{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:04:06Z","timestamp":1775228646778,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T00:00:00Z","timestamp":1713139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["UIDB\/04058\/2020"],"award-info":[{"award-number":["UIDB\/04058\/2020"]}]},{"name":"FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["UIDP\/04058\/2020"],"award-info":[{"award-number":["UIDP\/04058\/2020"]}]},{"name":"FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["305950\/2023-1"],"award-info":[{"award-number":["305950\/2023-1"]}]},{"DOI":"10.13039\/501100003593","name":"CNPq Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico\u2013Research funding in Productivity","doi-asserted-by":"publisher","award":["UIDB\/04058\/2020"],"award-info":[{"award-number":["UIDB\/04058\/2020"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"CNPq Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico\u2013Research funding in Productivity","doi-asserted-by":"publisher","award":["UIDP\/04058\/2020"],"award-info":[{"award-number":["UIDP\/04058\/2020"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"CNPq Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico\u2013Research funding in Productivity","doi-asserted-by":"publisher","award":["305950\/2023-1"],"award-info":[{"award-number":["305950\/2023-1"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>In the context of Industry 4.0, managing large amounts of data is essential to ensure informed decision-making in intelligent production environments. It enables, for example, predictive maintenance, which is essential for anticipating and identifying causes of failures in machines and equipment, optimizing processes, and promoting proactive management of human, financial, and material resources. However, generating accurate information for decision-making requires adopting suitable data preprocessing and analysis techniques. This study explores the identification of machine failures based on synthetic industrial data. Initially, we applied the feature selection techniques Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (mRMR), Neighborhood Component Analysis (NCA), and Denoising Autoencoder (DAE) to the collected data and compared their results. In the sequence, a comparison among three widely known machine learning classifiers, namely Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron neural network (MLP), was conducted, with and without considering feature selection. The results showed that PCA and RF were superior to the other techniques, allowing the classification of failures with rates of 0.98, 0.97, and 0.98 for the accuracy, precision, and recall metrics, respectively. Thus, this work contributes by solving an industrial problem and detailing techniques to identify the most relevant variables and machine learning algorithms for predicting machine failures that negatively impact production planning. The findings provided by this study can assist industries in giving preference to employing sensors and collecting data that can contribute more effectively to machine failure predictions.<\/jats:p>","DOI":"10.3390\/app14083337","type":"journal-article","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T11:53:53Z","timestamp":1713182033000},"page":"3337","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Impacts of Feature Selection on Predicting Machine Failures by Machine Learning Algorithms"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2829-1148","authenticated-orcid":false,"given":"Francisco El\u00e2nio","family":"Bezerra","sequence":"first","affiliation":[{"name":"Department of Energy Engineering and Electrical Automation, Polytechnic School, University of S\u00e3o Paulo (USP), 158 Prof. Luciano Gualberto Avenue, S\u00e3o Paulo 05508-010, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4744-3963","authenticated-orcid":false,"given":"Geraldo Cardoso de","family":"Oliveira Neto","sequence":"additional","affiliation":[{"name":"Industrial Engineering Post Graduation Program, Federal University of ABC, Alameda da Universidade, s\/n\u00ba Bairro Anchieta, S\u00e3o Bernardo do Campo, S\u00e3o Paulo 09606-045, Brazil"}]},{"given":"Gabriel Magalh\u00e3es","family":"Cervi","sequence":"additional","affiliation":[{"name":"Business Administration Post-Graduation Program, FEI University, Tamandar\u00e9 Street 688, 5 Floor, S\u00e3o Paulo 01525-000, Brazil"}]},{"given":"Rafaella","family":"Francesconi Mazetto","sequence":"additional","affiliation":[{"name":"Business Administration Post-Graduation Program, FEI University, Tamandar\u00e9 Street 688, 5 Floor, S\u00e3o Paulo 01525-000, Brazil"}]},{"given":"Aline Mariane de","family":"Faria","sequence":"additional","affiliation":[{"name":"Business Administration Post-Graduation Program, FEI University, Tamandar\u00e9 Street 688, 5 Floor, S\u00e3o Paulo 01525-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6347-9889","authenticated-orcid":false,"given":"Marcos","family":"Vido","sequence":"additional","affiliation":[{"name":"Industrial Engineering Post-Graduation Program, Nove de Julho University (UNINOVE), Vergueiro Street 235\/249, S\u00e3o Paulo 01504-001, Brazil"}]},{"given":"Gustavo Araujo","family":"Lima","sequence":"additional","affiliation":[{"name":"Informatics and Knowledge Management Post-Graduation Program, Nove de Julho University (UNINOVE), Vergueiro Street 235\/249, S\u00e3o Paulo 01504-001, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3970-5801","authenticated-orcid":false,"given":"Sidnei Alves de","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"Informatics and Knowledge Management Post-Graduation Program, Nove de Julho University (UNINOVE), Vergueiro Street 235\/249, S\u00e3o Paulo 01504-001, Brazil"}]},{"given":"Mauro","family":"Sampaio","sequence":"additional","affiliation":[{"name":"Industrial Engineering Post-Graduation Program, FEI University, Avenue Humberto de Alencar Castelo Branco 3972-B, S\u00e3o Bernardo do Campo, Assun\u00e7\u00e3o 09850-901, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0901-0614","authenticated-orcid":false,"given":"Marlene","family":"Amorim","sequence":"additional","affiliation":[{"name":"GOVCOPP-DEGEIT, University of Aveiro, 3810-193 Aveiro, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100110","DOI":"10.1016\/j.sintl.2021.100110","article-title":"Significance of sensors for industry 4.0: Roles, capabilities, and applications","volume":"2","author":"Javaid","year":"2021","journal-title":"Sens. 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