{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T07:20:34Z","timestamp":1771658434808,"version":"3.50.1"},"reference-count":18,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,14]],"date-time":"2020-04-14T00:00:00Z","timestamp":1586822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Heating appliances consume approximately \r\n          \r\n            \r\n              \r\n                48\r\n                %\r\n              \r\n            \r\n          \r\n         of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment\u2019s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction\/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data.<\/jats:p>","DOI":"10.3390\/info11040208","type":"journal-article","created":{"date-parts":[[2020,4,15]],"date-time":"2020-04-15T04:01:46Z","timestamp":1586923306000},"page":"208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0030-7155","authenticated-orcid":false,"given":"Sofia","family":"Fernandes","sequence":"first","affiliation":[{"name":"DETI, Universidade de Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6504-9441","authenticated-orcid":false,"given":"M\u00e1rio","family":"Antunes","sequence":"additional","affiliation":[{"name":"DETI, Universidade de Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1211-303X","authenticated-orcid":false,"given":"Ana Rita","family":"Santiago","sequence":"additional","affiliation":[{"name":"DETI, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5029-6191","authenticated-orcid":false,"given":"Jo\u00e3o Paulo","family":"Barraca","sequence":"additional","affiliation":[{"name":"DETI, Universidade de Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5848-2802","authenticated-orcid":false,"given":"Diogo","family":"Gomes","sequence":"additional","affiliation":[{"name":"DETI, Universidade de Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0107-6253","authenticated-orcid":false,"given":"Rui L.","family":"Aguiar","sequence":"additional","affiliation":[{"name":"DETI, Universidade de Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.engappai.2018.11.007","article-title":"Industry 4.0: A bibliometric analysis and detailed overview","volume":"78","author":"Muhuri","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Reis, M., and Gins, G. (2017). Industrial Process Monitoring in the Big Data\/Industry 4.0 Era: From Detection, to Diagnosis, to Prognosis. Processes, 5.","DOI":"10.3390\/pr5030035"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106024","DOI":"10.1016\/j.cie.2019.106024","article-title":"A systematic literature review of machine learning methods applied to predictive maintenance","volume":"137","author":"Carvalho","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_4","unstructured":"Ribeiro, J., Antunes, M., Gomes, D., and Aguiar, R.L. (2018, January 19\u201321). Outlier Identification in Multivariate Time Series: Boilers Case Study. Proceedings of the International Conference on Time Series and Forecasting (ITISE), Granada, Spain."},{"key":"ref_5","unstructured":"Satta, R., Cavallari, S., Pomponi, E., Grasselli, D., Picheo, D., and Annis, C. (2017). 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