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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E. Alhoniemi, J. Hollmén, O. Simula, and J. Vesanto. Process monitoring and Modeling Using the Self-Organizing Map. Integrated Computer-Aided Engineering, 6(1):3–14, 1999.
B. S. Dayal, J. F. MacGregor, P. A. Taylor, and S. Marcikic. Application of feedforward neural networks and partial least squares for modelling kappa number in a continuous kamyr digester. Pulp & Paper Canada, 95(1):26–32, 1994.
R. R. Gustafson, C. A. Sleicher, W. T. McKean, and B. A. Finlayson. Thoretical model of the kraft pulping process. Industrial & Engineering Chemistry Process, 22(1):87–96, Jan. 1983.
J. Himberg. Enhancing SOM-based data visualization by linking different data projections. In L. Xu, L. W. Chan, and I. King, editors, Intelligent Data Engineering and Learning, pages 427–434. Springer, 1998.
E. Härkönen. A mathematical model for two-phase flow in a continuous digester. Tappi Journal, 70:122–126, Dec. 1987.
S. Kaski, J. Venna, and T. Kohonen. Tips for Processing and Color-Coding of Self-Organizing Maps. In G. Deboeck and T. Kohonen, editors, Visual Explorations in Finance, Springer Finance, chapter 14, pages 195–202. Springer-Verlag, 1998.
T. Kohonen. Self-Organizing Maps, volume 30 of Springer Series in Information Sciences. Springer, Berlin, Heidelberg, 1995.
T. Kohonen, E. Oja, O. Simula, A. Visa, and J. Kangas, Engineering Applications of the Self-Organizing Map. Proceedings of the IEEE, 84(10):1358–1384, 1996.
M. T. Musavi, D. R. Coughglin, and M. Qiao. Prediction of wood pulp k# with radial basis function neural network. In Proceedings of the 1995 IEEE International Symposium on Circuits and Systems, volume 3, pages 1716–1719, Piscataway, 1995. IEEE.
K. Raivio, J. Henriksson, and O. Simula. Neural detection of QAM signal with strongly nonlinear receiver. Neurocomputing, 21:159–171, 1998.
J. B. Rudd. Prediction and control of pulping processes using neural network models. In 80th Annual Meeting, Technical Section, volume B, pages 169–173, Montreal, Quebec, Canada, Feb. 1994. Canadian Pulp & Paper Association.
O. Simula and J. Kangas. Neural Networks for Chemical Engineers, volume 6 of Computer-Aided Chemical Engineering, chapter 14: Process monitoring and visualization using self-organizing maps, pages 371–384. Elsevier, Amsterdam, 1995.
H. Tang and O. Simula. The optimal utilization of multi-service scp. In Intelligent Networks and New Technologies, pages 175–188. Chapman & Hall, 1996.
J. Vesanto. SOM-Based Data Visualization Methods. Intelligent Data Analysis, 1998. Accepted for publication.
J. Vesanto and J. Ahola. Hunting for Correlations in Data Using the Self-Organizing Map. Accepted for publication in International ICSC Symposium on Advances in Intelligent Data Analysis.
J. Vesanto, J. Himberg, M. Siponen, and O. Simula. Enhancing SOM Based Data Visualization. In T. Yamakawa and G. Matsumoto, editors, Proceedings of the 5th International Conference on Soft Computing and Information/Intelligent Systems, pages 64–67. World Scientific, 1998.
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Simula, O., Alhoniemi, E. (1999). SOM based analysis of pulping process data. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100524
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DOI: https://doi.org/10.1007/BFb0100524
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