Advanced Monitoring and Diagnosis of Industrial Processes

Industrial processes are ever more challenging to monitor due to their complexity, nonlinear dynamics, and a large number of affecting factors involved. There are several methods able to deal with multidimensionality and which can be utilized in process monitoring systems, but it seems that the monitoring systems used presently by the industry are not completely able to meet the standards of modern production. It seems that a system capable of handling the overflowing amount of measurement data, extract the essential pieces of information, and present them in a visual and easily understandable way could be a definite advantage in the monitoring and diagnosis of industrial processes. In this paper, we demonstrate the use of self-organizing maps (SOM) and existing process data in a new kind of approach to monitor industrial processes, an approach in which the multivariate and dynamical characteristics of the data are taken into account. The system is demonstrated here using measurement data from an industrial wastewater treatment plant.