Dynamics-Driven Dual-Stream Graph Neural Network with Adaptive Gated Fusion for Gearbox Fault Diagnosis

Conventional data-driven networks for gearbox fault diagnosis process multi-sensor streams as isolated sequences, failing to capture spatial-topological kinetic correlations and structural energy propagation pathways governed by multi-stage gearbox dynamics. To address these limitations, this study proposes a graph neural network-based fault diagnosis methodology integrating multi-dimensional attention and dynamic topological priors (KT-GNN-CBAM). Mesh stiffness characteristics are analytically evaluated to initialize physical topology edge weights, while a convolutional block attention module filters spatio-temporal features to suppress background noise. Node features are subsequently aggregated through a parallel dual-stream architecture comprising a physics-prior kinetic stream and a data-driven attention stream, which are dynamically fused via an adaptive gated mechanism. Experimental validation on the HP-GBS-2023 testbed under mixed operations and 6 dB noise shows that the proposed framework achieves an optimal diagnostic accuracy of 98.87%. Ablation evaluations confirm that omitting the mechanics-driven prior branch induces a 282.30% relative surge in the model’s misclassification rate. Ultimately, embedding mechanical invariants as a physical inductive bias mitigates purely data-driven black-box constraints, offering an interpretable and robust solution for advanced intelligent fault diagnosis in complex gearbox systems.