{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T22:15:47Z","timestamp":1781820947718,"version":"3.54.5"},"reference-count":42,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T00:00:00Z","timestamp":1616457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010198","name":"Ministerio de Econom\u00eda, Industria y Competitividad, Gobierno de Espa\u00f1a","doi-asserted-by":"publisher","award":["DPI2017-82930-C2-1-R"],"award-info":[{"award-number":["DPI2017-82930-C2-1-R"]}],"id":[{"id":"10.13039\/501100010198","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002943","name":"Departament d'Innovaci\u00f3, Universitats i Empresa, Generalitat de Catalunya","doi-asserted-by":"publisher","award":["2017 SGR 388"],"award-info":[{"award-number":["2017 SGR 388"]}],"id":[{"id":"10.13039\/501100002943","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s21062228","type":"journal-article","created":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T23:59:41Z","timestamp":1616543981000},"page":"2228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6259-8464","authenticated-orcid":false,"given":"\u00c1ngel","family":"Encalada-D\u00e1vila","sequence":"first","affiliation":[{"name":"Mechatronics Engineering, Faculty of Mechanical Engineering and Production Science (FIMCP), Campus Gustavo Galindol, ESPOL Polytechnic University, Escuela Superior Polit\u00e9cnica del Litoral, ESPOL, Km. 30.5 V\u00eda Perimetral, Guayaquil 090112, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2194-6853","authenticated-orcid":false,"given":"Bryan","family":"Puruncajas","sequence":"additional","affiliation":[{"name":"Mechatronics Engineering, Faculty of Mechanical Engineering and Production Science (FIMCP), Campus Gustavo Galindol, ESPOL Polytechnic University, Escuela Superior Polit\u00e9cnica del Litoral, ESPOL, Km. 30.5 V\u00eda Perimetral, Guayaquil 090112, Ecuador"},{"name":"Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d\u2019Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Bes\u00f3s (CDB), Universitat Polit\u00e8cnica de Catalunya (UPC), Eduard Maristany, 16, 08019 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6322-4608","authenticated-orcid":false,"given":"Christian","family":"Tutiv\u00e9n","sequence":"additional","affiliation":[{"name":"Mechatronics Engineering, Faculty of Mechanical Engineering and Production Science (FIMCP), Campus Gustavo Galindol, ESPOL Polytechnic University, Escuela Superior Polit\u00e9cnica del Litoral, ESPOL, Km. 30.5 V\u00eda Perimetral, Guayaquil 090112, Ecuador"},{"name":"Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d\u2019Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Bes\u00f3s (CDB), Universitat Polit\u00e8cnica de Catalunya (UPC), Eduard Maristany, 16, 08019 Barcelona, Spain"},{"name":"Facultad de Ingenier\u00edas, Universidad ECOTEC, Km. 13.5 V\u00eda a Samborond\u00f3n, Guayaquil 092302, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4964-6948","authenticated-orcid":false,"given":"Yolanda","family":"Vidal","sequence":"additional","affiliation":[{"name":"Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d\u2019Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Bes\u00f3s (CDB), Universitat Polit\u00e8cnica de Catalunya (UPC), Eduard Maristany, 16, 08019 Barcelona, Spain"},{"name":"Institute of Mathematics (IMTech), Universitat Polit\u00e8cnica de Catalunya (UPC), Pau Gargallo 14, 08028 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,23]]},"reference":[{"key":"ref_1","unstructured":"Europe, W. (2020). Wind Energy in Europe in 2019\u2014Trends and Statistics, Wind Europe."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"110301","DOI":"10.1016\/j.rser.2020.110301","article-title":"A comprehensive review of variable renewable energy levelized cost of electricity","volume":"133","author":"Shen","year":"2020","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tang, M., Zhao, Q., Ding, S.X., Wu, H., Li, L., Long, W., and Huang, B. (2020). An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes. Energies, 13.","DOI":"10.3390\/en13040807"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.ymssp.2017.12.035","article-title":"Wind turbine fault detection and classification by means of image texture analysis","volume":"107","author":"Ruiz","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pozo, F., Vidal, Y., and Serrahima, J.M. (2016). On real-time fault detection in wind turbines: Sensor selection algorithm and detection time reduction analysis. Energies, 9.","DOI":"10.3390\/en9070520"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hossain, M.L., Abu-Siada, A., and Muyeen, S. (2018). Methods for advanced wind turbine condition monitoring and early diagnosis: A literature review. Energies, 11.","DOI":"10.3390\/en11051309"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.3390\/jmse3031027","article-title":"Wind turbine blade life-time assessment model for preventive planning of operation and maintenance","volume":"3","author":"Florian","year":"2015","journal-title":"J. Mar. Sci. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.renene.2016.06.048","article-title":"An experimental study of acoustic emission methodology for in service condition monitoring of wind turbine blades","volume":"99","author":"Tang","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/TSTE.2017.2719626","article-title":"Fault prognosis and remaining useful life prediction of wind turbine gearboxes using current signal analysis","volume":"9","author":"Cheng","year":"2017","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dupuis, R. (2010, January 10\u201316). Application of oil debris monitoring for wind turbine gearbox prognostics and health management. Proceedings of the Annual Conference of the Prognostics and Health Management Society, Portland, OR, USA.","DOI":"10.36001\/phmconf.2010.v2i1.1867"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1468","DOI":"10.1109\/TMECH.2020.2978136","article-title":"An integrated feature-based failure prognosis method for wind turbine bearings","volume":"25","author":"Rezamand","year":"2020","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108154","DOI":"10.1016\/j.measurement.2020.108154","article-title":"ANFIS system for prognosis of dynamometer high-speed ball bearing based on frequency domain acoustic emission signals","volume":"166","author":"Jafari","year":"2020","journal-title":"Measurement"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.apacoust.2016.07.026","article-title":"A comparative study of the effectiveness of vibration and acoustic emission in diagnosing a defective bearing in a planetry gearbox","volume":"115","author":"Elasha","year":"2017","journal-title":"Appl. Acoust."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Leahy, K., Gallagher, C., O\u2019Donovan, P., and O\u2019Sullivan, D.T. (2019). Issues with data quality for wind turbine condition monitoring and reliability analyses. Energies, 12.","DOI":"10.3390\/en12020201"},{"key":"ref_15","first-page":"1","article-title":"Diagnosing and predicting wind turbine faults from SCADA data using support vector machines","volume":"9","author":"Leahy","year":"2018","journal-title":"Int. J. Progn. Health Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6863","DOI":"10.1016\/j.eswa.2013.06.018","article-title":"Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS","volume":"40","author":"Chen","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Leahy, K., Gallagher, C., O\u2019Donovan, P., Bruton, K., and O\u2019Sullivan, D.T. (2018). A robust prescriptive framework and performance metric for diagnosing and predicting wind turbine faults based on SCADA and alarms data with case study. Energies, 11.","DOI":"10.3390\/en11071738"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Theodoropoulos, P., Spandonidis, C.C., Themelis, N., Giordamlis, C., and Fassois, S. (2021). Evaluation of Different Deep-Learning Models for the Prediction of a Ship\u2019s Propulsion Power. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9020116"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, J., Wang, J., Zhu, J., Lee, T.H., and De Silva, C.C. (2020). Unsupervised Cross-domain Fault Diagnosis Using Feature Representation Alignment Networks for Rotating Machinery. IEEE\/ASME Trans. Mechatron.","DOI":"10.1109\/TMECH.2020.3046277"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105","DOI":"10.5194\/wes-5-105-2020","article-title":"A review of wind turbine main bearings: Design, operation, modelling, damage mechanisms and fault detection","volume":"5","author":"Hart","year":"2020","journal-title":"Wind. Energy Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vidal, Y., Pozo, F., and Tutiv\u00e9n, C. (2018). Wind turbine multi-fault detection and classification based on SCADA data. Energies, 11.","DOI":"10.3390\/en11113018"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1109\/TSTE.2020.2989220","article-title":"Condition Monitoring of Wind Turbine Generators Using SCADA Data Analysis","volume":"12","author":"Jin","year":"2020","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Leahy, K., Hu, R.L., Konstantakopoulos, I.C., Spanos, C.J., and Agogino, A.M. (2016, January 20\u201322). Diagnosing wind turbine faults using machine learning techniques applied to operational data. Proceedings of the 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), Ottawa, ON, Canada.","DOI":"10.1109\/ICPHM.2016.7542860"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Hu, W., Dong, W., Gao, Z., and Ren, Z. (2017). Structural reliability analysis of wind turbines: A review. Energies, 10.","DOI":"10.3390\/en10122099"},{"key":"ref_25","first-page":"4797","article-title":"Analysis of dynamic load characteristics on hydrostatic bearing with variable viscosity and temperature using simulation technique","volume":"6","author":"Srinivasan","year":"2013","journal-title":"Indian J. Sci. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hamadache, M., and Lee, D. (2016, January 16\u201319). Wind turbine main bearing fault detection via shaft speed signal analysis under constant load. Proceedings of the 2016 16th International Conference on Control, Automation and Systems (ICCAS), Gyeongju, Korea.","DOI":"10.1109\/ICCAS.2016.7832512"},{"key":"ref_27","unstructured":"(2021, January 24). Bearing Damage and Failure Analysis. Available online: https:\/\/www.skf.com\/binaries\/pub12\/Images\/0901d1968064c148-Bearing-failures---14219_2-EN_tcm_12-297619.pdf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1260\/030952406777641441","article-title":"Influence of wind speed on wind turbine reliability","volume":"30","author":"Tavner","year":"2006","journal-title":"Wind Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.envsoft.2018.05.002","article-title":"Effects of the pre-processing algorithms in fault diagnosis of wind turbines","volume":"110","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_30","first-page":"9","article-title":"Missing data imputation: Focusing on single imputation","volume":"4","author":"Zhang","year":"2016","journal-title":"Ann. Transl. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2304","DOI":"10.1109\/TAES.2018.2814278","article-title":"Novel High-Precision Simulation Technology for High-Dynamics Signal Simulators Based on Piecewise Hermite Cubic Interpolation","volume":"54","author":"Lu","year":"2018","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_32","first-page":"1787","article-title":"The performance of adaptive tuning piecewise cubic hermite interpolation model for signal-to-noise ratio estimation","volume":"14","author":"Sim","year":"2018","journal-title":"Int. J. Innov. Comput. Inf. Control."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107622","DOI":"10.1016\/j.cpc.2020.107622","article-title":"CIMBA: Fast Monte Carlo generation using cubic interpolation","volume":"258","author":"Ilten","year":"2021","journal-title":"Comput. Phys. Commun."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.amc.2018.04.008","article-title":"An improved EMD method with modified envelope algorithm based on C2 piecewise rational cubic spline interpolation for EMI signal decomposition","volume":"335","author":"Li","year":"2018","journal-title":"Appl. Math. Comput."},{"key":"ref_35","unstructured":"Foresee, F.D., and Hagan, M.T. (1997, January 12). Gauss-Newton approximation to Bayesian learning. Proceedings of the International Conference on Neural Networks (ICNN\u201997), Houston, TX, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Leahy, K., Gallagher, C.V., Bruton, K., O\u2019Donovan, P., and O\u2019Sullivan, D.T. (2017). Automatically identifying and predicting unplanned wind turbine stoppages using scada and alarms system data: Case study and results. Journal of Physics: Conference Series, IOP Publishing.","DOI":"10.1088\/1742-6596\/926\/1\/012011"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"McKinnon, C., Turnbull, A., Koukoura, S., Carroll, J., and McDonald, A. (2020). Effect of time history on normal behaviour modelling using SCADA data to predict wind turbine failures. Energies, 13.","DOI":"10.3390\/en13184745"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1090\/qam\/10666","article-title":"A method for the solution of certain non-linear problems in least squares","volume":"2","author":"Levenberg","year":"1944","journal-title":"Q. Appl. Math."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1137\/0111030","article-title":"An algorithm for least-squares estimation of nonlinear parameters","volume":"11","author":"Marquardt","year":"1963","journal-title":"J. Soc. Ind. Appl. Math."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1162\/neco.1992.4.3.415","article-title":"Bayesian interpolation","volume":"4","author":"MacKay","year":"1992","journal-title":"Neural Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s11063-018-9883-8","article-title":"Enhance the performance of deep neural networks via L2 regularization on the input of activations","volume":"50","author":"Shi","year":"2019","journal-title":"Neural Process. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1109\/72.329697","article-title":"Training multilayer networks with the Marquardt algorithm","volume":"5","author":"Hagen","year":"1994","journal-title":"IEEE Trans. Neural Netw."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/2228\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:39:31Z","timestamp":1760161171000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/2228"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,23]]},"references-count":42,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["s21062228"],"URL":"https:\/\/doi.org\/10.3390\/s21062228","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,23]]}}}