{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T01:02:59Z","timestamp":1775178179256,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,21]],"date-time":"2021-08-21T00:00:00Z","timestamp":1629504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>The availability maximization is a goal for any organization because the equipment downtime implies high non-production costs and, additionally, the abnormal stopping and restarting usually imply loss of product\u2019s quality. In this way, a method for predicting the equipment\u2019s health state is vital to maintain the production flow as well as to plan maintenance intervention strategies. This paper presents a maintenance prediction approach based on sensing data managed by Hidden Markov Models (HMM). To do so, a diagnosis of drying presses in a pulp industry is used as case study, which is done based on data collected every minute for three years and ten months. This paper presents an approach to manage a multivariate analysis, in this case merging the values of sensors, and optimizing the observable states to insert into a HMM model, which permits to identify three hidden states that characterize the equipment\u2019s health state: \u201cProper Function\u201d, \u201cAlert state\u201d, and \u201cEquipment Failure\u201d. The research described in this paper demonstrates how an equipment health diagnosis can be made using the HMM, through the collection of observations from various sensors, without information of machine failures occurrences. The approach developed demonstrated to be robust, even the complexity of the system, having the potential to be generalized to any other type of equipment.<\/jats:p>","DOI":"10.3390\/app11167685","type":"journal-article","created":{"date-parts":[[2021,8,22]],"date-time":"2021-08-22T21:42:12Z","timestamp":1629668532000},"page":"7685","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Maintenance Prediction through Sensing Using Hidden Markov Models\u2014A Case Study"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9947-0894","authenticated-orcid":false,"given":"Alexandre","family":"Martins","sequence":"first","affiliation":[{"name":"EIGeS\u2014Research Centre in Industrial Engineering, Management and Sustainability, Lus\u00f3fona University, Campo Grande, 376, 1749-024 Lisboa, Portugal"},{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, Cal\u00e7ada Fonte do Lameiro, P-62001-001 Covilh\u00e3, Portugal"}]},{"given":"In\u00e1cio","family":"Fonseca","sequence":"additional","affiliation":[{"name":"ISEC\/IPC\u2014Polytechnic Institute of Coimbra, 3045-093 Coimbra, Portugal"}]},{"given":"Jos\u00e9 Torres","family":"Farinha","sequence":"additional","affiliation":[{"name":"ISEC\/IPC\u2014Polytechnic Institute of Coimbra, 3045-093 Coimbra, Portugal"},{"name":"CEMMPRE\u2014Centre for Mechanical Engineering, Materials and Processes, University of Coimbra, 3030-788 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8504-0065","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Reis","sequence":"additional","affiliation":[{"name":"EIGeS\u2014Research Centre in Industrial Engineering, Management and Sustainability, Lus\u00f3fona University, Campo Grande, 376, 1749-024 Lisboa, Portugal"},{"name":"GOVCOPP\u2014Department of Economics, Management, Industrial Engineering and Tourism, Campus Universit\u00e1rio de Santiago, Aveiro University, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8737-6999","authenticated-orcid":false,"given":"Ant\u00f3nio J. Marques","family":"Cardoso","sequence":"additional","affiliation":[{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, Cal\u00e7ada Fonte do Lameiro, P-62001-001 Covilh\u00e3, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,21]]},"reference":[{"key":"ref_1","first-page":"2224","article-title":"Optimizing the Life Cycle of Physical Assets\u2014A Review","volume":"15","author":"Pais","year":"2020","journal-title":"WSEAS Trans. Syst. Control"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"440","DOI":"10.17531\/ein.2020.3.6","article-title":"Predicting Motor Oil Condition Using Artificial Neural Networks and Principal Components Analysis","volume":"22","author":"Rodrigues","year":"2020","journal-title":"Eksploatacja i Niezawodnosc Maint. 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