自适应变结构神经网络在航空发动机故障诊断上的应用

APPLICATION OF ADAPTIVE VARIABLE NEURAL NETWORK TO AEROENGINE FAILURE DIAGNOSIS

根据样本空间的内积特性，提出一种无需迭代学习的自适应变结构神经网络。它的特点是学习速度快，准确性高，且能根据出现的新样本，随时改变结构。在对某型航空发动机故障诊断中，比原软件包采用的方法在准确性方面有显著提高，而且维护工作量减少，并具有实时处理能力，因此有着良好的推广应用前景。

A new adaptive variable neural network is developed according to the inner product feature of sample space.This method does not need to iterate learning.It features that its self-learning velocity is fast,accuracy is high,and it is able to change self-structure on work according to producing new sample.It has been applied to a type of aeroengine start system,its accuracy of failure diagnosis is prior to the original one,and the maintenance work is cut down.Moveover it has capability for real-time failure diagnosis,which opens up bright prospect for its propagation.