144959 20260217205554.0 doi 10.3390/app14167256 sideral 139728 ART-2024-139728 eng Lázaro, Roberto A Robust Wind Turbine Component Health Status Indicator 2024 Access copy available to the general public Unrestricted Wind 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. info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 2.5 2024 ENGINEERING, MULTIDISCIPLINARY 50 / 179 = 0.279 2024 Q2 T1 CHEMISTRY, MULTIDISCIPLINARY 123 / 239 = 0.515 2024 Q3 T2 MATERIALS SCIENCE, MULTIDISCIPLINARY 284 / 461 = 0.616 2024 Q3 T2 PHYSICS, APPLIED 101 / 187 = 0.54 2024 Q3 T2 0.521 2024 Computer Science Applications 2024 Q2 Engineering (miscellaneous) 2024 Q2 Process Chemistry and Technology 2024 Q2 Instrumentation 2024 Q2 Materials Science (miscellaneous) 2024 Q2 Fluid Flow and Transfer Processes 2024 Q2 5.5 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Melero, Julio J. Universidad de Zaragoza (orcid)0000-0003-2360-0845 Yürüsen, Nurseda Y. (orcid)0000-0002-1206-9756 5009 535 Universidad de Zaragoza Dpto. Ingeniería Eléctrica Área Ingeniería Eléctrica 14, 16 (2024), 7256 [29 pp.] Appl. sci. Applied Sciences (Switzerland) 2076-3417 7856541 http://zaguan.unizar.es/record/144959/files/texto_completo.pdf Versión publicada 2612909 http://zaguan.unizar.es/record/144959/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:144959 articulos driver 2026-02-17-20:41:51 ARTICLE