Enhanced Changeover Detection in Industry 4.0 Environments with Machine Learning
| Autor: | Jan Schmitt, Eddi Miller, Moritz Heusinger, Bastian Engelmann, Vladyslav Borysenko, Niklas Niedner |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
human–machine interaction
Support Vector Machine Computer science Decision tree TP1-1185 Machine learning computer.software_genre Biochemistry Article Analytical Chemistry Overall equipment effectiveness Electrical and Electronic Engineering Instrumentation Artificial neural network business.industry Chemical technology Changeover Atomic and Molecular Physics and Optics Random forest Support vector machine machine learning Binary classification changeover Artificial intelligence Neural Networks Computer F1 score business computer Algorithms |
| Zdroj: | Sensors Volume 21 Issue 17 Sensors, Vol 21, Iss 5896, p 5896 (2021) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s21175896 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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