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dc.contributor | Escuela de Ingenierias Industrial, Informática y Aeroespacial | es_ES |
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.contributor.other | Ingenieria de Sistemas y Automatica | es_ES |
dc.date | 2019 | |
dc.date.accessioned | 2024-06-03T09:26:38Z | |
dc.date.available | 2024-06-03T09:26:38Z | |
dc.identifier.citation | Aláiz-Moretón, H., Castejón-Limas, M., Casteleiro-Roca, J.-L., Jove, E., Fernández Robles, L., & Calvo-Rolle, J. L. (2019). A fault detection system for a geothermal heat exchanger sensor based on intelligent techniques. Sensors (Switzerland), 19(12). https://doi.org/10.3390/S19122740 | es_ES |
dc.identifier.other | https://www.mdpi.com/1424-8220/19/12/2740 | es_ES |
dc.identifier.uri | https://hdl.handle.net/10612/21141 | |
dc.description.abstract | [EN] 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.language | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Ingeniería de sistemas | es_ES |
dc.subject.other | Fault detection | es_ES |
dc.subject.other | Geothermal heat exchanger | es_ES |
dc.subject.other | Random decision forests | es_ES |
dc.subject.other | Gradient boostings | es_ES |
dc.subject.other | Extremely randomized trees | es_ES |
dc.subject.other | Adaptive boosting | es_ES |
dc.subject.other | K-nearest neighbors | es_ES |
dc.subject.other | Shallow neural networks | 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.identifier.doi | 10.3390/s19122740 | |
dc.description.peerreviewed | SI | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.essn | 1424-8220 | |
dc.journal.title | Sensors | es_ES |
dc.volume.number | 19 | es_ES |
dc.issue.number | 12 | es_ES |
dc.page.initial | 2740 | es_ES |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es_ES |
dc.subject.unesco | 3313 Tecnología E Ingeniería Mecánicas | es_ES |
dc.description.project | Junta de Castilla y León ,Consejería de Educación( Proyecto LE078G18. UXXI2018/000149. U-220) | es_ES |
dc.description.project | Ministerio de Economía, Industria y Competitividad ( Proyecto grant DPI2016-79960-C3-2-P) | es_ES |
dc.description.project | NVIDIA GPU | es_ES |
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