A Bayesian approach for fatigue damage diagnosis and prognosis of wind turbine blades

Simple item page
dc.coverageDOI: 10.1016/j.ymssp.2022.109067
dc.creatorJaramillo, Francisco
dc.creatorGutiérrez, José Martín
dc.creatorOrchard, Marcos
dc.creatorGuarini, Marcelo
dc.creatorAstroza, Rodrigo
dc.date2022
dc.date.accessioned2025-11-18T19:41:54Z
dc.date.available2025-11-18T19:41:54Z
dc.description<p>This paper proposes a Bayesian framework based on particle filters for online fatigue damage diagnosis and prognosis for wind turbine blades (WTBs). The framework integrates theoretical and practical aspects with the purpose of developing a robust monitoring tool. Besides, a damage indicator based on identified modal frequencies of the WTB is defined to quantify the degree of damage to the monitored blade. Furthermore, feature extraction techniques on vibration signals are considered to obtain the online observations and inputs needed by the Bayesian framework. Experimental data collected from a fatigue test performed on a WTB was used to validate the proposed methodology. The results obtained by the damage diagnosis algorithm show the great potential of the Bayesian processor for damage estimation of the monitored WTB and uncertainty quantification related to the estimated variable. According to the damage prognosis results, the proposed algorithm could generate suitable long-term predictions of the damage indicator so as to estimate the time-of-failure (ToF) probability mass function (PMF) for the monitored WTB. Consequently, the computed ToF-PMF was able to include the experimental ToF within its 95% confidence interval, demonstrating the accuracy of the Bayesian approach.</p>eng
dc.identifierhttps://investigadores.uandes.cl/en/publications/3724305a-0ac5-4c57-b640-a80cde8f4a5e
dc.identifier.urihttps://repositorio.uandes.cl/handle/uandes/52065
dc.languageeng
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourcevol.174 (2022) date: 2022-07-15
dc.subjectBayesian estimation
dc.subjectDamage diagnosis
dc.subjectDamage prognosis
dc.subjectWind turbine blades
dc.titleA Bayesian approach for fatigue damage diagnosis and prognosis of wind turbine bladeseng
dc.typeArticleeng
dc.typeArtículospa
Files
Collections
PURE