摘要
为提取微弱的轴承故障信号,研究了一种基于最小熵反褶积(Minimum Entropy Deconvolution,MED)的滚动轴承故障特征提取方法:在利用AR模型去除齿轮啮合产生的确定性信号的基础上,对保留信号进行最小熵反褶积,增强冲击信号。该方法避免了传统轴承故障诊断方法中带通滤波器设计的难题,实车测试表明:与共振解调技术相比,该方法提取的滚动轴承故障特征更加明显,更适合于工程应用。
In order to extract the weak bearing fault signal,a method of rolling bearing fault feature extraction based on Minimum Entropy Deconvolution(MED) is studied.After the AR model is used to remove the deterministic signal generated by gear engagement,the MED is applied to retention signals for enhancing impulses.This method avoids a design aporia of band-pass filter in the traditional bearing fault diagnosis method.The armored vehicle experiment results show that compared with the resonate demodulation technology,this method extracts more obvious fault features of rolling bearing and more adaptable to engineering application.
出处
《装甲兵工程学院学报》
2013年第3期35-38,共4页
Journal of Academy of Armored Force Engineering
基金
军队科研计划项目
关键词
滚动轴承
故障诊断
最小熵反褶积
rolling bearing
fault diagnosis
Minimum Entropy Deconvolution(MED)
