Process Monitoring and Modeling Using the Self-Organizing Map

  • Authors:

  • Esa Alhoniemi;Jaakko Hollmén;Olli Simula;Juha Vesanto

  • Affiliations:

  • Helsinki University of Technology, Laboratory of Computer and Information Science, P.O. Box 5400, FIN-02015 HUT, Finland;Helsinki University of Technology, Laboratory of Computer and Information Science, P.O. Box 5400, FIN-02015 HUT, Finland;Helsinki University of Technology, Laboratory of Computer and Information Science, P.O. Box 5400, FIN-02015 HUT, Finland (Correspondence to: Prof. Olli Simula. Integrated Computer-Aided Engineerin ...;Helsinki University of Technology, Laboratory of Computer and Information Science, P.O. Box 5400, FIN-02015 HUT, Finland

  • Venue:
  • Integrated Computer-Aided Engineering
  • Year:
  • 1999

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Abstract

The Self-Organizing Map (SOM) is a powerful neural network method for analysis and visualization of high-dimensional data. It maps nonlinear statistical dependencies between high-dimensional measurement data into simple geometric rela- tionships on a usually two-dimensional grid. The mapping roughly preserves the most important topological and metric relationships of the original data elements and, thus, inherently clusters the data. The need for visualization and clustering occurs, for instance, in the analysis of various engineering problems. In this paper, the SOM has been applied in monitoring and modeling of complex industrial processes. Case studies, including pulp process, steel production, and paper industry are described.