基于改进多尺度模糊熵的滚动轴承故障诊断方法
A Rolling Bearing Fault Diagnosis Approach Based on Improved Multiscale Fuzzy Entropy

滚动轴承故障诊断的关键是敏感故障特征的提取。多尺度模糊熵（multi-scale fuzzy entropy，简称MFE)是一种衡量时间序列复杂性的有效分析方法，已经被用于滚动轴承振动信号故障特征提取。针对MFE算法中多尺度粗粒化过程存在的缺陷，笔者采用滑动均值的方式代替粗粒化过程，提出了改进的多尺度模糊熵算法，并通过仿真信号将其与MFE进行了对比分析。在此基础上，提出了一种基于改进多尺度模糊熵与支持向量机的滚动轴承故障诊断方法。最后，将所提故障诊断方法应用于的滚动轴承实验数据分析，并与基于MFE的故障诊断方法进行了对比，结果验证了所提方法的有效性和优越性。
The key of rolling bearing fault diagnosis is the extraction of sensitive fault features. Multiscale fuzzy entropy (MFE) is an effective analysis method for complexity measurement of time series and has been used for fault features extraction from rolling bearing vibration signals. Considering the defects existed in the MFE coarse graining , its process is replaced by sliding average and then an improved MFE algorithm is proposed in this paper. It is also compared with MFE by using simulation signal analysis. In this case, a new fault diagnosis method for rolling bearing is proposed based on the improved multiscale fuzzy entropy and support vector machine. Finally, the proposed fault diagnosis method is applied to data analysis of rolling bearing experiment by comparing with the traditional MFE method, and the analysis results verify the effectiveness and superiority of the proposed method.