基于最优提升小波局部熵的轴承故障特征提取

Rolling Bearing Fault Feature Extracting Based on Best Lifting Wavelet Local Entropy

中文关键词:  滚动轴承  最优提升小波  局部熵  无量纲指标

英文关键词:rolling bearing  best lifting wavelet  local entropy  dimensionless index

中文摘要:

      为了解决轴承故障诊断中，传统无量纲指标没有考虑其他噪声干扰，且分析结果有一定偏差等问题，提出一种基于提升小波分解的局部熵无量纲指标特征提取方法。该方法采用最优提升小波进行分解，并结合局部熵提出一种无量纲指标，对滚动轴承振动实验信号进行故障特征提取，并与常用无量纲指标进行对比，验证了该方法的有效性。

英文摘要:

      In order to solve the problems that traditional dimensionless index does not considering other noise interference and the analysis result has certain deviations in fault diagnosis of bearing,a local entropy dimensionless index feature extraction method based on lifting wavelet decomposition is proposed.The method used the optimal lifting wavelet to decompose,and combined the local entropy to propose the dimensionless index.The fault features of the fault signal of rolling bearing vibration experiment are extracted and compared with other commonly used dimensionless index methods,the effectiveness of the proposed method is verified.