Title :
Tension prediction by using ANN and SOM in heavy facilities
Author :
Gajdos, Petr ; Platos, Jan ; Fiala, Petr
Author_Institution :
Dept. of Comput. Sci., VSB-Tech. Univ. of Ostrava, Poruba, Czech Republic
Abstract :
Diagnostic systems based on mathematical models of material damaging process can be used to collect necessary information on trends and/or level of material and function damage. This paper is focused on the improvement of a particular part of the power plant diagnostic system. It describes some alternatives based on Artifical Neural Networks and Self-Organising Maps. Finally, this can help to eliminate the damages of power plant facilities.
Keywords :
finite element analysis; power engineering computing; power plants; self-organising feature maps; ANN; FEM; SOM; artifical neural networks; material damaging process; power plant diagnostic system; self-organising maps; tension prediction; Artificial neural networks; Biological neural networks; Finite element methods; Generators; Materials; Neurons; Vectors; FEM; Neural Networks; SOM; soft-computing; tension prediction;
Conference_Titel :
Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress on
Conference_Location :
Salamanca
Print_ISBN :
978-1-4577-1122-0
DOI :
10.1109/NaBIC.2011.6089653
