基于改进辛几何模态分解的滚动轴承故障信号特征提取方法OA

To address the challenge of extracting fault features from vibration signals in rolling bearing fault diagnosis under strong noise interference,an improved feature extraction method based on symplectic geometry modal decomposition(SGMD)is proposed.In this method,the density of samples around core points is calculated,and then the radius based on density ratios is adaptively adjusted to perform cluster analysis on the initial components obtained through SGMD.And this approach is used to resolve the parameter sensitivity issue encountered during initial component recombination in traditional SGMD.Com-parative simulation experiments demonstrate that the proposed improved SGMD does not require param-eter selection and achieves optimal denoising performance with a signal-to-noise ratio of 22.9 dB.By applying the improved SGMD and short-time Fourier transform time-frequency analysis to vibration sig-nals of normal bearings,inner race faults,and outer race faults,the method is validated to effectively extract the rolling bearing characteristic features.Combining with the AlexNet algorithm,precise fault diagnosis with the highest accuracy rate reaching 98.53％is achieved.