A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques - Héctor Aláiz-Moretón; Manuel Castejón-Limas; José-Luis Casteleiro-Roca; Esteban Jove; Laura Fernández Robles; José Luis Calvo-Rolle (Sensors, 2019)
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.