Articles | Volume 9, issue 1
https://doi.org/10.5194/wes-9-181-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.Active trailing edge flap system fault detection via machine learning
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Thematic area: Materials and operation | Topic: Operation and maintenance, condition monitoring, reliability
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2024Wind Energ. Sci. Discuss.,
2023Revised manuscript accepted for WES
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2023Cited articles
Abdallah, Imad Chatzi, E.: Probabilistic fault diagnostics using ensemble time-varying decision tree learning, Zenodo, https://doi.org/10.5281/zenodo.3474633, 2019. a, b
Badihi, H., Zhang, Y., Jiang, B., Pillay, P., and Rakheja, S.: A Comprehensive Review on Signal-Based and Model-Based Condition Monitoring of Wind Turbines: Fault Diagnosis and Lifetime Prognosis, Proc. IEEE, 110, 754–806, https://doi.org/10.1109/JPROC.2022.3171691, 2022. a
Barlas, T., Pettas, V., Gertz, D., and Madsen, H. A.: Extreme load alleviation using industrial implementation of active trailing edge flaps in a full design load basis, J. Phys.: Conf. Ser., 753, 17426596, https://doi.org/10.1088/1742-6596/753/4/042001, 2016. a
Bir, G.: Multi-blade coordinate transformation and its application to wind turbine analysis, in: 46th AIAA Aerospace Sciences Meeting and Exhibit, 7 January 2008–10 January 2008 Reno, Nevada, 2008–1300, https://doi.org/10.2514/6.2008-1300, 2008. a
Breiman, L.: Bagging predictors, Mach. Learn., 24, 123–140, 1996. a