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Enhanced Changeover Detection in Industry 4.0 Environments with Machine Learning
| Content Provider | MDPI |
|---|---|
| Author | Miller, Eddi Borysenko, Vladyslav Heusinger, Moritz Niedner, Niklas Engelmann, Bastian Schmitt, Jan |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 5896 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21175896 |
| Journal | Sensors |
| Issue Number | 17 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-09-01 |
| Access Restriction | Open |
| Subject Keyword | Sensors Computer Science Machine Learning Changeover Human–machine Interaction |
| Content Type | Text |
| Resource Type | Article |
