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Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data

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  1. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2023. "A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data," Renewable Energy, Elsevier, vol. 208(C), pages 420-430.
  2. Jin, Zhenglei & Xu, Qifa & Jiang, Cuixia & Wang, Xiangxiang & Chen, Hao, 2023. "Ordinal few-shot learning with applications to fault diagnosis of offshore wind turbines," Renewable Energy, Elsevier, vol. 206(C), pages 1158-1169.
  3. Abdelmoumen Saci & Mohamed Nadour & Lakhmissi Cherroun & Ahmed Hafaifa & Abdellah Kouzou & Jose Rodriguez & Mohamed Abdelrahem, 2024. "Condition Monitoring Using Digital Fault-Detection Approach for Pitch System in Wind Turbines," Energies, MDPI, vol. 17(16), pages 1-35, August.
  4. Zemali, Zakaria & Cherroun, Lakhmissi & Hadroug, Nadji & Hafaifa, Ahmed & Iratni, Abdelhamid & Alshammari, Obaid S. & Colak, Ilhami, 2023. "Robust intelligent fault diagnosis strategy using Kalman observers and neuro-fuzzy systems for a wind turbine benchmark," Renewable Energy, Elsevier, vol. 205(C), pages 873-898.
  5. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
  6. Rami Al-Hajj & Ali Assi & Bilel Neji & Raymond Ghandour & Zaher Al Barakeh, 2023. "Transfer Learning for Renewable Energy Systems: A Survey," Sustainability, MDPI, vol. 15(11), pages 1-28, June.
  7. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Song, Chaosheng & Chen, Dingliang & Zheng, Jie, 2022. "Fault detection of offshore wind turbine gearboxes based on deep adaptive networks via considering Spatio-temporal fusion," Renewable Energy, Elsevier, vol. 200(C), pages 1023-1036.
  8. Wang, Xiaomin & Zhuang, Xiao & Zhou, Di & Ge, Jian & Xiang, Jiawei, 2025. "A novel sparrow search algorithm based co-correlation graph construction strategy for wind turbine group anomaly identification via graph attention networks," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
  9. Zhou, Rui & Li, Yanting & Lin, Xinhua, 2025. "A clustered federated learning framework for collaborative fault diagnosis of wind turbines," Applied Energy, Elsevier, vol. 377(PB).
  10. 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.
  11. Wang, Shun & Vidal, Yolanda & Pozo, Francesc, 2026. "Recent advances in wind turbine condition monitoring using SCADA data: A state-of-the-art review," Reliability Engineering and System Safety, Elsevier, vol. 267(PA).
  12. Silvio Simani & Saverio Farsoni & Paolo Castaldi, 2023. "RETRACTED: Supervisory Control and Data Acquisition for Fault Diagnosis of Wind Turbines via Deep Transfer Learning," Energies, MDPI, vol. 16(9), pages 1-22, April.
  13. Ren, Xinyu & Zhao, Wanli & Liu, Mengmeng & Wang, Suixin & Shao, Haidong & Zhao, Ke, 2024. "Multi-source domain self-supervised enhanced transfer fault diagnosis approach with source sample refinement strategy," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
  14. Arturo Y. Jaen-Cuellar & David A. Elvira-Ortiz & Roque A. Osornio-Rios & Jose A. Antonino-Daviu, 2022. "Advances in Fault Condition Monitoring for Solar Photovoltaic and Wind Turbine Energy Generation: A Review," Energies, MDPI, vol. 15(15), pages 1-36, July.
  15. Li, Guannan & Chen, Liang & Liu, Jiangyan & Fang, Xi, 2023. "Comparative study on deep transfer learning strategies for cross-system and cross-operation-condition building energy systems fault diagnosis," Energy, Elsevier, vol. 263(PD).
  16. Gang Li & Chenbi Li & Chengli Wang & Zeheng Wang, 2024. "Suboptimal capability of individual machine learning algorithms in modeling small-scale imbalanced clinical data of local hospital," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-13, February.
  17. Dibaj, Ali & Gao, Zhen & Nejad, Amir R., 2023. "Fault detection of offshore wind turbine drivetrains in different environmental conditions through optimal selection of vibration measurements," Renewable Energy, Elsevier, vol. 203(C), pages 161-176.
  18. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Wang, Yili & Tao, Jianquan, 2022. "Operational state assessment of wind turbine gearbox based on long short-term memory networks and fuzzy synthesis," Renewable Energy, Elsevier, vol. 181(C), pages 1167-1176.
  19. Adaiton Oliveira-Filho & Monelle Comeau & James Cave & Charbel Nasr & Pavel Côté & Antoine Tahan, 2024. "Wind Turbine SCADA Data Imbalance: A Review of Its Impact on Health Condition Analyses and Mitigation Strategies," Energies, MDPI, vol. 18(1), pages 1-23, December.
  20. Zhang, Boyan & Rezgui, Yacine & Luo, Zhiwen & Zhao, Tianyi, 2024. "Fault detection research on novel transfer learning-based method for cross-condition, cross-system and cross-operation in public building HVAC sensors," Energy, Elsevier, vol. 313(C).
  21. Stefan Jonas & Dimitrios Anagnostos & Bernhard Brodbeck & Angela Meyer, 2023. "Vibration Fault Detection in Wind Turbines Based on Normal Behaviour Models without Feature Engineering," Energies, MDPI, vol. 16(4), pages 1-16, February.
  22. Shujie Yang & Peikun Yang & Hao Yu & Jing Bai & Wuwei Feng & Yuxiang Su & Yulin Si, 2022. "A 2DCNN-RF Model for Offshore Wind Turbine High-Speed Bearing-Fault Diagnosis under Noisy Environment," Energies, MDPI, vol. 15(9), pages 1-16, May.
  23. Peng Jieyang & Andreas Kimmig & Wang Dongkun & Zhibin Niu & Fan Zhi & Wang Jiahai & Xiufeng Liu & Jivka Ovtcharova, 2023. "A systematic review of data-driven approaches to fault diagnosis and early warning," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3277-3304, December.
  24. Ha, Byeongmin & Lee, Hyeonjeong & Hwangbo, Soonho, 2025. "Towards sustainable energy efficiency: Data-driven optimization in large-scale plants using machine learning applications," Energy, Elsevier, vol. 331(C).
  25. Jiang, Ruolin & Fang, Fang & Rodríguez-Andina, Juan José & Song, Ziqiu & Liu, Jizhen & Chen, Yuanye & Wang, Hua, 2026. "Artificial intelligence in wind turbine fault diagnosis: A systematic knowledge mapping and trend analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PD).
  26. Wang, Anqi & Pei, Yan & Zhu, Yunyi & Qian, Zheng, 2023. "Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern," Renewable Energy, Elsevier, vol. 211(C), pages 918-937.
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