Abstract
Data driven analysis of complex systems or processes is necessary in many practical applications where analytical modeling is not possible. The Self-Organizing Map (SOM) is a neural network algorithm that has been widely applied in analysis and visualization of high-dimensional data. It carries out a nonlinear mapping of input data onto a two-dimensional grid. The mapping preserves the most important topological and metric relationships of the data. The SOM has turned out to be an efficient tool in data exploration tasks in various engineering applications: process analysis in forest industry, steel production and analysis of telecommunication networks and systems. In this paper, SOM based analysis of complex process data is discussed. As a case study, analysis of a continuous pulp digester is presented. The SOM is used to form visual presentations of the data. By interpreting the visualizations, complex parameter dependencies can be revealed. By concentrating on the significant measurements, reasons for digester faults can be determined.
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