Monitoring And Modeling Of Complex Processes Using Hierarchical Self-organizing Maps
Olli Simula, Esa J. Alhoniemi, Jaakko Hollmén, Juha Vesanto · 2005
In this paper, a neural network based analysis method for monitoring and modeling the dynamic behavior of complex industrial processes is considered. The method is based on the unsupervised learning property of the Self-Organizing Map (SOM) algorithm. The time series produced by several sensors measuring the process parameters as well as other process data are used in mapping the process behavior and dynamics into the network. 1. INTRODUCTION Analysis, modeling, and control of complex nonlinear systems constitutes a difficult problem area. Such systems, e.g. machines or industrial processes, should be described using a set of variables, which can be determined by various measurements and parameters. The problem in process analysis is to find the characteristic states, or clusters of states, that determine the general behavior of the system and reflect the measurements. In modeling, the behavior of the system should be described in a closed form in order to be able to predict the futu...