Loading...
Loading...

Go to the content (press return)

A damage classification approach for structural health monitoring using machine learning

Author
Tibaduiza, D.A.; Torres-Arredondo, M.A.; Vitola, J.; Anaya, M.; Pozo, F.
Type of activity
Journal article
Journal
Complexity
Date of publication
2018-12-02
Volume
2018
First page
1
Last page
14
DOI
https://doi.org/10.1155/2018/5081283 Open in new window
Project funding
Development and validation of failure detection and design of fault-tolerant control strategies with application in offshore wind energy plants
Development and validation of intelligent monitoring systems, pitch and structural damping control strategies for floating offshore wind turbines
Repository
http://hdl.handle.net/2117/125815 Open in new window
Abstract
Inspection strategies with guided wave-based approaches give to structural health monitoring (SHM) applications several advantages, among them, the possibility of the use of real data from the structure which enables continuous monitoring and online damage identification. These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal...
Citation
Tibaduiza, D.A., Torres-Arredondo, M.A., Vitola, J., Anaya, M., Pozo, F. A damage classification approach for structural health monitoring using machine learning. "Complexity", 2 Desembre 2018, vol. 2018, p. 1-14.
Group of research
CoDAlab - Control, Dynamics and Applications

Participants

  • Tibaduiza Burgos, Diego Alexander  (author)
  • Torres-Arredondo, Miguel Ángel  (author)
  • Vitola Oyaga, Jaime  (author)
  • Anaya Vejar, Maribel  (author)
  • Pozo Montero, Francesc  (author)

Attachments