Industrie 4.0 and data analytics blur the separation of operational and information technology that prevailed for industrial automation over the last decades. Decentralized control systems for production plants and robot cells collaborate actively with higher-level systems for big data analytics. In parallel, the complexity of designing and operating a system architecture for data collection and analysis increases dramatically as more experts from different domains get involved. Graphical modeling notations facilitate the design process by formalizing implicit knowledge, but currently do not exist for the combined description for field layer and data analytics. Modeling the system architecture, relevant constraints, and requirements early during the design process can increase the efficiency of the system development and deployment, especially as experts with various backgrounds are involved. In this contribution, a new graphical notation is introduced and evaluated in three industrial case-studies. The notation describes the underlying hardware and software components of cyber-physical systems of systems, the flow of data, and relevant constraints. The evaluation proved that the notation is powerful in supporting the engineering of data collection and analysis architectures in industrial automation. Future work is related to extending the scope of the modeling approach to include safety applications and real-time considerations on the field level.
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Industrie 4.0 and data analytics blur the separation of operational and information technology that prevailed for industrial automation over the last decades. Decentralized control systems for production plants and robot cells collaborate actively with higher-level systems for big data analytics. In parallel, the complexity of designing and operating a system architecture for data collection and analysis increases dramatically as more experts from different domains get involved. Graphical modeli...
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