A Multi-Task Multimodal Attention Graph Convolutional Network for Acoustic–Vibration Fusion-Based Rolling Bearing Fault Diagnosis

Single-sensor-based fault diagnosis of rolling bearings often suffers from noise sensitivity, installation-dependent performance, and incomplete fault characterization. To address these limitations, this paper proposes a multi-task multimodal attention graph convolutional network (MTMAGNet) that integrates acoustic and vibration signals for bearing fault diagnosis. First, one-dimensional convolutional neural networks are used to extract modality-specific features. These features are then fused through a multi-modal attention mechanism to exploit the complementary information contained in the two signal sources. Based on the fused representations, a dynamic k-nearest neighbor graph is constructed to model relationships among samples, and a graph convolutional network is employed to learn discriminative structural features. Moreover, a multi-task learning scheme is introduced, in which fault classification serves as the primary task and modal classification is used as an auxiliary task to enhance feature learning and improve model generalization. Experimental results on a self-built acoustic–vibration test bench collected under three rotational speeds (1800 rpm, 2400 rpm, and 3000 rpm) demonstrate that the proposed method achieves high diagnostic accuracy and strong generalization performance under different fault conditions.