Multi-channel adaptive graph neural network fault diagnosis method based on dynamic graph learning

Abstract Traditional graph neural network-based gearbox fault diagnosis methods often exhibit limited adaptability to variations in signal characteristics caused by measurement disturbances and changing operating conditions. This limitation primarily stems from their reliance on predefined static graph structures and fixed adjacency relationships, which constrain the effective modeling of dynamic inter-signal dependencies and may impair diagnostic performance under varying conditions. To address this issue, this paper proposes a multi-channel adaptive dynamic graph neural network (MCADG-Net) for robust fault diagnosis based on dynamic graph learning. First, a dynamic graph learning module is introduced to adaptively construct a global adjacency matrix through a multi-head attention mechanism with regularization, enabling flexible graph topology optimization and improved robustness to measurement perturbations. Second, a multi-scale adaptive Gaussian perception layer is designed to adjust dimension-wise feature weights via parameterized Gaussian functions, enhancing sensitivity to local feature variations. Finally, a multi-level aggregation network is developed to integrate the complementary advantages of GCN-based local smoothing and graph attention network-based global attention weighting, achieving multi-scale feature fusion and improved generalization across fluctuating working environments. Experimental results on two benchmark gearbox datasets demonstrate that the proposed MCADG-Net consistently outperforms representative dynamic GNN models and exhibits improved robustness and predictive reliability under diverse operational states. These findings indicate that MCADG-Net provides an effective and adaptive solution for intelligent measurement-based mechanical fault diagnosis.