Diagnosis and monitoring of complex industrial processes based on self-organizing maps and watershed transformations
Abstract
A cost-effective operation of complex automation systems requires the continuous diagnosis of the asset functionality. The early detection of potential failures and malfunctions, the identification and localization of present or impending component failures and, in particular, the monitoring of the underlying physical process are of crucial importance for the efficient operation of complex process industry assets. With respect to these suppositions a software agent based diagnosis and monitoring concept has been developed, which allows an integrated and continuous diagnosis of the communication network and the underlying physical process behavior. The present paper outlines the architecture of the developed distributed diagnostic concept based on software agents and presents the functionality for the diagnosis of the unknown process behaviour of the underlying automation system based on machine learning methods.
