000144959 001__ 144959 000144959 005__ 20240920135617.0 000144959 0247_ $$2doi$$a10.3390/app14167256 000144959 0248_ $$2sideral$$a139728 000144959 037__ $$aART-2024-139728 000144959 041__ $$aeng 000144959 100__ $$aLázaro, Roberto 000144959 245__ $$aA Robust Wind Turbine Component Health Status Indicator 000144959 260__ $$c2024 000144959 5060_ $$aAccess copy available to the general public$$fUnrestricted 000144959 5203_ $$aWind turbine components’ failure prognosis allows wind farm owners to apply predictive maintenance techniques to their fleets. Determining the health status of a turbine’s component typically requires verifying many variables that should be monitored simultaneously. The scope of this study is the selection of the more relevant variables and the generation of a health status indicator (Failure Index) to be considered as a decision criterion in Operation and Maintenance activities. The proposed methodology is based on Gaussian Mixture Copula Models (GMCMs) combined with a smoothing method (Cubic spline smoothing) to define a component’s health index based on the previous behavior and relationships between the considered variables. The GMCM allows for determining the component’s status in a multivariate environment, providing the selected variables’ joint probability and obtaining an easy-to-track univariate health status indicator. When the health of a component is degrading, anomalous behavior becomes apparent in certain Supervisory Control and Data Acquisition (SCADA) signals. By monitoring these SCADA signals using this indicator, the proposed anomaly detection method could capture the deviations from the healthy working state. The resulting indicator shows whether any failure is likely to occur in a wind turbine component and would aid in a preventive intervention scheduling. 000144959 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000144959 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000144959 700__ $$0(orcid)0000-0003-2360-0845$$aMelero, Julio J.$$uUniversidad de Zaragoza 000144959 700__ $$0(orcid)0000-0002-1206-9756$$aYürüsen, Nurseda Y. 000144959 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica 000144959 773__ $$g14, 16 (2024), 7256 [29 pp.]$$pAppl. sci.$$tApplied Sciences (Switzerland)$$x2076-3417 000144959 8564_ $$s7856541$$uhttps://zaguan.unizar.es/record/144959/files/texto_completo.pdf$$yVersión publicada 000144959 8564_ $$s2612909$$uhttps://zaguan.unizar.es/record/144959/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000144959 909CO $$ooai:zaguan.unizar.es:144959$$particulos$$pdriver 000144959 951__ $$a2024-09-20-13:01:23 000144959 980__ $$aARTICLE