Enhanced Changeover Detection in Industry 4.0 Environments with Machine Learning

Sensors

Moritz Heusinger¹  Jan Schmitt¹  Eddi Miller¹  Vladyslav Borysenko¹  Bastian Engelmann¹  Niklas Niedner¹ 

[1] Institute Digital Engineering (IDEE), University of Applied Sciences, Würzburg-Schweinfurt, Ignaz-Schön-Strasse 11, 97421 Schweinfurt, Germany;

关键词: machine learning;    changeover;    human–machine interaction;   

DOI  :  10.3390/s21175896

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown