Title :
A neural networks based system for post pulse fault detection and disruption data validation in tokamak machines
Author :
Fortuna, L. ; Marchese, V. ; Rizzo, A. ; Xibilia, M.G.
Author_Institution :
DEES, Catania Univ., Italy
Abstract :
A novel neural network based fault detection strategy to isolate and classify faults occurring in a tokamak fusion plant is described. In particular, attention is focused on measurements of vertical stresses during plasma disruptions. The strategy is based on a neural model which estimates suitable features of the expected sensor response, allowing to isolate the most frequently occurring faults. The proposed strategy has been validated at JET, the Joint European Torus, on several disruptions, and is currently used for fault detection purposes, providing great accuracy in detecting sensor faults, together with a high degree of automation
Keywords :
Tokamak devices; fault location; fusion reactor safety; neural nets; nuclear engineering computing; plasma instability; JET; Joint European Torus; disruption data validation; fault detection purposes; neural networks based system; post pulse fault detection; sensor faults; sensor response; tokamak fusion plant; tokamak machines; vertical stresses; Artificial neural networks; Fault detection; Intelligent networks; Mechanical sensors; Neural networks; Plasma measurements; Sensor phenomena and characterization; Strain measurement; Stress measurement; Tokamaks;
Conference_Titel :
Circuits and Systems, 1999. ISCAS '99. Proceedings of the 1999 IEEE International Symposium on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-5471-0
DOI :
10.1109/ISCAS.1999.777634
