PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Process state and progress visualization using self-organizing map
Risto Hakala, Timo Similä, Miki Sirola and Jukka Parviainen
In: International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), 20-23 Sep 2006, Burgos, Spain.

Abstract

The self-organizing map (SOM) 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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
ID Code:2202
Deposited By:Timo Similä
Deposited On:21 September 2006