Articles | Volume 8, issue 4
https://doi.org/10.5194/wes-8-557-2023
https://doi.org/10.5194/wes-8-557-2023
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 authors

Fault Detection in Wind Turbines Using Health Index Monitoring with Variational Autoencoders
Shun Wang, Yolanda Vidal, and Francesc Pozo
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-123,https://doi.org/10.5194/wes-2025-123, 2025
Preprint under review for WES
Short summary

Related subject area

Thematic area: Materials and operation | Topic: Structural monitoring and testing
Dynamic displacement measurement of a wind turbine tower using accelerometers: tilt error compensation and validation
Clemens Jonscher, Paula Helming, David Märtins, Andreas Fischer, David Bonilla, Benedikt Hofmeister, Tanja Grießmann, and Raimund Rolfes
Wind Energ. Sci., 10, 193–205, https://doi.org/10.5194/wes-10-193-2025,https://doi.org/10.5194/wes-10-193-2025, 2025
Short summary
Wear test programs for roller-type pitch bearings of wind turbines
Matthias Stammler
Wind Energ. Sci., 8, 1821–1837, https://doi.org/10.5194/wes-8-1821-2023,https://doi.org/10.5194/wes-8-1821-2023, 2023
Short summary
Exploring limiting factors of wear in pitch bearings of wind turbines with real-scale tests
Karsten Behnke and Florian Schleich
Wind Energ. Sci., 8, 289–301, https://doi.org/10.5194/wes-8-289-2023,https://doi.org/10.5194/wes-8-289-2023, 2023
Short summary

Cited articles

Artigao, E., Martín-Martínez, S., Honrubia-Escribano, A., and Gómez-Lázaro, E.: Wind turbine: A comprehensive review towards effective condition monitoring development, Appl. Energy, 228, 1569–1583, https://doi.org/10.1016/j.apenergy.2018.07.037, 2018. a
Astolfi, D., Castellani, F., and Natili, F.: Wind turbine generator slip ring damage detection through temperature data analysis, Diagnostyka, 20, 3–9, https://doi.org/10.29354/diag/109968, 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., https://wvvw.easychair.org/publications/preprint_download/GsVf (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, https://doi.org/10.1007/s10668-021-01524-8, 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, https://doi.org/10.1002/we.1852, 2016. a
Download
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.
Share
Altmetrics
Final-revised paper
Preprint