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The present work used supervised and unsupervised learning methods to create models for predicting the condition of an industrial machine. The main objective was to determine when the asset was either in its nominal operation or working outside this zone, thus being at risk of failure or sub-optimal operation. The results showed that it is possible to classify the machine state using artificial neural networks. K-means clustering and PCA methods showed that three states, chosen through the Elbow Method, cover almost all the variance of the data under study. Knowing the importance that the quality of the lubricants has in the functioning and classification of the state of machines, a lubricant classification algorithm was developed using Neural Networks. The lubricant classifier results were 98% accurate compared to human expert classifications. The main gap identified in the research is that the found classification works only carried out classifications of present, short-term, or mid-term failures. To close this gap, the work presented in this paper conducts a long-term classification.<\/jats:p>","DOI":"10.3390\/en15249387","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T05:10:19Z","timestamp":1670821819000},"page":"9387","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8210-5468","authenticated-orcid":false,"given":"Jo\u00e3o Antunes","family":"Rodrigues","sequence":"first","affiliation":[{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, Cal\u00e7ada Fonte do Lameiro, 6201-001 Covilh\u00e3, Portugal"},{"name":"EIGeS\u2014Research Centre in Industrial Engineering, Management and Sustainability, Universidade Lus\u00f3fona, Campo Grande 376, 1749-024 Lisboa, Portugal"}]},{"given":"Alexandre","family":"Martins","sequence":"additional","affiliation":[{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, Cal\u00e7ada Fonte do Lameiro, 6201-001 Covilh\u00e3, Portugal"},{"name":"EIGeS\u2014Research Centre in Industrial Engineering, Management and Sustainability, Universidade Lus\u00f3fona, Campo Grande 376, 1749-024 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4313-7966","authenticated-orcid":false,"given":"Mateus","family":"Mendes","sequence":"additional","affiliation":[{"name":"Polytechnic of Coimbra\u2014 ISEC, Quinta da Nora, 3030-199 Coimbra, Portugal"},{"name":"Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, 3030-194 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-8079","authenticated-orcid":false,"given":"Jos\u00e9 Torres","family":"Farinha","sequence":"additional","affiliation":[{"name":"Polytechnic of Coimbra\u2014 ISEC, Quinta da Nora, 3030-199 Coimbra, Portugal"},{"name":"Department of Mechanical Engineering, Centre for Mechanical Engineering, Materials and Processes, University of Coimbra, 3030-290 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3630-6426","authenticated-orcid":false,"given":"Ricardo J. 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