  In this paper, an automatic sensor validation strategy for the measurements
of plasma line density in the ENEA-FTU tokamak is presented. Density
measurements are performed by a 5-channel DCN interferometer. The approach
proposed is based on the design of a neural model of the observed system., i.e.
a model able to emulate the behavior of a fault-free sensor and of a two-stage
fuzzy system able to detect the occurence of a fault by using a set of suitable
indicators. The fault diagnosis and classification is also accomplished. The
validation strategy has been implemented and embedded in an interactive
software tool installed at FTU. Statistics concerning the rate of fault
detection agree with the rate of uncertainty usually achieved in the post-pulse
manual validation.
