An innovative intelligent system for sensor validation in Tokamak machines

In this paper, a sensor validation strategy based on soft computing techniques to isolate and classify some faults occurring in the measurement system of a Tokamak fusion plant is described. Particular attention is focused on the system used to measure vertical stress in the mechanical structure of a Tokamak nuclear fusion plant during fusion experiments. The strategy adopted is based on a modular structure comprising two stages. The first stage consists of a neural network which acts as a symptom model able to estimate directly some suitable features of the expected sensor responses, thus allowing the most frequently occurring sensor faults to be isolated. The second stage consists of a fault classifier implemented via a fuzzy inference system, in order to exploit the knowledge of the experts. The proposed strategy has been validated at the Joint European Torus (JET), on several experiments. A comparison has been made with both traditional sensor monitoring techniques and validation performed manually by experts. A great improvement has been achieved, in terms of both fault detection and classification capabilities, and the degree of automation achieved.