Anomaly-based fault detection in wind turbine main bearings
Lorena Campoverde-Vilela,María del Cisne Feijóo,Yolanda Vidal,José Sampietro,and Christian Tutivén
Lorena Campoverde-Vilela
Mechatronic Engineering, Faculty of Mechanical Engineering and Production Science (FIMCP), Escuela Superior Politécnica del Litoral, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador
María del Cisne Feijóo
Mechatronic Engineering, Faculty of Mechanical Engineering and Production Science (FIMCP), Escuela Superior Politécnica del Litoral, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador
Control, Data, and Artificial Intelligence (CoDAlab), Department of Mathematics, Escola d'Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besós (CDB), Eduard Maristany 16, 08019 Barcelona, Spain
Institute of Mathematics (IMTech), Universitat Politècnica de Catalunya (UPC), Pau Gargallo 14, 08028 Barcelona, Spain
José Sampietro
Facultad de Ingenierías, Universidad Ecotec, Km. 13.5 Samborondón, Samborondón, EC092302, Ecuador
Christian Tutivén
Mechatronic Engineering, Faculty of Mechanical Engineering and Production Science (FIMCP), Escuela Superior Politécnica del Litoral, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador
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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.
In order to provide early warnings of faults in the main bearing, a fault detection system is...
