Automated Detection of Production Cycles in Production Plants using Machine Learning

被引：0

作者：

Bunte, Andreas ^{[1
]}

Ressler, Henrik ^{[1
]}

Moriz, Natalia ^{[1
]}

机构：

[1] Ostwestfalen Lippe Univ Appl Sci & Arts, inIT Inst Ind IT, Lemgo, Germany

来源：

2020 25TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA) | 2020年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1419 / 1422

页数：4

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