关键词: 故障诊断, 直升机尾传动系统, 图卷积网络, 域泛化, 动力学建模

Abstract: To address the challenges of limited access to high-quality fault data and the poor generalization performance of traditional transfer learning methods on unseen fault types, this paper proposes an Adaptive-weight Multi-level Fusion Graph Domain Generalization (AMFGDG) framework for fault diagnosis of helicopter tail transmission systems. Specifically, multi-node vibration signals under various health conditions are generated by integrating the concentrated mass method with the finite element method, and their validity is experimentally verified. Graph-structured samples with finite element nodes are constructed from the generated data for multiple source domains, while test data from different sensors are formulated as target domain graphs. A self-attention mechanism is employed to adaptively update edge weights, enabling precise modeling of inter-node influence. Additionally, a mixture-of-experts gating strategy is introduced to fuse information across graph layers and mitigate the over-smoothing effect in deep GNNs. The model is optimized via a joint loss function combining classification loss, deep clustering loss, and local distribution alignment loss. Diagnostic results and ablation studies on a private helicopter tail transmission test bench dataset and the public CWRU bearing dataset demonstrate the proposed method’s low data dependency and strong generalization capability, achieving an accuracy of up to 95.4%.

Key words: Fault diagnosis, Helicopter tail transmission system, Graph convolutional networks, Domain generalization, Dynamic modeling