{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T10:38:18Z","timestamp":1769942298591,"version":"3.49.0"},"reference-count":17,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Polytechnic University of Coimbra within the scope of Regulamento de Apoio \u00e0 Publica\u00e7\u00e3o Cient\u00edfica dos Trabalhadores do Instituto Polit\u00e9cnico de Coimbra","award":["Despacho n.\u00ba 4654\/2024"],"award-info":[{"award-number":["Despacho n.\u00ba 4654\/2024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Predictive maintenance is essential for minimizing unplanned downtime and optimizing industrial processes. In the case of plastic injection molding machines, failures that lead to downtime, slowing production, or manufacturing defects can cause large financial losses or even endanger people and property. As industrialization advances, proactive equipment management enhances cost efficiency, reliability, and operational continuity. This study aims to detect machine anomalies as early as possible, using sensors, statistical analysis and classification models. A case study was carried out, including machine characterization and data collection. Clustering methods identified operational patterns and anomalies, classifying the machine\u2019s behavior into distinct states, validated by company experts. Dimensionality reduction with PCA contributed to highlighting salient features and reducing noise. State classification was carried out using the resulting cluster data. Classification using XGBoost achieved the best performance among the machine learning models tested, reaching an accuracy of 83%. This approach can contribute to maximizing plastic injection machines\u2019 availability and reducing losses due to malfunctions and downtime.<\/jats:p>","DOI":"10.3390\/a18080521","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T15:34:53Z","timestamp":1755531293000},"page":"521","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Analysis of the State and Fault Detection of a Plastic Injection Machine\u2014A Machine Learning-Based Approach"],"prefix":"10.3390","volume":"18","author":[{"given":"Jo\u00e3o","family":"Costa","sequence":"first","affiliation":[{"name":"Coimbra Institute of Engineering, Polytechnic University of Coimbra, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal"}]},{"given":"Rui","family":"Silva","sequence":"additional","affiliation":[{"name":"Sinmetro LDA, Rua dos Costas, Lote 19, Loja 74, R\/C, 2415-567 Leiria, Portugal"}]},{"given":"Gon\u00e7alo","family":"Martins","sequence":"additional","affiliation":[{"name":"Sinmetro LDA, Rua dos Costas, Lote 19, Loja 74, R\/C, 2415-567 Leiria, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5672-4468","authenticated-orcid":false,"given":"Jorge","family":"Barreiros","sequence":"additional","affiliation":[{"name":"Coimbra Institute of Engineering, Polytechnic University of Coimbra, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal"},{"name":"RCM<sup>2+<\/sup> Research Centre for Asset Management and Systems Engineering, 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":"Coimbra Institute of Engineering, Polytechnic University of Coimbra, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal"},{"name":"RCM<sup>2+<\/sup> Research Centre for Asset Management and Systems Engineering, Rua Pedro Nunes, 3030-199 Coimbra, Portugal"},{"name":"Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Daurenbayeva, N., Nurlanuly, A., Atymtayeva, L., and Mendes, M. 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Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Park, Y.J., Fan, S.K.S., and Hsu, C.Y. (2020). A review on fault detection and process diagnostics in industrial processes. Processes, 8.","DOI":"10.3390\/pr8091123"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s00521-022-08017-3","article-title":"Challenges and opportunities of deep learning-based process fault detection and diagnosis: A review","volume":"35","author":"Yu","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_9","unstructured":"Zhang, J., and Alexander, S.M. (2004, January 18\u201321). Fault Diagnosis in Injection Molding via Cavity Pressure Signals. 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