Graph Neural Network With Directional Information and Global Perception Fusion for UAV Fault Diagnosis

Fault diagnosis is critical to flight safety for multirotor uncrewed aerial vehicles (UAVs). While graph neural networks (GNNs) have shown validity in modeling complex structural data, conventional approaches relying on undirected graphs and local message-passing struggle with graph heterophily and limited receptive fields. To overcome these limitations, this article proposes directional information and global perception fusion (DIGPGNN)—a novel GNN architecture that integrates for enhanced UAV fault diagnosis. Initially, a directed graph is constructed based on feature similarity to reduce graph heterophily. Subsequently, an adaptive bidirectional aggregation GNN (ABAGNN) is used to adaptively aggregate the feature information of bidirectional neighbor nodes, thereby enhancing the model’s focus on homophilic neighborhood nodes. Finally, the efficient graph transformer (EGT) incorporates a global self-attention strategy to learn the optimal graph structure by connecting distant homophilic nodes. Experimental results validate the effectiveness of DIGPGNN, achieving a 4.01% and 1.88% improvement in classification accuracy on public and self-built UAV datasets, respectively.