{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T10:03:47Z","timestamp":1766311427292,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,6,28]],"date-time":"2019-06-28T00:00:00Z","timestamp":1561680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["DPI2015-69891-C2-1-R\/2-R"],"award-info":[{"award-number":["DPI2015-69891-C2-1-R\/2-R"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["DPI2015-69891-C2-1-R\/2-R"],"award-info":[{"award-number":["DPI2015-69891-C2-1-R\/2-R"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Chillers are commonly used for thermal regulation to maintain indoor comfort in medium and large buildings. However, inefficiencies in this process produce significant losses, and optimization tasks are limited because of accessibility to the system. Data analysis techniques transform measurements coming from several sensors into useful information. Recent deep learning approaches have achieved excellent results in many applications. These techniques can be used for computing new data representations that provide comprehensive information from the device. This allows real-time monitoring, where information can be checked with current working operation to detect any type of anomaly in the process. In this work, a model based on a 1D convolutional neural network is proposed for fusing data in order to predict four different control stages of a screw compressor in a chiller. The evaluation of the method was performed using real data from a chiller in a hospital building. Results show a satisfactory performance and acceptable training time in comparison with other recent methods. In addition, the model is capable of predicting control states of other screw compressors different than the one used in the training. Furthermore, two failure cases are simulated, providing an early alarm detection when a continuous wrong classification is performed by the model.<\/jats:p>","DOI":"10.3390\/s19132868","type":"journal-article","created":{"date-parts":[[2019,6,28]],"date-time":"2019-06-28T11:20:26Z","timestamp":1561720826000},"page":"2868","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Deep Learning Approach for Fusing Sensor Data from Screw Compressors"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3467-4938","authenticated-orcid":false,"given":"Seraf\u00edn","family":"Alonso","sequence":"first","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Supervisi\u00f3n, Control y Automatizaci\u00f3n de Procesos Industriales (SUPPRESS), Esc. de Ing. Industrial, Inform\u00e1tica y Aeroespacial, Universidad de Le\u00f3n, Campus de Vegazana s\/n, 24007 Le\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2173-3364","authenticated-orcid":false,"given":"Daniel","family":"P\u00e9rez","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Supervisi\u00f3n, Control y Automatizaci\u00f3n de Procesos Industriales (SUPPRESS), Esc. de Ing. Industrial, Inform\u00e1tica y Aeroespacial, Universidad de Le\u00f3n, Campus de Vegazana s\/n, 24007 Le\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2762-6949","authenticated-orcid":false,"given":"Antonio","family":"Mor\u00e1n","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Supervisi\u00f3n, Control y Automatizaci\u00f3n de Procesos Industriales (SUPPRESS), Esc. de Ing. Industrial, Inform\u00e1tica y Aeroespacial, Universidad de Le\u00f3n, Campus de Vegazana s\/n, 24007 Le\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9023-0341","authenticated-orcid":false,"given":"Juan Jos\u00e9","family":"Fuertes","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Supervisi\u00f3n, Control y Automatizaci\u00f3n de Procesos Industriales (SUPPRESS), Esc. de Ing. Industrial, Inform\u00e1tica y Aeroespacial, Universidad de Le\u00f3n, Campus de Vegazana s\/n, 24007 Le\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0420-2315","authenticated-orcid":false,"given":"Ignacio","family":"D\u00edaz","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, University of Oviedo, Edif. Departmental 2, Campus de Viesques s\/n, 33204 Gij\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3921-1599","authenticated-orcid":false,"given":"Manuel","family":"Dom\u00ednguez","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Supervisi\u00f3n, Control y Automatizaci\u00f3n de Procesos Industriales (SUPPRESS), Esc. de Ing. Industrial, Inform\u00e1tica y Aeroespacial, Universidad de Le\u00f3n, Campus de Vegazana s\/n, 24007 Le\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/MIE.2017.2649104","article-title":"The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0","volume":"11","author":"Wollschlaeger","year":"2017","journal-title":"IEEE Ind. Electron. 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