Detecting Anomalous Energy Consumptions in Distributed Manufacturing Systems
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.
Sebastian Faltinski Holger Flatt Florian Pethig Bj(o)rn Kroll Asmir Voden(c)arevi(c) Alexander Maier Oliver Niggemann
Fraunhofer IOSB-INA, Application Center Industrial Automation University of Paderborn, Knowledge-Based Systems Research Group Ostwestfalen-Lippe University of Applied Science, inIT-Institute Industrial IT Fraunhofer IOSB-INA, Application Center Industrial Automation Ostwestfalen-Lippe University of Appli
国际会议
IEEE 10th International Conference on Industrial Informatics(第十届IEEE工业信息学国际学术会议 INDIN2012)
北京
英文
358-363
2012-07-25（万方平台首次上网日期，不代表论文的发表时间）