In this paper, 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.
A neural networks based system for post pulse fault detection and disruption data validation in tokamak machines / Fortuna, L.; Marchese, V.; Rizzo, A.; Xibilia, M. G.. - STAMPA. - (1999), pp. 563-566. (Intervento presentato al convegno IEEE International Symposium on Circuits and Systems, ISCAS 99 tenutosi a Orlando, FL nel May 30 - June 02, 1999) [10.1109/ISCAS.1999.777634].
A neural networks based system for post pulse fault detection and disruption data validation in tokamak machines
A. Rizzo;
1999-01-01
Abstract
In this paper, 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
