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The diagnosis and prognosis of the health status of equipment, predictive maintenance (PdM), are fundamental strategies to perform informed maintenance, increasing the company\u2019s profit. This article aims to present a diagnosis and prognosis methodology using a hidden Markov model (HMM) classifier to recognise the equipment status in real time and a deep neural network (DNN), specifically a gated recurrent unit (GRU), to determine this same status in a future of one week. The data collected by the sensors go through several phases, starting by cleaning them. After that, temporal windows are created in order to generate statistical features of the time domain to better understand the equipment\u2019s behaviour. These features go through a normalisation to produce inputs for a feature extraction process, via a principal component analysis (PCA). After the dimensional reduction and obtaining new features with more information, a clustering is performed by the K-means algorithm, in order to group similar data. These clusters enter the HMM classifier as observable states. After training using the Baum\u2013Welch algorithm, the Viterbi algorithm is used to find the best path of hidden states that represent the diagnosis of the equipment, containing three states: state 1\u2014\u201cState of Good Operation\u201d; state 2\u2014\u201cWarning State\u201d; state 3\u2014\u201cFailure State\u201d. Once the equipment diagnosis is complete, the GRU model is used to predict the future, both of the observable states as well as the hidden states coming out from the HMM. Thus, through this network, it is possible to directly obtain the health states 7 days ahead, without the necessity to run the whole methodology from scratch.<\/jats:p>","DOI":"10.3390\/en16062651","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T03:28:33Z","timestamp":1678678113000},"page":"2651","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models"],"prefix":"10.3390","volume":"16","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, 62001-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1953-7268","authenticated-orcid":false,"given":"Baldu\u00edno","family":"Mateus","sequence":"additional","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, 62001-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0167-7489","authenticated-orcid":false,"given":"In\u00e1cio","family":"Fonseca","sequence":"additional","affiliation":[{"name":"Instituto Superior de Engenharia de Coimbra, Polytechnic of Coimbra, 3045-093 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-8079","authenticated-orcid":false,"given":"Jos\u00e9 Torres","family":"Farinha","sequence":"additional","affiliation":[{"name":"Instituto Superior de Engenharia de Coimbra, Polytechnic of Coimbra, 3045-093 Coimbra, Portugal"},{"name":"Centre for Mechanical Engineering, Materials and Processes\u2014CEMMPRE, University of Coimbra, 3030-788 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8210-5468","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Rodrigues","sequence":"additional","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, 62001-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4313-7966","authenticated-orcid":false,"given":"Mateus","family":"Mendes","sequence":"additional","affiliation":[{"name":"Instituto Superior de Engenharia de Coimbra, Polytechnic of Coimbra, 3045-093 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8737-6999","authenticated-orcid":false,"given":"Ant\u00f3nio Marques","family":"Cardoso","sequence":"additional","affiliation":[{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, 62001-001 Covilh\u00e3, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, M., Amaitik, N., Wang, Z., Xu, Y., Maisuradze, A., Peschl, M., and Tzovaras, D. 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