Graph feature-enhanced DDPM-based data augmentation method for intelligent fault diagnosis of rolling bearings with small samples

Abstract In practical industrial applications, fault samples of critical components such as rolling bearings are often scarce, thereby limiting the diagnostic performance of deep learning models under small samples scenarios. To address this issue, a graph feature-enhanced denoising diffusion probabilistic model (GF-DDPM) is proposed to generate high-quality fault samples and improve the accuracy of intelligent fault diagnosis. The proposed method utilizes a dynamic graph feature construction strategy to transform time–frequency representations into undirected graphs, enabling explicit modeling of spatial–frequency correlations among pixels. Furthermore, a residual block (GF-RB) is incorporated into the diffusion process to effectively fuse local spatial features extracted by convolutional neural network with global structural information captured by graph convolutional network, while gated feature fusion mechanisms are employed to enhance feature representation. Finally, high-quality fault samples are generated using the improved residual U-Net architecture, and a multidimensional comprehensive evaluation is performed based on the constructed comprehensive quality index Q FSP . Experimental results on public and private datasets demonstrate that the proposed GF-DDPM significantly enhances sample diversity and diagnostic accuracy, offering an efficient and feasible solution for intelligent fault diagnosis with small samples scenarios.