{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T08:53:12Z","timestamp":1771231992870,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,12]],"date-time":"2024-10-12T00:00:00Z","timestamp":1728691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Predictive maintenance has gained importance due to industrialization. Harnessing advanced technologies like sensors and data analytics enables proactive interventions, preventing unplanned downtime, reducing costs, and enhancing workplace safety. They play a crucial role in optimizing industrial operations, ensuring the efficiency, reliability, and longevity of equipment, which have become increasingly vital in the context of industrialization. The analysis of time series\u2019 stationarity is a powerful and agnostic approach to studying variations and trends that may indicate imminent failures in equipment, thus contributing to the effectiveness of predictive maintenance in industrial environments. The present paper explores the use of the Augmented Dickey\u2013Fuller p-value temporal variation as a possible method for determining trends in sensor time series and thus anticipating possible failures of a wood chip pump in the paper industry.<\/jats:p>","DOI":"10.3390\/a17100455","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T07:47:05Z","timestamp":1728892025000},"page":"455","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5380-7587","authenticated-orcid":false,"given":"Dinis","family":"Falc\u00e3o","sequence":"first","affiliation":[{"name":"Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9431-0570","authenticated-orcid":false,"given":"Francisco","family":"Reis","sequence":"additional","affiliation":[{"name":"Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-8079","authenticated-orcid":false,"given":"Jos\u00e9","family":"Farinha","sequence":"additional","affiliation":[{"name":"Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal"},{"name":"RCM<sup>2+<\/sup> Research Centre for Asset Management and Systems Engineering, ISEC\/IPC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8237-3086","authenticated-orcid":false,"given":"Nuno","family":"Lavado","sequence":"additional","affiliation":[{"name":"Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal"},{"name":"RCM<sup>2+<\/sup> Research Centre for Asset Management and Systems Engineering, ISEC\/IPC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4313-7966","authenticated-orcid":false,"given":"Mateus","family":"Mendes","sequence":"additional","affiliation":[{"name":"Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal"},{"name":"RCM<sup>2+<\/sup> Research Centre for Asset Management and Systems Engineering, ISEC\/IPC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11:121","DOI":"10.1147\/JRD.2017.2648298","article-title":"Predictive maintenance based on event-log analysis: A case study","volume":"61","author":"Wang","year":"2017","journal-title":"IBM J. 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