Title:
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Ensemble learning as approach for pipeline condition assessment
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Author:
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Camacho-Navarro, Jhonatan; Ruiz Ordóñez, Magda; Villamizar Mejía, Rodolfo; Mujica Delgado, Luis Eduardo; Moreno Beltran, Gustavo Adolfo
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Matemàtiques; Universitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions |
Abstract:
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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 algorithms in bagging scheme, the accuracy of damage detection is improved. It takes advantage by combining prediction of preliminary algorithms based on regression models. The methodology is experimentally validated on a carbon steel pipe section, where mass adding conditions are studied as possible failures . Data from an active system based on piezoelectric sensors are stored and characterized through the T2 and Q statistical indexes. Then, they are the inputs to the ensemble learning. The proposed methodology allows determining the condition assessment and damage localizations in the structure. The results of the studied cases show the feasibility of ensemble learning for detecting occurrence of structural damages with successful results. |
Subject(s):
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-Àrees temàtiques de la UPC::Física -Structural engineering -Enginyeria d'estructures |
Rights:
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Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/ |
Document type:
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Article - Published version Article |
Published by:
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Institute of Physics (IOP)
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