Abstract
The self-organizing map (SOM) [1] is used in data analysis for resolving and visualizing nonlinear relationships in complex data. This paper presents an application of the SOM for depicting state and progress of a real-time process. A self-organizing map is used as a visual regression model for estimating the state configuration and progress of an observation in process data. The proposed technique is used for examining full-scope nuclear power plant simulator data. One aim is to depict only the most relevant information of the process so that interpretating process behaviour would become easier for plant operators. In our experiments, the method was able to detect a leakage situation in an early stage and it was possible to observe how the system changed its state as time went on.
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References
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer Series in Information Sciences, vol. 30. Springer, New York (2001)
Simula, O., Vesanto, J., Vasara, P., Helminen, R.R.: Self-organizing map in industry analysis. In: Industrial Applications of Neural Networks, pp. 87–112. CRC Press, Boca Raton (1999)
Alhoniemi, E., Hollmén, J., Simula, O., Vesanto, J.: Process monitoring and modeling using the self-organizing map. Integrated Computer-Aided Engineering 6, 3–14 (1999)
Sirola, M., Lampi, G., Parviainen, J.: SOM based decision support in failure management. International Scientific Journal of Computing 3, 124–130 (2005)
Paulsen, J.L.: Design of Process Displays based on Risk Analysis Techniques. PhD thesis, The Technical University of Denmark and Risø National Laboratory, Roskilde, Denmark (2004)
Kiviluoto, K.: Predicting bankruptcies with the self-organizing map. Neurocomputing 21, 191–201 (1998)
Similä, T.: Self-organizing map learning nonlinearly embedded manifolds. Information Visualization 4, 22–31 (2005)
Pershagen, B.: Light Water Reactor Safety. Pergamon Press, Stockholm (1989)
Vesanto, J.: Data Exploration Process Based on the Self-Organizing Map. PhD thesis, Helsinki University of Technology, Espoo, Finland (2002)
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© 2006 Springer-Verlag Berlin Heidelberg
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Hakala, R., Similä, T., Sirola, M., Parviainen, J. (2006). Process State and Progress Visualization Using Self-Organizing Map. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_9
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DOI: https://doi.org/10.1007/11875581_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
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