A Decentralized Resource Monitoring System Using Structural, Context and Process Information

Lisa Abele; Lisa Ollinger; Ines Dahmann; Martin Kleinsteuber

In: Trends in Intelligent Robotics, Automation and Manufacturing. Intelligent Robotics, Automation and Manufacturing (IRAM-2012), November 28-30, Kuala Lumpur, Malaysia, Pages 371-378, Communications in Compute, Vol. 330, Springer , Berlin - Heidelberg, 2012.


Over the past century there has been a dramatic increase in the con- sumption of resources such as energy, raw materials, water, etc. in the manu- facturing domain. An intelligent resource monitoring system that uses structural, context and process information of the plant can deliver more accurate monitor- ing results that can be used to detect excessive resource consumption. Recent monitoring systems usually run on a central unit. However, modern plants re- quire a higher degree of reusability and adaptability which can be achieved by several monitoring units running on decentralized autonomous devices that allow the components to monitor themselves. To integrate structural, context and process information on such autonomous de- vices for resource monitoring, semantic models and rules are appropriate. This paper will present an architecture of a decentralized, intelligent resource moni- toring system which uses structural, context and process knowledge to compute the state of the individual components by means of models and rules. This archi- tecture might also be used for other manufacturing systems such as diagnostic or prognostic systems.


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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence