Ensemble learning as approach for pipeline condition assessment

12th International Conference on Damage Assessment of Structures

Camacho-Navarro, Jhonatan^1 ; Ruiz, Magda^1 ; Villamizar, Rodolfo^2 ; Mujica, Luis^1 ; Moreno-Beltrán, Gustavo^2

Universitat Politécnica de Catalunya (UPC) BARCELONATECH, Department of Mathematics, Escola d'Enginyeria de Barcelona Est. (EEBE), Campus Diagonal Besòs, Edifici A, C. Eduard Maristany, 10-14, Barcelona

08019, Spain^1

Universidad Industrial de Santander (UIS), Escuela de Ingenierías Eléctrica, Electrónica y de Telecomunicaciones (E3T), Bucaramanga, Colombia^2

关键词: Condition assessments;    Damage condition monitoring;    Damage localization;    Decision algorithms;    Piezoelectric sensors;    Pipeline condition assessments;    Preliminary algorithms;    Structural damages;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/842/1/012019/pdf
DOI  :  10.1088/1742-6596/842/1/012019

来源: IOP

【 摘 要 】

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 T2and 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.