RBF neural net for the AIRIX HV generators diagnosis

Summary form only given. The AIRIX facility is a high current linear accelerator (2-3.5 kA) used for flash-radiography at the CEA of Moronvilliers France. The general background of this study is the diagnosis and the predictive maintenance of the AIRIX components. We are interested in the performances of the HV generators, which furnish the energy to accelerate the beam. In a first part, we will present a tool for fault diagnosis based on pattern recognition using an artificial neural network. We use statistical models to define an error vector, representative of the state of the generators, which must be classified. To reduce the redundancy of this information and the computation time, we study two algorithms: the principal component analysis and the curvilinear components analysis. A classifier has been defined, with a three layer Radial Basis Function (RBF) neural network. We initialize the network by applying an unsupervised fuzzy technique to a training base. The configuration of the whole net is realized by supervised training. Membership and ambiguity rejection enable the network to learn unknown failures, and to monitor generator operations to predict future failures. We will present the first results obtained on the generators. In a second part we will briefly describe the experiments, we make to improve the synchronization of the generators to obtain the best acceleration performances. We calculate an absolute time basis to compare the signals of the beam and of the generators to determine the delay to apply to the trigger.