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A modular architecture for smart data analysis using AutomationML, OPC-UA and data-driven algorithms

: Kühnert, Christian; Schleipen, Miriam; Okon, Michael; Henßen, Robert; Bischoff, Tino


Beyerer, Jürgen (Ed.); Niggemann, Oliver (Ed.); Kühnert, Christian (Ed.):
Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2016, held at the Fraunhofer IOSB in Karlsruhe, September 29th, 2016
Berlin: Springer Vieweg, 2017 (Technologien für die intelligente Automation 3)
ISBN: 978-3-662-53805-0 (Print)
ISBN: 978-3-662-53806-7
Conference on Machine Learning for Cyber-Physical-Systems and Industry 4.0 (ML4CPS) <2, 2016, Karlsruhe>
Conference Paper
Fraunhofer IOSB ()

Today, heterogeneous tool landscape and different data suppliers in the production environment complicate a universal component for the processing of process and quality data. The development effort of a suitable system for process data analysis comprises a serious effort for the connection to the data sources, the comprehension of the recorded data, and the development of a feasible visualization. To avoid this, an integrated architecture based on existing industrial standards can be used. The present paper discusses such a modular architecture which makes the possibilities of process optimization and predictive maintenance transparent to the user. The architecture is based on standards für production plant modelling (AutomationML) and for the connection to the production process (OPC UA). It includes an example implementation of water quality monitoring using principal component analysis.