变速器齿轮故障特征模糊熵提取方法研究

针对样本熵相似性度量函数的突变问题,提出了一种变速器齿轮故障特征模糊熵提取方法。模糊熵通过引入模糊隶属度函数代替样本熵中的硬阈值判据,可以减小模糊熵对参数的敏感度和依赖性。利用模糊熵作为变速器齿轮故障的特征值进行提取包括变速器齿轮正常、齿面轻度磨损、齿面中度磨损和断齿等4种工况的振动信号,依据不同的故障对应不同的模糊熵分布,对各种故障状态进行分类。变速器齿轮故障识别的实例验证了模糊熵较样本熵具有较好的故障分类能力。

Transmission gear fault feature extraction by using fuzzy entropy

Aiming at the abrupt problem of sample entropy similarity measure function,a gear in transmission fault feature extraction method based on fuzzy entropy was proposed.Fuzzy entropy replaced the hard threshold criterion in sample entropy by introducing fuzzy membership function,thus reducing the sensitivity and dependence of fuzzy entropy on parameters.Fuzzy entropy was extracted as the characteristic value of transmission gear failure,the tested signal types were normal,slight-worn,medium-worn and broken-teeth.Due to the different fault type correspondence with different fuzzy entropy,it was used as fault feature to evaluate the different fault condition.Practical results proved that fuzzy entropy had better classification ability than sample entropy.