A deep boosted transfer learning method for wind turbine gearbox fault detection
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DOI: 10.1016/j.renene.2022.07.117
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References listed on IDEAS
- Verstraeten, Timothy & Nowé, Ann & Keller, Jonathan & Guo, Yi & Sheng, Shuangwen & Helsen, Jan, 2019. "Fleetwide data-enabled reliability improvement of wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 428-437.
- Jialin Li & Xueyi Li & David He & Yongzhi Qu, 2020. "A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network," Journal of Risk and Reliability, , vol. 234(1), pages 168-182, February.
- Irawan, Chandra Ade & Ouelhadj, Djamila & Jones, Dylan & Stålhane, Magnus & Sperstad, Iver Bakken, 2017. "Optimisation of maintenance routing and scheduling for offshore wind farms," European Journal of Operational Research, Elsevier, vol. 256(1), pages 76-89.
- Helsen, J. & Devriendt, C. & Weijtjens, W. & Guillaume, P., 2016. "Experimental dynamic identification of modeshape driving wind turbine grid loss event on nacelle testrig," Renewable Energy, Elsevier, vol. 85(C), pages 259-272.
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Cited by:
- 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.
- Kangji Li & Borui Wei & Qianqian Tang & Yufei Liu, 2022. "A Data-Efficient Building Electricity Load Forecasting Method Based on Maximum Mean Discrepancy and Improved TrAdaBoost Algorithm," Energies, MDPI, vol. 15(23), pages 1-18, November.
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Keywords
Fault detection; Deep transfer learning; Vibrations; Gearbox;All these keywords.
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