Articles | Volume 8, issue 4
Research article
14 Apr 2023
Research article |  | 14 Apr 2023

Anomaly-based fault detection in wind turbine main bearings

Lorena Campoverde-Vilela, María del Cisne Feijóo, Yolanda Vidal, José Sampietro, and Christian Tutivén

Related subject area

Thematic area: Materials and operation | Topic: Structural monitoring and testing
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Matthias Stammler
Wind Energ. Sci., 8, 1821–1837,,, 2023
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Cited articles

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Astolfi, D., Castellani, F., and Natili, F.: Wind turbine generator slip ring damage detection through temperature data analysis, Diagnostyka, 20, 3–9,, 2019. a
Bahar, K. P., Yıldız, G. B., and Soylu, B.: Predictive Maintenance System Integrated with Periodic Maintenance: Machine Learning and Classical Approaches, EasyChair, 5806 pp., (last access: April 2023), 2021. a
Baloch, Z. A., Tan, Q., Kamran, H. W., Nawaz, M. A., Albashar, G., and Hameed, J.: A multi-perspective assessment approach of renewable energy production: policy perspective analysis, Environ. Dev. Sustain., 24, 2164–2192,, 2022. a
Borchersen, A. B. and Kinnaert, M.: Model‐based fault detection for generator cooling system in wind turbines using SCADA data, Wind Energy, 19, 593–606,, 2016. a
Short summary
In order to provide early warnings of faults in the main bearing, a fault detection system is developed by applying an anomaly detector based on principal component analysis. Without the need to obtain the fault history or install additional equipment or sensors that would require a larger investment, this model is constructed using only healthy supervisory control and data acquisition (SCADA) data. The results obtained enable failure detection even months before the fatal breakdown takes place.
Final-revised paper