Exploratory analysis of SCADA data fromwind turbines using the K-means clustering algorithm for predictive maintenance purposes

Product maintenance costs throughout the product’s lifetime can account for between 30–60% of total operating costs, making it necessary to implement maintenance strategies. This problem not only affects the economy but is also related to the impact on the environment, since breakdowns are also resp...

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Guardado en:
Detalles Bibliográficos
Autores principales: Cosa Rodriguez, Pablo, Marti Puig, Pere, Caiafa, Cesar Federico, Serra Serra, Moisès, Cusidó, Jordi, Solé Casals, Jordi
Formato: Articulo
Lenguaje:Inglés
Publicado: 2023
Materias:
Ingeniería
Informática
Predictive maintenance
Prognosis
Machine learning
K-means
Clustering
SCADA data
Renewable energies
Wind turbine
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/152530
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata

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