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Detecting anomalous energy consumptions in distributed manufacturing systems

: Faltinski, Sebastian; Flatt, Holger; Pethig, Florian; Kroll, Björn; Vodencarevic, A.; Maier, A.; Niggemann, Oliver

Postprint urn:nbn:de:0011-n-2291696 (834 KByte PDF)
MD5 Fingerprint: 52da3994de30cf47c209c95e76621fec
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Erstellt am: 23.3.2013

Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 10th International Conference on Industrial Informatics, INDIN 2012. Vol.1 : Beijing, China, 25 - 27 July 2012
Piscataway/NJ: IEEE, 2012
ISBN: 978-1-4673-0312-5 (Print)
ISBN: 978-1-4673-0311-8
International Conference on Industrial Informatics (INDIN) <10, 2012, Beijing>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

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 samples as model input. According to comparisons of predicted energy consumption with the production plant observations, anomalies can be detected automatically. An evaluation within a small factory shows that anomalies of 10%differences in energy consumption, wrong control sequences and wrong timings can be detected with a minimum accuracy of 98 %. With this approach, downtimes of production systems can be shortened and atypical energy consumptions can be detected and adjusted to optimal operation.