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Ensemble learning as approach for pipeline condition assessment

Author
Camacho, J.; Ruiz, M.; Villamizar, R.; Mujica, L.E.; Moreno, G.
Type of activity
Presentation of work at congresses
Name of edition
12th International Conference on Damage Assessment of Structures
Date of publication
2017
Presentation's date
2017-07-11
Book of congress proceedings
DAMAS 2017: 12th International Conference on Damage Assessment of Structures : Kitakyushu, Japó: July 10-12, 2017: proceedings book
First page
1
Last page
9
Publisher
Ghent University
Project funding
Development and validation of failure detection and design of fault-tolerant control strategies with application in offshore wind energy plants
Repository
http://hdl.handle.net/2117/105753 Open in new window
Abstract
The algorithms commonly used for damage condition monitoring present several drawbacks related to unbalanced data, optimal training requirements, low capability to manage feature diversity and low tolerance to errors. In this work, an approach based on ensemble learning is discussed as alternative to obtain more efficient diagnosis. The main advantage of ensemble learning is the use of several algorithms at the same time for a better proficiency. Thereby, combining simplest tree decision algorit...
Citation
Camacho-Navarro, J., Ruiz, M., Villamizar, R., Mujica, L.E., Moreno, G. Ensemble learning as approach for pipeline condition assessment. A: International Conference on Damage Assessment of Structures. "DAMAS 2017: 12th International Conference on Damage Assessment of Structures : Kitakyushu, Japó: July 10-12, 2017: proceedings book". Kitakyushu: Ghent University, 2017, p. 1-9.
Group of research
CoDAlab - Control, Dynamics and Applications

Participants

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