基于神经网络预测模型的歼击机结构故障检测方法

 提出了一种基于预测神经网络的歼击机结构故障检测新方法 ,与传统的基于模型的非线性系统的故障检测方法相比 ,神经网络方法有着非线性逼近能力强和故障检测实时性好等优点。给出了基于预测神经网络的故障检测方案 ,以及多步直接预测算法和阈值选取原则 ,最后以某型歼击机为例进行了仿真验证 ,仿真结果表明本方法能有效地检测出歼击机的各种结构故障。

STRUCTURE FAULT DETECTION BASED ON NEURAL NETWORK PREDICTION MODEL FOR A FIGHTER

This paper describes the application of neural networks for structure failure detection for a fighter. As compared with traditional model based failure detection for nonlinear systems, neural network methods have the advantages of strong nonlinear approximation ability and fast detection. A prediction neural network scheme for fault detection has been developed, along with multiple step direct prediction algorithm and threshold selection principle in this paper. Finally, the proposed scheme is demonstrated using the model of a fighter and the results show that the neural network method is an effective tool for structure fault detection of a fighter.