Aiming at the problem that fault identification of aviation hydraulic pipeline is difficult due to noise interference,a deep learning hydraulic pipeline fault diagnosis method based on Bi-GRU was proposed.The Bi-GRU neural network model was used to extract time series features from hydraulic pipeline data.Then,based on the measured vibration data of the hydraulic pipeline with the same noise,the data were input into five fault diagnosis models including Bi-GRU,GRU,RNN,SVM and BPNN for training.Finally,in order to further demonstrate the learning ability of BI-GRU model for the characteristics of different fault types of aviation hydraulic pipelines,t-SNE dimension reduction algorithm was used to visualize the characteristics of hydraulic pipelines.The results show that:based on Bi-GRU aviation fault diagnosis method,the accuracy can reach 99.60%,which is obviously better than the other four kinds of neural network model such as GRU,the Bi-GRU model shows better feature extraction ability on the hydraulic pipeline data containing noises,which can effectively extract the hydraulic pipeline fault data characteristics,so as to realize the intelligent identification of the hydraulic pipeline fault.