A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

• Main Authors: Héctor Aláiz-Moretón, Manuel Castejón-Limas, José-Luis Casteleiro-Roca, Esteban Jove, Laura Fernández Robles, José Luis Calvo-Rolle
• Format: Article
• Language: English
• Published: MDPI AG 2019-06-01
• Series: Sensors
• Subjects: fault detection; geothermal heat exchanger; random decision forests; gradient boosting; extremely randomized trees; adaptive boosting; k-nearest neighbors; shallow neural networks
• Online Access: https://www.mdpi.com/1424-8220/19/12/2740

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.