Multiscale Channel Attention-Driven Graph Dynamic Fusion Learning Method for Robust Fault Diagnosis

Recently, research on multisensor fault diagnosis under noisy signals has gained significant attention. Due to various degrees of external interference and differences in sensor precision, the signal quality across different channels is inconsistent. These discrepancies are often neglected by fault diagnosis models and are also difficult to capture accurately. To solve this problem, this article introduces a multiscale channel attention-driven graph dynamic fusion network for mechanical fault diagnosis. It can mine the differences in importance among channels at multiple scales and calculate the channel attention weights to enhance the node feature representation. Additionally, a graph dynamic fusion framework for multisource features is proposed to process the subgraphs in parallel, which achieves a deep-level feature fusion and enables dynamic adjustments to the fusion process based on real-time model output. With the proposed graph dynamic reconstruction module, the reliability of the feature fusion process is further improved. In the experimental part, three noise distribution scenarios were simulated to validate the robustness of the proposed method on an axial flow pump and a gearbox. The comparative analysis with various state-of-the-art models and traditional deep learning models confirms the effectiveness and superiority of the proposed method.