Fault Diagnosis of Hydraulic System of Aircraft Landing Gear Based on CNN-SVM

Aiming at the difficulty of extracting fault features on the aircraft landing gear hydraulic system, traditional feature extraction methods rely heavily on expert knowledge, and the accuracy of fault diagnosis is difficult to guarantee. This paper combined convolutional neural network (CNN) and support vector machine classification algorithm (SVM) to propose a fault diagnosis model suitable for aircraft landing gear hydraulic system. The diagnosis model adopted the one-dimensional multi-channel CNN network structure, took the original pressure signal of multiple nodes as input, adaptively extracts the feature value of the pressure signal through CNN, and built a multi-feature fusion layer to realize the feature fusion of the pressure signal of each node. Finally, input the fused features into the SVM classifier to complete the fault classification. In order to verify the proposed fault diagnosis model, a typical aircraft landing gear hydraulic system simulation model was built based on AMESim, and several typical fault types such as hydraulic pump leakage, actuator leakage, selector valve clogging and accumulator failure were simulated, and corresponding Fault type data set, and use overlapping sample segmentation for data enhancement. Experiments show that the diagnosis accuracy of the proposed fault diagnosis algorithm can reach 99.25%, which can realize the adaptive extraction of the fault features of the aircraft landing gear hydraulic system, and the features after multi-dimensional fusion have better discrimination, compared with traditional feature extraction methods more effective and more accurate.