自组织神经网络航空发动机气路故障诊断

 为克服学习样本依赖于发动机精确模型的问题,提出了一种基于自组织神经网络的发动机智能故障诊断的方法,并运用故障特征提取的数据预处理方式,成功地对航空发动机气路部件的几种典型故障做出正确诊断。为验证网络的抗噪性能,引入了自联想神经网络。研究表明,自组织网络可以脱离发动机模型,并且对测量噪声有良好的鲁棒性,能基本满足航空发动机故障诊断的要求,具有较好的工程应用前景。

Self-Organizing Neural Network Based Fault Diagnosis for Aeroengine Gas Path

To overcome the weakness of dependence on the accurate model, a method using self organizing neural networks for aeroengine fault diagnosis is developed.Because self organizing neural networks can be trained to adjust their own structures and eventually identify the faults only with the sensed data, the precise engine model is not necessary. With the help of the data preprocession,which extracts the features of faults, the neural networks finally get the correct results of fault diagnosis of aeroengine gas path components. Then the autoassociative neural network is used to test the noise rekection abilities of the self organizing networks. Since they can work without the engine model and are robust for noise rejection, the self organizing neural networks are able to meet the application requirements.