{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T04:16:45Z","timestamp":1778559405387,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T00:00:00Z","timestamp":1606262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The intelligent condition monitoring of wind turbines reduces their downtime and increases reliability. In this manuscript, a feature selection-based methodology that essentially works on regression models is used for identifying faulty scenarios. Supervisory control and data acquisition (SCADA) data with 1009 samples from one year and one month before failure are considered. Gearbox oil and bearing temperatures are treated as target variables with all the other variables used for the prediction model. Neighborhood component analysis (NCA) as a feature selection technique is employed to select the best features and prediction performance for several machine learning regression models is assessed. The results reveal that twin support vector regression (99.91%) and decision trees (98.74%) yield the highest accuracy for gearbox oil and bearing temperatures respectively. It is observed that NCA increases the accuracy and thus reliability of the condition monitoring system. Furthermore, the residuals from the class of support vector regression (SVR) models are tested from a statistical point of view. Diebold\u2013Mariano and Durbin\u2013Watson tests are carried out to establish the robustness of the tested models.<\/jats:p>","DOI":"10.3390\/s20236742","type":"journal-article","created":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T08:59:15Z","timestamp":1606294755000},"page":"6742","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Wind Turbine Gearbox Condition Monitoring Based on Class of Support Vector Regression Models and Residual Analysis"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7394-7102","authenticated-orcid":false,"given":"Harsh S.","family":"Dhiman","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Adani Institute of Infrastructure Engineering, Ahmedabad 382421, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4419-4516","authenticated-orcid":false,"given":"Dipankar","family":"Deb","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Carroll","sequence":"additional","affiliation":[{"name":"Department of Electronics and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vlad","family":"Muresan","sequence":"additional","affiliation":[{"name":"Department of Automation, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9193-6741","authenticated-orcid":false,"given":"Mihaela-Ligia","family":"Unguresan","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109912","DOI":"10.1016\/j.rser.2020.109912","article-title":"Wake management based life enhancement of battery energy storage system for hybrid wind farms","volume":"130","author":"Dhiman","year":"2020","journal-title":"Renew. 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