基于多尺度熵的滚动轴承故障诊断方法

A Rolling Bearing Fault Diagnosis Approach Based on Multiscale Entropy

针对滚动轴承故障振动信号具有不同复杂性的特点,提出了一种新的基于多尺度熵(multi-scale entropy,简称MSE)和支持向量机的滚动轴承故障诊断方法.该方法首先利用MSE方法对滚动轴承不同类型振动信号进行故障特征提取,然后与样本熵方法对比说明MSE方法相对于样本熵方法的优势,最后通过适合小样本分类的支持向量机作为分类器来识别滚动轴承故障类型.对实验数据分析的结果表明,该方法能有效地实现滚动轴承故障类型的诊断.

When the rolling bearing works in fault condition, the complextty o2 tiae vtbratilon stgnal will change. Sample entropy （SE） is defined to measure the complexity of time series in single scale, while multiscale entropy （MSE） is used to measure the complexity of time series in different scales, which con- tains much more information. Based on this, a new rolling bearing fault diagnosis approach based on MSE and SVM was put forward. In this paper, the concepts of SE and MSE were introduced, then MSE was applied to extract the feature information from bearing vibration signals and SVM was used to identify the rolling hearing fault categories. Finally the proposed approach was applied to the experimental data, and the analysis results indicate that the proposed approach can fulfill the roiling bearing fault classification ef- fectively.