DocumentCode :
1948627
Title :
The importance of model-learning for the analysis of the energy consumption of production plants
Author :
Gilani, Syed Shiraz ; Windmann, Stefan ; Pethig, Florian ; Kroll, Bjorn ; Niggemann, Oliver
Author_Institution :
Applic. Center for Ind. Autom., Fraunhofer IOSB-INA, Lemgo, Germany
fYear :
2013
fDate :
10-13 Sept. 2013
Firstpage :
1
Lastpage :
8
Abstract :
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 for systems, models and model learning algorithms. Based on this analysis of open research questions, the new learning algorithm HyBUTLA is presented. In this paper, this solution approach is applied to the analysis and diagnosis of the energy consumption of production plants. To the best of the authors´ knowledge, this is the first learning and adaptable energy anomaly detection solution for complex hybrid production systems.
Keywords :
energy consumption; factory automation; industrial plants; learning (artificial intelligence); manufacturing systems; pattern classification; production engineering computing; HyBUTLA; adaptable energy anomaly detection solution; automatic generation; classification schema; complex hybrid production systems; energy consumption analysis; energy consumption diagnosis; intelligent automation systems; intelligent capabilities; model learning algorithm; production plants; Analytical models; Automation; Energy consumption; Learning automata; Mathematical model; Merging; Production;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging Technologies & Factory Automation (ETFA), 2013 IEEE 18th Conference on
Conference_Location :
Cagliari
ISSN :
1946-0740
Print_ISBN :
978-1-4799-0862-2
Type :
conf
DOI :
10.1109/ETFA.2013.6647976
Filename :
6647976
Link To Document :
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