  The AIRIX facility is a high current linear accelerator (2-3.5kA) used for
flash-radiography at the CEA of Moronvilliers France. The general background of
this study is the diagnosis and the predictive maintenance of AIRIX. We will
present a tool for fault diagnosis and monitoring based on pattern recognition
using artificial neural network. Parameters extracted from the signals recorded
on each shot are used to define a vector to be classified. The principal
component analysis permits us to select the most pertinent information and
reduce the redundancy. A three layer Radial Basis Function (RBF) neural network
is used to classify the states of the accelerator. We initialize the network by
applying an unsupervised fuzzy technique to the training base. This allows us
to determine the number of clusters and real classes, which define the number
of cells on the hidden and output layers of the network. The weights between
the hidden and the output layers, realising the non-convex union of the
clusters, are determined by a least square method. Membership and ambiguity
rejection enable the network to learn unknown failures, and to monitor
accelerator operations to predict future failures. We will present the first
results obtained on the injector.
