基于超曲空间几何结构感知的轴承多模态故障诊断方法OA

To address the issue that bearing fault diagnosis methods were often sensitive to outliers and struggle to fully capture the underlying geometric structure of the multimodal data,a bearing multimodal fault diagnosis method based on hyperspace geometric structure perception(HGSP)was proposed.Firstly,the original multimodal features were uniformly mapped into a hyperspace constructed using the Poincaré ball model,enabling the characterization of latent nonlinear structural relationships among data.Then,a structure-preserving strategy based on cosine similarity was designed to perceive intra-modal semantic consistency and enhance inter-modal feature fusion.Finally,the method took into account the geometric differences and directional similarities among multi-modal data.It enhanced the robustness against outliers and the ability to perceive the geometric structure of the data,significantly improving the separability of fault categories and the diagnostic accuracy.Experiments were conducted on the Paderborn University(PU)bearing dataset and the self-developed experimental platform roadheader dataset.Compared with the locality preserving canonical correlation analysis(LPCCA)method,the average recognition accuracy of the HGSP method on the PU dataset was improved by 2.00 percentage points,and by 1.83 percentage points on the roadheader dataset.The results indicated that the method can effectively enhance the discriminability of fault categories and demonstrate practical value in bearing fault diagnosis.