Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors
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DOI: 10.1016/j.renene.2021.10.024
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- Kirill A. Bashmur & Oleg A. Kolenchukov & Vladimir V. Bukhtoyarov & Vadim S. Tynchenko & Sergei O. Kurashkin & Elena V. Tsygankova & Vladislav V. Kukartsev & Roman B. Sergienko, 2022. "Biofuel Technologies and Petroleum Industry: Synergy of Sustainable Development for the Eastern Siberian Arctic," Sustainability, MDPI, vol. 14(20), pages 1-25, October.
- Xie, Tianming & Xu, Qifa & Jiang, Cuixia & Lu, Shixiang & Wang, Xiangxiang, 2023. "The fault frequency priors fusion deep learning framework with application to fault diagnosis of offshore wind turbines," Renewable Energy, Elsevier, vol. 202(C), pages 143-153.
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Keywords
Bearing; Wind turbine; Convolutional neural network; Fault diagnosis; Information fusion;All these keywords.
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