A Modular Architecture for Smart Data Analysis using AutomationML, OPC-UA and Data-driven Algorithms.

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 fur 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.