Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data
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 m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/6/2228 |
| _version_ | 1797540364222988288 |
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| author | Ángel Encalada-Dávila Bryan Puruncajas Christian Tutivén Yolanda Vidal |
| author_facet | Ángel Encalada-Dávila Bryan Puruncajas Christian Tutivén Yolanda Vidal |
| author_sort | Ángel Encalada-Dávila |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:00:01Z |
| format | Article |
| id | doaj.art-7317eece3a344ae4bae6682d05635aef |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:00:01Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7317eece3a344ae4bae6682d05635aef2023-11-21T11:36:08ZengMDPI AGSensors1424-82202021-03-01216222810.3390/s21062228Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA DataÁngel Encalada-Dávila0Bryan Puruncajas1Christian Tutivén2Yolanda Vidal3Mechatronics 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, EcuadorMechatronics 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, EcuadorMechatronics 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, EcuadorControl, 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 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.https://www.mdpi.com/1424-8220/21/6/2228fault prognosiswind turbinemain bearingnormality modelreal SCADA data |
| spellingShingle | Ángel Encalada-Dávila Bryan Puruncajas Christian Tutivén Yolanda Vidal Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data Sensors fault prognosis wind turbine main bearing normality model real SCADA data |
| title | Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data |
| title_full | Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data |
| title_fullStr | Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data |
| title_full_unstemmed | Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data |
| title_short | Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data |
| title_sort | wind turbine main bearing fault prognosis based solely on scada data |
| topic | fault prognosis wind turbine main bearing normality model real SCADA data |
| url | https://www.mdpi.com/1424-8220/21/6/2228 |
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