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Unsupervised damage detection for offshore jacket wind turbine foundations based on an autoencoder neural network

Author
Feijóo, M.d.C; Zambrano, Y.; Vidal, Y.; Tutivén, C.
Type of activity
Journal article
Journal
Sensors
Date of publication
2021-05-11
Volume
21
Number
10
First page
3333:1
Last page
3333:17
DOI
10.3390/s21103333
Project funding
Development and validation of intelligent monitoring systems, pitch and structural damping control strategies for floating offshore wind turbines
Grup de recerca acreditat: Control, Modelització, Identificació i Aplicacions
Repository
http://hdl.handle.net/2117/347796 Open in new window
URL
https://www.mdpi.com/1424-8220/21/10/3333/htm Open in new window
Abstract
Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in large water depths. In this work, a jacket-type foundation damage diagnosis strategy is stated. Normally, most or all the available data are of regular operation, thus methods that focus on the data leading to failures end up using only a small subset of...
Citation
Feijóo, M. d. . [et al.]. Unsupervised damage detection for offshore jacket wind turbine foundations based on an autoencoder neural network. "Sensors", 11 Maig 2021, vol. 21, núm. 10, p. 3333:1-3333:17.
Keywords
Autoencoder, Damage diagnosis, Offshore foundation, Offshore wind turbine, Structural health monitoring
Group of research
CoDAlab - Control, Dynamics and Applications

Participants

  • Feijóo, Maria del Cisne  (author)
  • Zambrano, Yovana  (author)
  • Vidal Segui, Yolanda  (author)
  • Tutivén Gálvez, Christian  (author)

Attachments