A novel convolutional neural network with global perception for bearing fault diagnosis

Bearings are key support components in rotating machinery, and their stability is crucial to the reliability of the entire mechanical system. To address the limitations of existing Transformer architectures in edge-side optimization and convolutional neural networks in global feature extraction, especially the resulting poor real-time performance and low accuracy in bearing fault diagnosis based on acoustic signals, this paper proposes a novel global-aware convolutional neural network based on residual masking and position-aware strategies (ParCReSMNet). Firstly, the network is based on the residual network (ResNet-18) and designed with a residual mask block combined with an improved spatial pyramid mask attention mechanism, which effectively removes redundant spatial information and focuses on critical fault features, thereby enhancing the robustness and generalization of the network. Secondly, a position-aware circular module is introduced to replace specific residual blocks in the original network, achieving an effective fusion of positional and global information, thereby augmenting the modeling capability of the convolutional neural network. Experiments are conducted on a selfmade belt conveyor idler dataset and the Detection and Classification of Acoustic Scenes and Events (DCASE) 2023 Task2 bearing dataset, with results showing that ParC-ReSMNet achieves 95.49% and 96.67% accuracy, respectively. Compared to seven state-of-the-art models, it has the highest precision and recall, along with good real-time performance, which demonstrates great application value for fault monitoring of belt conveyors used in coal mines, power plants, ports, and other rotating machinery. The code library is available at: https://github. com/xgli411/Parc-ResMNet.