• 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