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A fault detection system for a geothermal heat exchanger sensor based on intelligent techniques
dc.contributor.author | Alaiz Moretón, Héctor | |
dc.contributor.author | Castejón Limas, Manuel | |
dc.contributor.author | Casteleiro-Roca, José-Luis | |
dc.contributor.author | Jove, Esteban | |
dc.contributor.author | Fernández-Robles, Laura | |
dc.contributor.author | Calvo-Rolle, José Luis | |
dc.date.accessioned | 2019-10-09T15:38:13Z | |
dc.date.available | 2019-10-09T15:38:13Z | |
dc.date.issued | 2019-06-18 | |
dc.identifier.citation | Aláiz-Moretón, H.; Castejón-Limas, M.; Casteleiro-Roca, J.-L.; Jove, E.; Fernández Robles, L.; Calvo-Rolle, J.L. A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques. Sensors 2019, 19, 2740. | es_ES |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/2183/24075 | |
dc.description.abstract | [Abstract ]:This 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. | es_ES |
dc.description.sponsorship | Junta de Castilla y León; LE078G18. UXXI2018/000149. U-220. | es_ES |
dc.description.sponsorship | Ministerio de Economía, Industria y Competitividad; DPI2016-79960-C3-2-P | |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation.uri | https://doi.org/10.3390/s19122740 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights | Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/es/ | * |
dc.subject | Fault detection | es_ES |
dc.subject | Geothermal heat exchanger | es_ES |
dc.subject | Random decision forests | es_ES |
dc.subject | Gradient boosting | es_ES |
dc.subject | Extremely randomized trees | es_ES |
dc.subject | Adaptive boosting | es_ES |
dc.subject | K-nearest neighbors | es_ES |
dc.subject | Shallow neural networks | es_ES |
dc.subject | Detección de fallos | es_ES |
dc.subject | Intercambiador de calor geotérmico | es_ES |
dc.subject | Bosque de decisión aleatoria | es_ES |
dc.subject | Potenciación del gradiente | es_ES |
dc.subject | Árboles extremadamente aleatorios | es_ES |
dc.subject | K vecinos más cercanos | es_ES |
dc.subject | Redes neuronales poco profundas | es_ES |
dc.title | A fault detection system for a geothermal heat exchanger sensor based on intelligent techniques | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Sensors | es_ES |
UDC.volume | 19 | es_ES |
UDC.issue | 12 | es_ES |
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