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The efficient handling of complex production systems and the implementation of more flexible and adaptable production lies at the heart of cyber-physical production systems and its german equivalent Industry 4.0. Such scenarios currently face one main difficulty: the creation, configuration and maintenance of the corresponding automation software is time-consuming and error-prone. Two main solutions...
Electricity, water or air are some Industrial energy carriers which are struggling under the prices of primary energy carriers. The European Union for example used more 20.000.000 GWh electricity in 2011 based on the IEA Report [1]. Cyber Physical Production Systems (CPPS) are able to reduce this amount, but they also help to increase the efficiency of machines above expectations which results in...
This paper contributes a framework that helps to fulfill the requirements of the standards DIN EN 16247 and ISO 50001 by combining (i) a synchronized data acquisition, (ii) data integration, (iii) learning of normal behavior models and (iv) a implementation of an anomaly detection as prototype. Both standards require a reliable data acquisition and energy consumption analysis for implementing a certified...
Model-learning is the key to the new generation of intelligent automation systems: Without the automatic generation of models from system observations, models of the plant's behavior will not be available for most systems. And without such models, no intelligent capabilities such as self-diagnosis or self-optimization can be implemented. This paper therefore presents a novel classification schema...
This paper presents a novel approach for data acquisition in distributed, heterogeneous automation systems as a basis for new condition monitoring, anomaly detection and visualization applications. The described solution enables process data acquisition in real time Ethernet systems, using the Precision Time Protocol (PTP) from IEEE 1588 for synchronization and the OPC Unified Architecture for data...
This paper presents a novel model-based approach for the prediction of energy consumption in production plants in order to detect anomalies. A special Ethernet-based data acquisition approach is implemented that features real-time sampling of process and energy data. Hybrid timed automaton models of the supervised production plant are generated and executed in parallel to the system by using data...
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