J.C. Ribes*, G. Delaunay* J. Delvaux**, E. Merle**, M. Mouillet**
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 the study is the diagnosis and the predictive maintenance of AIRIX. We will present a tool for fault diagnostic and monitoring based on pattern recognition using artificial neural network. Parameters extracted on 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 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, wich 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. Distance 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 accelerator
* URCA - UniversitH de Reims Champagne-Ardenne - F51687 REIMS cedex2 France
** CEA - PEM F51490 Pontfaverger - France
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