关键词:
Geometry
摘要:
Here, aiming at the problem of difficulty in extracting early weak fault features of gearbox teeth surface wear under environmental noise, a gear wear fault diagnosis method based on optimized symplectic geometry mode decomposition-Hilbert envelope logarithmic analysis was proposed. In the new method, Cao algorithm and power spectral density were introduced, and the nearest neighbor fluctuation deviation was proposed to adaptively determine embedding dimension number. Singular value decomposition was used for denoising. Pearson power spectral entropy difference and Minkowski distance were taken as reconstruction criteria to obtain characteristic modal components. Hubert envelope logarithmic analysis method was used to highlight fault frequency components and perform fault diagnosis. This new method could overcome shortcomings of dependency on empirical formulas for embedding dimension number in symplectic geometry modal decomposition, singularity of reconstruction criteria and poor noise robustness. Simulation and experimental results showed that compared with symplectic geometry modal decomposition (SGMD), iterative SGMD, variational mode decomposition and empirical mode decomposition, this new method can effectively extract early gear teeth surface wear fault feature Information, and exhibit better robustness. © 2025 Chinese Vibration Engineering Society. All rights reserved.