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Online structural damage classification methodology for offshore wind turbine foundations using data stream analysis
Structural health monitoring (SHM) of wind turbines is crucial to improve maintenance and extend their lifespan. This study develops an online data analysis methodology using data stream analysis to classify damage in the links of an offshore wind turbine foundation. The methodology is validated using a laboratory-scaled jacket-type wind turbine foundation structure. 2460 measurements of the healthy structure were acquired, and a 5mm crack was applied to four different links to determine the four unhealthy classes. 820 measurements were taken for each of the unhealthy structures, resulting in a dataset with 5740 instances. As this is an imbalanced multiclass classification problem, a random sampler approach was used to treat the data. The only data obtained was from eight triaxial accelerometers distributed throughout the structure. Three different tree-based stream data classifiers were compared: Hoeffding Tree classifier, Extremely Fast Decision Tree classifier, and Hoeffding Adaptive Tree classifier. Each classification model underwent a tuning parameter procedure, and high values of the receiving operating characteristic area under the curve (ROC AUC) metric were achieved as a result. It is important to note that stream learning differs from batch learning. ; Peer Reviewed ; Postprint (published version)
Online structural damage classification methodology for offshore wind turbine foundations using data stream analysis
Structural health monitoring (SHM) of wind turbines is crucial to improve maintenance and extend their lifespan. This study develops an online data analysis methodology using data stream analysis to classify damage in the links of an offshore wind turbine foundation. The methodology is validated using a laboratory-scaled jacket-type wind turbine foundation structure. 2460 measurements of the healthy structure were acquired, and a 5mm crack was applied to four different links to determine the four unhealthy classes. 820 measurements were taken for each of the unhealthy structures, resulting in a dataset with 5740 instances. As this is an imbalanced multiclass classification problem, a random sampler approach was used to treat the data. The only data obtained was from eight triaxial accelerometers distributed throughout the structure. Three different tree-based stream data classifiers were compared: Hoeffding Tree classifier, Extremely Fast Decision Tree classifier, and Hoeffding Adaptive Tree classifier. Each classification model underwent a tuning parameter procedure, and high values of the receiving operating characteristic area under the curve (ROC AUC) metric were achieved as a result. It is important to note that stream learning differs from batch learning. ; Peer Reviewed ; Postprint (published version)
Online structural damage classification methodology for offshore wind turbine foundations using data stream analysis
Leon Medina, Jersson Xavier (Autor:in) / Parés Mariné, Núria (Autor:in) / Pozo Montero, Francesc (Autor:in) / Universitat Politècnica de Catalunya. Departament de Matemàtiques / Universitat Politècnica de Catalunya. LACÀN - Mètodes Numèrics en Ciències Aplicades i Enginyeria / Universitat Politècnica de Catalunya. CoDAlab - Control, Dades i Intel·ligència Artificial
01.01.2023
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic , Machine learning , Wind turbines , Stream learning , Online data analysis , Structural health monitoring , Wind turbine foundation , Hoeffding Tree classifier , Data stream analysis , Vibration analysis , Condition monitoring , Aprenentatge automàtic , Aerogeneradors
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