Abstract
The integration of smart devices into the production process results in the emergence of cyber-physical production systems (CPPSs) that are a key part of Industrie 4.0. Various sensors, actuators, Programmable Logic Controllers (PLCs), Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) systems produce huge amounts of data and meta data that can hardly be handled by conventional analytic methods. The main goal of this work is to develop an innovative architecture for handling big data from various heterogeneous sources within an automated production system (aPS). Moreover, enabling data analysis to gain a better understanding of the whole process, spotting possible defects in advance and increasing the overall equipment effectiveness (OEE), is in focus. This new architecture vertically connects the production lines to the analysts by using a generic data format for dealing with various types of data. The presented model is applied prototypically to a lab-scale production unit. Based on a message broker, the presented prototype is able to process messages from different sources, using e.g. OPC UA and MQTT protocols, storing them in a database and providing them for live-analysis. Furthermore, data can be anonymized, depending on granted access rights, and can be provided to external analyzers. The prototypical implementation of the architecture is able to operate in a heterogeneous environment supporting many platforms. The prototype is stress tested with different workloads showing hardly any response in the form of longer delivery times. Thus, feasibility of the architecture and its suitability for industrial, near real-time applications can be shown on a labscale.
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Trunzer, E., Lötzerich, S., Vogel-Heuser, B. (2018). Concept and Implementation of a Software Architecture for Unifying Data Transfer in Automated Production Systems. In: Niggemann, O., Schüller, P. (eds) IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency. Technologien für die intelligente Automation, vol 8. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57805-6_1
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