Detection of crack bar deterioration at offshore wind turbine supports using generative adversarial networks and autoencoders

被引：0
|
作者：
Prieto-Galarza, Ricardo [1 ,2 ]
Tutivén, Christian [3 ]
Vidal, Yolanda [1 ,4 ]
机构：
[1] Control, Data and Artificial Intelligence (CoDAlab), Department of Mathematics, Escola d'Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besós (CDB), Eduard Maristany, 16, Barcelona,08019, Spain
[2] Universidad Ecotec, Km. 13.5 Samborondón, Samborondón,EC092302, Ecuador
[3] ESPOL Polytechnic University, Escuela Superior Politécnica Del Litoral, Faculty of Mechanical Engineering and Production Science (FIMCP), Mechatronic Engineering, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador
[4] Institute of Mathematics (IMTech), Universitat Politècnica de Catalunya (UPC), Pau Gargallo 14, Barcelona,08028, Spain
来源：
Journal of Physics: Conference Series | 2024年 / 2647卷 / 18期
关键词：
Accelerometer data - Anomaly detection models - Auto encoders - Input sample - Mechanism-based - Network models - Network training - Response mechanisms - Training phasis - Training process;
D O I：
182010
中图分类号：
学科分类号：
摘要：
14
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