基于角域级联最大相关峭度反褶积的滚动轴承早期故障诊断  

Rolling bearing early fault diagnosis based on angular domain cascade maximum correlation kurtosis deconvolution

作　　者：任学平[1] 张玉皓[1] 邢义通 王朝阁[1] 

机构地区：[1]内蒙古科技大学机械工程学院,包头014010

出　　处：《仪器仪表学报》2015年第9期2104-2111,共8页Chinese Journal of Scientific Instrument

基　　金：内蒙古自治区自然科学基金(2012MS0717)项目资助

摘　　要：变转速工况是某些启制动工作制设备常用的工作方式,针对启制动工作制下滚动轴承故障的振动信号呈现非平稳特性,加之现场环境噪声的干扰,难以从原始故障信号中提取特征频率。提出基于角域级联最大相关峭度(CMCKD)的滚动轴承故障诊断方法。首先将时域非平稳故障信号进行角域重采样转换为角域内的平稳信号;然后用级联最大相关峭度反褶积对故障信号进行处理,抑制信号中的噪声,提取信号中的周期冲击成分。通过对仿真和实验数据的分析,验证了角域级联最大相关峭度反褶积方法的有效性。Variable rotation speed is the common working mode of start-stop duty type equipment. Due to the non-stationary characteristic of rolling bearing vibration signal under start-stop duty type working condition and the interference of the environment noise, it is difficult to extract characteristic frequency from original fault signal. This paper puts forward a rolling bearing fault diagnosis method based on angular domain cascade maximum correlated kurtosis deconvolution （CMCKD）. Firstly, the non-stationary time domain fault signal is converted into angular domain stationary signal through angular domain resampling. Then, the obtained fault signal is processed using cascade maximum correlated kurtosis deconvolution to suppress the noise and extract the periodic impact components in the signal. Through analyzing the simulation and experiment data, the effectiveness of the angular domain cascade maximum correlated kurtosis deconvolution method is verified.

关 键 词：滚动轴承 早期故障诊断 角域重采样 最大相关峭度反褶积 

分 类 号：TH17[机械工程—机械制造及自动化] TH825