{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:17:54Z","timestamp":1760235474189,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100020639","name":"Bayerisches Staatsministerium f\u00fcr Wirtschaft, Landesentwicklung und Energie","doi-asserted-by":"publisher","award":["IUK 530\/10"],"award-info":[{"award-number":["IUK 530\/10"]}],"id":[{"id":"10.13039\/501100020639","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Changeover times are an important element when evaluating the Overall Equipment Effectiveness (OEE) of a production machine. The article presents a machine learning (ML) approach that is based on an external sensor setup to automatically detect changeovers in a shopfloor environment. The door statuses, coolant flow, power consumption, and operator indoor GPS data of a milling machine were used in the ML approach. As ML methods, Decision Trees, Support Vector Machines, (Balanced) Random Forest algorithms, and Neural Networks were chosen, and their performance was compared. The best results were achieved with the Random Forest ML model (97% F1 score, 99.72% AUC score). It was also carried out that model performance is optimal when only a binary classification of a changeover phase and a production phase is considered and less subphases of the changeover process are applied.<\/jats:p>","DOI":"10.3390\/s21175896","type":"journal-article","created":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T23:05:12Z","timestamp":1630623912000},"page":"5896","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Enhanced Changeover Detection in Industry 4.0 Environments with Machine Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3755-0666","authenticated-orcid":false,"given":"Eddi","family":"Miller","sequence":"first","affiliation":[{"name":"Institute Digital Engineering (IDEE), University of Applied Sciences, W\u00fcrzburg-Schweinfurt, Ignaz-Sch\u00f6n-Strasse 11, 97421 Schweinfurt, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2364-5250","authenticated-orcid":false,"given":"Vladyslav","family":"Borysenko","sequence":"additional","affiliation":[{"name":"Institute Digital Engineering (IDEE), University of Applied Sciences, W\u00fcrzburg-Schweinfurt, Ignaz-Sch\u00f6n-Strasse 11, 97421 Schweinfurt, Germany"}]},{"given":"Moritz","family":"Heusinger","sequence":"additional","affiliation":[{"name":"Institute Digital Engineering (IDEE), University of Applied Sciences, W\u00fcrzburg-Schweinfurt, Ignaz-Sch\u00f6n-Strasse 11, 97421 Schweinfurt, Germany"}]},{"given":"Niklas","family":"Niedner","sequence":"additional","affiliation":[{"name":"Institute Digital Engineering (IDEE), University of Applied Sciences, W\u00fcrzburg-Schweinfurt, Ignaz-Sch\u00f6n-Strasse 11, 97421 Schweinfurt, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2053-0995","authenticated-orcid":false,"given":"Bastian","family":"Engelmann","sequence":"additional","affiliation":[{"name":"Institute Digital Engineering (IDEE), University of Applied Sciences, W\u00fcrzburg-Schweinfurt, Ignaz-Sch\u00f6n-Strasse 11, 97421 Schweinfurt, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4537-7680","authenticated-orcid":false,"given":"Jan","family":"Schmitt","sequence":"additional","affiliation":[{"name":"Institute Digital Engineering (IDEE), University of Applied Sciences, W\u00fcrzburg-Schweinfurt, Ignaz-Sch\u00f6n-Strasse 11, 97421 Schweinfurt, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,1]]},"reference":[{"key":"ref_1","unstructured":"(2021, July 29). 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