Alaiz Moretón, HéctorCastejón Limas, ManuelCasteleiro-Roca, José-LuisJove, EstebanFernández-Robles, LauraCalvo-Rolle, José Luis2019-10-092019-10-092019-06-18Aláiz-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, 2740.1424-8220http://hdl.handle.net/2183/24075[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.engAtribución 3.0 EspañaCreative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).http://creativecommons.org/licenses/by/4.0/es/Fault detectionGeothermal heat exchangerRandom decision forestsGradient boostingExtremely randomized treesAdaptive boostingK-nearest neighborsShallow neural networksDetección de fallosIntercambiador de calor geotérmicoBosque de decisión aleatoriaPotenciación del gradienteÁrboles extremadamente aleatoriosK vecinos más cercanosRedes neuronales poco profundasjournal articleopen access