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

Over the past century there has been a dramatic increase in the consumption of resources such as energy, raw materials, water, etc. in the manufacturing domain. An intelligent resource monitoring system that uses structural, context and process information of the plant can deliver more accurate monitoring results that can be used to detect excessive resource consumption. Recent monitoring systems usually run on a central unit. However, modern plants require 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 devices for resource monitoring, semantic models and rules are appropriate. This paper will present an architecture of a decentralized, intelligent resource monitoring system which uses structural, context and process knowledge to compute the state of the individual components by means of models and rules. This architecture might also be used for other manufacturing systems such as diagnostic or prognostic systems.