{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:02:23Z","timestamp":1762956143936,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,18]],"date-time":"2019-06-18T00:00:00Z","timestamp":1560816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s19122740","type":"journal-article","created":{"date-parts":[[2019,6,19]],"date-time":"2019-06-19T02:42:46Z","timestamp":1560912166000},"page":"2740","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6572-1261","authenticated-orcid":false,"given":"H\u00e9ctor","family":"Al\u00e1iz-Moret\u00f3n","sequence":"first","affiliation":[{"name":"Departamento de Ingenier\u00eda de Sistemas y Autom\u00e1tica, Universidad de Le\u00f3n, 24071 Le\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5152-4555","authenticated-orcid":false,"given":"Manuel","family":"Castej\u00f3n-Limas","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00edas Mec\u00e1nica, Inform\u00e1tica y Aeroespacial, Universidad de Le\u00f3n, 24071 Le\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9740-6477","authenticated-orcid":false,"given":"Jos\u00e9-Luis","family":"Casteleiro-Roca","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Industrial, Universidade da Coru\u00f1a, 15405 Ferrol, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0625-359X","authenticated-orcid":false,"given":"Esteban","family":"Jove","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Industrial, Universidade da Coru\u00f1a, 15405 Ferrol, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6573-8477","authenticated-orcid":false,"given":"Laura","family":"Fern\u00e1ndez Robles","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00edas Mec\u00e1nica, Inform\u00e1tica y Aeroespacial, Universidad de Le\u00f3n, 24071 Le\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2333-8405","authenticated-orcid":false,"given":"Jos\u00e9 Luis","family":"Calvo-Rolle","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Industrial, Universidade da Coru\u00f1a, 15405 Ferrol, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kaltschmitt, M., Streicher, W., and Wiese, A. 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