Next Article in Journal
Behaviour Classification on Giraffes (Giraffa camelopardalis) Using Machine Learning Algorithms on Triaxial Acceleration Data of Two Commonly Used GPS Devices and Its Possible Application for Their Management and Conservation
Next Article in Special Issue
Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network
Previous Article in Journal
A Deep Learning Framework for Recognizing Both Static and Dynamic Gestures
Previous Article in Special Issue
An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing

Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data

Mechatronics Engineering, Faculty of Mechanical Engineering and Production Science (FIMCP), Campus Gustavo Galindol, ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, ESPOL, Km. 30.5 Vía Perimetral, Guayaquil 090112, Ecuador
Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besós (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany, 16, 08019 Barcelona, Spain
Facultad de Ingenierías, Universidad ECOTEC, Km. 13.5 Vía a Samborondón, Guayaquil 092302, Ecuador
Institute of Mathematics (IMTech), Universitat Politècnica de Catalunya (UPC), Pau Gargallo 14, 08028 Barcelona, Spain
Author to whom correspondence should be addressed.
Academic Editor: Steven Chatterton
Sensors 2021, 21(6), 2228;
Received: 4 March 2021 / Revised: 15 March 2021 / Accepted: 20 March 2021 / Published: 23 March 2021
(This article belongs to the Special Issue Sensors for Wind Turbine Fault Diagnosis and Prognosis)
As stated by the European Academy of Wind Energy (EAWE), the wind industry has identified main bearing failures as a critical issue in terms of increasing wind turbine reliability and availability. This is owing to major repairs with high replacement costs and long downtime periods associated with main bearing failures. Thus, the main bearing fault prognosis has become an economically relevant topic and is a technical challenge. In this work, a data-based methodology for fault prognosis is presented. The main contributions of this work are as follows: (i) Prognosis is achieved by using only supervisory control and data acquisition (SCADA) data, which is already available in all industrial-sized wind turbines; thus, no extra sensors that are designed for a specific purpose need to be installed. (ii) The proposed method only requires healthy data to be collected; thus, it can be applied to any wind farm even when no faulty data has been recorded. (iii) The proposed algorithm works under different and varying operating and environmental conditions. (iv) The validity and performance of the established methodology is demonstrated on a real underproduction wind farm consisting of 12 wind turbines. The obtained results show that advanced prognostic systems based solely on SCADA data can predict failures several months prior to their occurrence and allow wind turbine operators to plan their operations. View Full-Text
Keywords: fault prognosis; wind turbine; main bearing; normality model; real SCADA data fault prognosis; wind turbine; main bearing; normality model; real SCADA data
Show Figures

Figure 1

MDPI and ACS Style

Encalada-Dávila, Á.; Puruncajas, B.; Tutivén, C.; Vidal, Y. Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data. Sensors 2021, 21, 2228.

AMA Style

Encalada-Dávila Á, Puruncajas B, Tutivén C, Vidal Y. Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data. Sensors. 2021; 21(6):2228.

Chicago/Turabian Style

Encalada-Dávila, Ángel, Bryan Puruncajas, Christian Tutivén, and Yolanda Vidal. 2021. "Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data" Sensors 21, no. 6: 2228.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop