基于RBF神经网络的加速器故障分类器AIRIX
RBF neural net based classifier for the AIRIX accelerator fault diagnosis
摘要：
AIRIX设施是一个高电流直线加速器（2-3.5kA），用于法国Moronvilliers的CEA的闪光X射线照相术。本研究的总体背景是AIRIX的诊断和预测性维护。我们将提出一种基于人工神经网络模式识别的故障诊断和监测工具。从每个镜头上记录的信号中提取的参数用于定义要分类的向量。主成分分析使我们能够选择最相关的信息，减少冗余。采用三层径向基函数（RBF）神经网络对加速器的状态进行分类。我们初始化的网络应用无监督模糊技术的训练基地。这使我们能够确定聚类和真实类的数量，它们定义了网络隐藏层和输出层上的细胞数量。隐层和输出层之间的权重由最小二乘法确定，实现了聚类的非凸联合。成员资格和模糊性拒绝使网络能够学习未知的故障，并监视加速器操作以预测未来的故障。我们将介绍在喷射器上获得的第一批结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
