Information agents handling semantic data as an extension to process monitoring systems

An approach to extend process monitoring with the help of information agents (IA) handling semantic data is presented in this paper. According to this approach, an operator of a process automation system can configure monitoring tasks that a group of IAs performs proactively. The monitoring tasks are assumed to be composites which refer to several process observations and their logical relations. The purpose of these composite monitoring tasks is to enhance the work of the operator by letting him to supervise process phenomena at a higher level of abstraction instead of following a large amount of simple measurement data. The monitoring agents operate as a multi-agent system consisting of agents with capabilities to combine both numerical and symbolic information from several data sources. The agents can setup and execute user configured monitoring tasks cooperatively. The approach is illustrated with test scenarios using data from an industrial paper making process