A feature extraction and machine learning framework for bearing fault diagnosis
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DOI: 10.1016/j.renene.2022.04.061
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- Helbing, Georg & Ritter, Matthias, 2018. "Deep Learning for fault detection in wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 189-198.
- Marvuglia, Antonino & Messineo, Antonio, 2012. "Monitoring of wind farms’ power curves using machine learning techniques," Applied Energy, Elsevier, vol. 98(C), pages 574-583.
- Tang, Baoping & Song, Tao & Li, Feng & Deng, Lei, 2014. "Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine," Renewable Energy, Elsevier, vol. 62(C), pages 1-9.
- Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
- Marino, Enzo & Giusti, Alessandro & Manuel, Lance, 2017. "Offshore wind turbine fatigue loads: The influence of alternative wave modeling for different turbulent and mean winds," Renewable Energy, Elsevier, vol. 102(PA), pages 157-169.
- Chandrasekhar, Kartik & Stevanovic, Nevena & Cross, Elizabeth J. & Dervilis, Nikolaos & Worden, Keith, 2021. "Damage detection in operational wind turbine blades using a new approach based on machine learning," Renewable Energy, Elsevier, vol. 168(C), pages 1249-1264.
- Abdalrahman, Gebreel & Melek, William & Lien, Fue-Sang, 2017. "Pitch angle control for a small-scale Darrieus vertical axis wind turbine with straight blades (H-Type VAWT)," Renewable Energy, Elsevier, vol. 114(PB), pages 1353-1362.
- Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
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Cited by:
- Adam Lundström & Mattias O’Nils, 2023. "Factory-Based Vibration Data for Bearing-Fault Detection," Data, MDPI, vol. 8(7), pages 1-9, June.
- Zhao, Zhigao & Chen, Fei & Gui, Zhonghua & Liu, Dong & Yang, Jiandong, 2023. "Refined composite hierarchical multiscale Lempel-Ziv complexity: A quantitative diagnostic method of multi-feature fusion for rotating energy devices," Renewable Energy, Elsevier, vol. 218(C).
- Tang, Yaochi & Chang, Yunchi & Li, Kuohao, 2023. "Applications of K-nearest neighbor algorithm in intelligent diagnosis of wind turbine blades damage," Renewable Energy, Elsevier, vol. 212(C), pages 855-864.
- Mehmet Akif Bütüner & İlhan Koşalay & Doğan Gezer, 2022. "Machine-Learning-Based Modeling of a Hydraulic Speed Governor for Anomaly Detection in Hydropower Plants," Energies, MDPI, vol. 15(21), pages 1-19, October.
- Zhao, Zhigao & Chen, Fei & He, Xianghui & Lan, Pengfei & Chen, Diyi & Yin, Xiuxing & Yang, Jiandong, 2024. "A universal hydraulic-mechanical diagnostic framework based on feature extraction of abnormal on-field measurements: Application in micro pumped storage system," Applied Energy, Elsevier, vol. 357(C).
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Keywords
Bearing fault diagnosis; Feature extraction; Machine learning; Nonstationary signals; Time-frequency analysis;All these keywords.
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