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Título
A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Sensors
Número de la revista
12
Cita Bibliográfica
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
Editorial
MDPI
Fecha
2019
Resumen
[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.
Materia
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SI
URI
DOI
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