关键词: font-size:15.04px, ">故障诊断；民机液压系统；卷积神经网络；残差结构；全局均值池化；挤压与激励网络

Abstract: Aiming at the dependence of expert experience on fault diagnosis of civil aircraft hydraulic system and the degradation of deep network diagnosis model, an improved one-dimensional convolutional neural network algorithm is proposed. Firstly, the simulated fault data is directly inputted into the one-dimensional convolutional neural network, and then the residual block mechanism is used to improve the utilization of information in the convolutional layer. The squeeze and excitation network is introduced to weight the feature vectors of the convo lutional layer, so as to reduce the invalid information and achieve anti-interference effect. Then one-dimension al global average pooling layer is used to process the information of last layer to reduce the neural network parameters and diagnosis time. Finally, in order to verify the effectiveness and practicability of the proposed method, this method is tested by the fault data of the aircraft elevator hydraulic system obtained by the laboratory simulation platform, and compared with the mainstream algorithm. The experimental results show that the test set accuracy of the proposed method is as high as 99.3%. Compared with other networks, the accuracy and generalization of fault diagnosis of hydraulic system are significantly improved. With 20% noise added environment, the accuracy of the proposed network is 4.4% higher than that of the traditional convolutional network, and it has strong practicability.

Key words: fault diagnosis, civil aircraft hydraulic system, convolutional neural network, residual structure, global average pooling, squeeze and excitation network