基于复合多尺度加权反向模糊散布熵和增强t分布随机邻域嵌入的滚动轴承退化评估OA

To address the problem of accurately detecting the early degradation moment of rolling bearings,a degradation state assessment model was proposed based on CMWRFDE and ATSNE.First,the CMWRFDE algorithm was utilized to extract multi-scale degradation features from the collected vibra-tion signals.Then,the ATSNE algorithm was employed to compress and reduce the dimensionality of the extracted multi-scale features,constructing a comprehensive characteristic indicator that accurately repre-sents the performance changes of the rolling bearing.Finally,the performance degradation assessment re-sult of the rolling bearing was obtained through a health threshold strategy based on Chebyshev's inequal-ity.The results demonstrate that the proposed model accurately characterizes the performance degradation trend of rolling bearings,promptly detects the initial degradation moment,and enables precise assessment of the bearing's degradation state.