, 2020. "Wind turbine fault detection based on expanded linguistic terms and rules using non-singleton fuzzy logic," Applied Energy, Elsevier, vol. 262(C).
- Yang, Wenguang & Liu, Chao & Jiang, Dongxiang, 2018. "An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring," Renewable Energy, Elsevier, vol. 127(C), pages 230-241.
- Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
- Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
- Chen, Junsheng & Li, Jian & Chen, Weigen & Wang, Youyuan & Jiang, Tianyan, 2020. "Anomaly detection for wind turbines based on the reconstruction of condition parameters using stacked denoising autoencoders," Renewable Energy, Elsevier, vol. 147(P1), pages 1469-1480.
- Tao, Tao & Liu, Yongqian & Qiao, Yanhui & Gao, Linyue & Lu, Jiaoyang & Zhang, Ce & Wang, Yu, 2021. "Wind turbine blade icing diagnosis using hybrid features and Stacked-XGBoost algorithm," Renewable Energy, Elsevier, vol. 180(C), pages 1004-1013.
- Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
- 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.
- Pinjia Zhang & Delong Lu, 2019. "A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines," Energies, MDPI, vol. 12(14), pages 1-22, July.
- Fryzlewicz, Piotr, 2014. "Wild binary segmentation for multiple change-point detection," LSE Research Online Documents on Economics 57146, London School of Economics and Political Science, LSE Library.
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Keywords
wind turbine; fault detection; self-attention; gated recurrent unit; changepoint detection;All these keywords.
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