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Convolutional neural network for wind turbine failure classification based on scada data
ArticleAbstract: As a renewable energy source and an alternative to fossil fuels, the wind power industry is growingPalabras claves:Convolutional neural network, Fast, Fault detection and classification, SCADA data, Wind turbineAutores:BRYAN JOAO PURUNCAJAS MAZA, Christian Tutivén Gálvez, Encalada-Dávila , Vidal Y., William ÁlavaFuentes:googlescopusDamage Detection in an Offshore Experimental Wind Turbine Foundation based on Autoencoder Deep Neural Networks
Conference ObjectAbstract: The rapid growth of the energy capacity generated by wind turbines (WT), as well as their size, creaPalabras claves:ADNN, damage detection, dynamic response, SHM, vibrational response, wind turbinesAutores:Christian Tutivén Gálvez, María del Cisne Feijóo, Vidal Y., Yovana ZambranoFuentes:scopusDamage Detection on Offshore Wind Turbine Jacket Foundations Based on an AutoEncoder
Conference ObjectAbstract: This work addresses the problem of damage detection on offshore wind turbine jacket-type foundationsPalabras claves:Autores:Ángel Encalada-Dávila, Benalcázar-Parra C., BRYAN JOAO PURUNCAJAS MAZA, Christian Tutivén Gálvez, Felipe González, Vidal Y.Fuentes:googlescopusEarly Fault Diagnosis Strategy for WT Main Bearings Based on SCADA Data and One-Class SVM
ArticleAbstract: To reduce the levelized cost of wind energy, through the reduction in operation and maintenance costPalabras claves:Anomaly detection, condition monitoring, Condition-Based Maintenance, Fault diagnosis, Main bearing, One-Class Support Vector Machine, pbkp_redictive maintenance, SCADA data, Wind turbineAutores:Achicanoy W., Christian Tutivén Gálvez, Insuasty A., Lorena Campoverde-Vilela, Vidal Y.Fuentes:scopusDetecting bearing failures in wind energy parks: A main bearing early damage detection method using SCADA data and a convolutional autoencoder
ArticleAbstract: Wind energy maintenance and operation costs can total millions of dollars each year in an average inPalabras claves:Autoencoder, Fault Detection, Wind turbineAutores:Ángel Encalada-Dávila, Benalcázar-Parra C., Christian Tutivén Gálvez, Vidal Y.Fuentes:googlescopusDetection of Jacket Offshore Wind Turbine Structural Damage using an 1D-Convolutional Neural Network with a Support Vector Machine Layer
Conference ObjectAbstract: Because offshore wind turbines, particularly their foundations, operate in hostile environments, impPalabras claves:Autores:Benalcázar C., Christian Tutivén Gálvez, Sueanny Moreno, Vidal Y.Fuentes:googlescopusMulti-fault diagnosis for wind turbines based on principal component analysis and support vector machines
Conference ObjectAbstract: The reliability requirements of wind turbine (WT) components have increased significantly in recentPalabras claves:Autores:Christian Tutivén Gálvez, Pozo F., Vidal Y.Fuentes:googlescopusUnsupervised damage detection for offshore jacket wind turbine foundations based on an autoencoder neural network
ArticleAbstract: Structural health monitoring for offshore wind turbine foundations is paramount to the further develPalabras claves:Autoencoder, Damage diagnosis, Offshore founda-tion, Offshore wind turbine, Structural Health MonitoringAutores:Christian Tutivén Gálvez, María Del Cisne Feijóo, Vidal Y., Yovana ZambranoFuentes:googlescopusScada data-driven wind turbine main bearing fault prognosis based on one-class support vector machines
ArticleAbstract: This work proposes a fault prognosis methodology to predict the main bearing fault several months inPalabras claves:Fault prognosis, Main bearing, SCADA data, Wind turbineAutores:Christian Tutivén Gálvez, Insuasty A., Vidal Y.Fuentes:googlescopusSiamese Neural Networks for Damage Detection and Diagnosis of Jacket-Type Offshore Wind Turbine Platforms
ArticleAbstract: Offshore wind energy is increasingly being realized at deeper ocean depths where jacket foundationsPalabras claves:Convolutional neural network, damage detection, Damage diagnosis, Data-driven, Jacket structure, Offshore fixed wind turbine, Siamese neural network, Vibration-based SHMAutores:BRYAN JOAO PURUNCAJAS MAZA, Christian Tutivén Gálvez, Joseph Baquerizo, Sampietro J., Vidal Y.Fuentes:scopus