[Zurück]

M. Hennig, M. Grafinger, R. Hofmann, D. Gerhard, S. Dumss, P. Rosenberger:
"Introduction of a time series machine learning methodology for the application in a production system";
Advanced Engineering Informatics, Volume 47 (2021), 47; 12 S.

Machine learning methods are considered a promising approach for improving operations and processes in manufacturing. However, the application of machine
learning often requires the expertise of a data scientist combined with thorough knowledge of the manufacturing processes. Small and medium-sized companies that
specialize in certain high value-added, variant rich production processes often lack an in-house data scientist and therefore miss out on generating a deeper datadriven
insight from their production data streams. This paper proposes a three-step machine learning methodology to empower process experts with limited
knowledge in machine learning: 1) data exploration through clustering, 2) representation of the production systems behaviour through specially structured neural
networks and 3) querying this representation through evolutionary algorithms to achieve decision support through online optimization or scenario simulation. The
chosen algorithms focus on parameter-light, well-established, general use algorithms in order to lower knowledge requirements for their application.

Machinle learning, time series

http://dx.doi.org/10.1016/j.aei.2020.101197

https://publik.tuwien.ac.at/files/publik_299925.pdf