Articles | Volume 9, issue 1
Research article
22 Jan 2024
Research article |  | 22 Jan 2024

Active trailing edge flap system fault detection via machine learning

Andrea Gamberini and Imad Abdallah

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Revised manuscript accepted for WES
Short summary

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Cited articles

Abdallah, Imad Chatzi, E.: Probabilistic fault diagnostics using ensemble time-varying decision tree learning, Zenodo,, 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,, 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,, 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,, 2008. a
Breiman, L.: Bagging predictors, Mach. Learn., 24, 123–140, 1996. a
Short summary
Active trailing edge flaps can potentially reduce wind turbine (WT) loads. To monitor their performance, we present two methods based on machine learning that identify flap health states, including degraded performance, in normal power production and idling condition. Both methods rely only on sensors commonly available on WTs. One approach properly detects all the flap states if a fault occurs on only one blade. The other approach can identify two specific flap states in all fault scenarios.
Final-revised paper