Publications
2019
1.
Aláiz-Moretón, Héctor; Castejón-Limas, Manuel; Casteleiro-Roca, José-Luis; Jove, Esteban; Robles, Laura Fernández; Calvo-Rolle, José Luis
A fault detection system for a geothermal heat exchanger sensor based on intelligent techniques Artículo de revista
En: Sensors, vol. 19, no 12, pp. 2740, 2019, (Publisher: MDPI).
Resumen | Enlaces | BibTeX | Etiquetas: adaptative boosting, extremely randomized trees, fault detection, geothermal heat exchanger, gradient boosting, k-nearest neighbors, random decision forest, shallow neural networks
@article{alaiz-moreton_fault_2019,
title = {A fault detection system for a geothermal heat exchanger sensor based on intelligent techniques},
author = {Héctor Aláiz-Moretón and Manuel Castejón-Limas and José-Luis Casteleiro-Roca and Esteban Jove and Laura Fernández Robles and José Luis Calvo-Rolle},
url = {https://www.mdpi.com/1424-8220/19/12/2740},
year = {2019},
date = {2019-01-01},
journal = {Sensors},
volume = {19},
number = {12},
pages = {2740},
abstract = {his paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems.},
note = {Publisher: MDPI},
keywords = {adaptative boosting, extremely randomized trees, fault detection, geothermal heat exchanger, gradient boosting, k-nearest neighbors, random decision forest, shallow neural networks},
pubstate = {published},
tppubtype = {article}
}
his paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems.
2.
Moretón, Héctor Aláiz; Limas, Manuel Castejón; Roca, José-Luis Casteleiro; Jove, Esteban; Robles, Laura Fernández; Rolle, José Luis Calvo
A fault detection system for a geothermal heat exchanger sensor based on intelligent techniques Artículo de revista
En: Sensors, vol. 19, no 12, pp. 2740, 2019, (Publisher: MDPI).
Resumen | Enlaces | BibTeX | Etiquetas: adaptative boosting, extremely randomized trees, fault detection, geothermal heat exchanger, gradient boosting, k-nearest neighbors, random decision forest, shallow neural networks
@article{alaiz_moreton_fault_2019,
title = {A fault detection system for a geothermal heat exchanger sensor based on intelligent techniques},
author = {Héctor Aláiz Moretón and Manuel Castejón Limas and José-Luis Casteleiro Roca and Esteban Jove and Laura Fernández Robles and José Luis Calvo Rolle},
url = {https://www.mdpi.com/1424-8220/19/12/2740},
year = {2019},
date = {2019-01-01},
journal = {Sensors},
volume = {19},
number = {12},
pages = {2740},
abstract = {his paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems.},
note = {Publisher: MDPI},
keywords = {adaptative boosting, extremely randomized trees, fault detection, geothermal heat exchanger, gradient boosting, k-nearest neighbors, random decision forest, shallow neural networks},
pubstate = {published},
tppubtype = {article}
}
his paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems.