A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/12/2740 |
| _version_ | 1828152669691183104 |
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| author | Héctor Aláiz-Moretón Manuel Castejón-Limas José-Luis Casteleiro-Roca Esteban Jove Laura Fernández Robles José Luis Calvo-Rolle |
| author_facet | Héctor Aláiz-Moretón Manuel Castejón-Limas José-Luis Casteleiro-Roca Esteban Jove Laura Fernández Robles José Luis Calvo-Rolle |
| author_sort | Héctor Aláiz-Moretón |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:14:43Z |
| format | Article |
| id | doaj.art-69872cf2167a4dd28f4d79fcd1efc0b6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:14:43Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-69872cf2167a4dd28f4d79fcd1efc0b62022-12-22T04:00:28ZengMDPI AGSensors1424-82202019-06-011912274010.3390/s19122740s19122740A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent TechniquesHéctor Aláiz-Moretón0Manuel Castejón-Limas1José-Luis Casteleiro-Roca2Esteban Jove3Laura Fernández Robles4José Luis Calvo-Rolle5Departamento de Ingeniería de Sistemas y Automática, Universidad de León, 24071 León, SpainDepartamento de Ingenierías Mecánica, Informática y Aeroespacial, Universidad de León, 24071 León, SpainDepartamento de Ingeniería Industrial, Universidade da Coruña, 15405 Ferrol, SpainDepartamento de Ingeniería Industrial, Universidade da Coruña, 15405 Ferrol, SpainDepartamento de Ingenierías Mecánica, Informática y Aeroespacial, Universidad de León, 24071 León, SpainDepartamento de Ingeniería Industrial, Universidade da Coruña, 15405 Ferrol, SpainThis 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.https://www.mdpi.com/1424-8220/19/12/2740fault detectiongeothermal heat exchangerrandom decision forestsgradient boostingextremely randomized treesadaptive boostingk-nearest neighborsshallow neural networks |
| spellingShingle | Héctor Aláiz-Moretón Manuel Castejón-Limas José-Luis Casteleiro-Roca Esteban Jove Laura Fernández Robles José Luis Calvo-Rolle A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques Sensors fault detection geothermal heat exchanger random decision forests gradient boosting extremely randomized trees adaptive boosting k-nearest neighbors shallow neural networks |
| title | A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques |
| title_full | A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques |
| title_fullStr | A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques |
| title_full_unstemmed | A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques |
| title_short | A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques |
| title_sort | fault detection system for a geothermal heat exchanger sensor based on intelligent techniques |
| topic | fault detection geothermal heat exchanger random decision forests gradient boosting extremely randomized trees adaptive boosting k-nearest neighbors shallow neural networks |
| url | https://www.mdpi.com/1424-8220/19/12/2740 |
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