Automated Detection of Production Cycles in Production Plants using Machine Learning

Data-driven algorithms can be used to derive new information from data. In modern production plants, this can be used to reduce manual effort, e.g. to create a behavior model. In this work, one offline and one online algorithm are introduced that can determine the production cycles automatically. Th...

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Bibliographic Details
Published in:	2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Vol. 1; pp. 1423 - 1426
Main Authors:	Bunte, Andreas, Ressler, Henrik, Moriz, Natalia
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-09-2020
Subjects:	Conferences Learning automata Machine learning Machine learning algorithms Manuals Manufacturing automation Production
Online Access:	Get full text
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Summary:	Data-driven algorithms can be used to derive new information from data. In modern production plants, this can be used to reduce manual effort, e.g. to create a behavior model. In this work, one offline and one online algorithm are introduced that can determine the production cycles automatically. The algorithms use learned automaton to detect production cycles. A first evaluation is presented, which points out differences of the algorithms. However, overall the results are promising.
ISSN:	1946-0759
DOI:	10.1109/ETFA46521.2020.9211929