Table of Contents
Advances in Artificial Neural Systems
Volume 2012 (2012), Article ID 534683, 6 pages
http://dx.doi.org/10.1155/2012/534683
Review Article

Combining Neural Methods and Knowledge-Based Methods in Accident Management

Department of Information and Computer Science, Aalto University, P.O. Box 15400, 00076 Aalto, Finland

Received 10 February 2012; Revised 22 May 2012; Accepted 3 June 2012

Academic Editor: Wilson Wang

Copyright © 2012 Miki Sirola and Jaakko Talonen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Accident management became a popular research issue in the early 1990s. Computerized decision support was studied from many points of view. Early fault detection and information visualization are important key issues in accident management also today. In this paper we make a brief review on this research history mostly from the last two decades including the severe accident management. The author’s studies are reflected to the state of the art. The self-organizing map method is combined with other more or less traditional methods. Neural methods used together with knowledge-based methods constitute a methodological base for the presented decision support prototypes. Two application examples with modern decision support visualizations are introduced more in detail. A case example of detecting a pressure drift on the boiling water reactor by multivariate methods including innovative visualizations is studied in detail. Promising results in early fault detection are achieved. The operators are provided by added information value to be able to detect anomalies in an early stage already. We provide the plant staff with a methodological tool set, which can be combined in various ways depending on the special needs in each case.