关键词: 旋转机械, 故障诊断, 声学信号, 轻量化网络, 1D-2D 卷积建模, 多尺度动态注意力

Abstract: To address the strong nonstationarity and significant noise interference commonly present in acoustic signals of rotating machinery,as well as the limitations of existing methods,including insufficient capability in temporal-scale feature modeling,reliance on handcrafted time-frequency transformations,and excessive model complexity that hinders edge deployment,a lightweight end-to-end fault diagnosis model was proposed that integrates Multiscale Dynamic Attention and 1D-2D convolutional Fusion Network (MDAF-Net). The proposed model comprised four key components. First,a Multiscale Dynamic Weighting Feature Extractor (MDW-FE) was constructed to enhance the perception of nonstationary acoustic patterns through the combination of multiscale convolutional kernels and adaptive weighting mechanisms. Second,a Reshaped Multiscale Projection (RMP) layer was designed to transform one-dimensional sequences into two-dimensional structures,thereby preserving temporal-scale dependencies. Third,a Pyramid Convolutional Block Attention Module integrated with Depthwise Separable Convolution (P-CBAM-DSC) was introduced to improve the model′s capability in focusing on fault-sensitive regions and capturing contextual semantics. Finally,a Global Feature Aggregation Classifier (GFA-C) enables efficient and accurate end-to-end fault identification. Experimental results on the public DCASE2023 dataset and a self-built rolling bearing acoustic benchmark demonstrate that the proposed method outperforms mainstream lightweight models in terms of diagnostic accuracy,model compactness,and inference efficiency,while exhibiting excellent performance in noise robustness and suitability for edge deployment.

Key words: rotating machinery, fault diagnosis, acoustic signal, lightweight network, 1D-2D convolutional modeling, multiscale dynamic attention