Self-attention Pooled Graph Neural Network Fault Diagnosis of Vehicle Outrigger Hydraulic System
Aimed at the complexity and feature aliasing of fault signals in the hydraulic system of special vehicle support legs,a fault diagnosis method for vehicle support leg hydraulic system based on self-attention pooling graph neural network is proposed,and common fault modes and failure mechanisms of vehicle support legs are introduced.Convert the fault signal into a 2D feature map representation and propose an improved 3D structured feature map.Taking the fault feature map as input,combining graph convolution with self-attention pooling for feature extraction,and then classifying and recognizing the extracted features through a fully connected layer.The results show that compared with 2D feature map,the proposed 3D feature map improves the diagnostic accuracy of the model by 2％to 3％;Compared with the original pooling method,the graph neural network with self-attention mechanism has improved the accuracy by 7％to 8％on the support leg fault dataset,demonstrating high diagnostic accuracy and stability,providing a method reference for hydraulic system fault diagnosis.
万方数据
维普
