改进WMRA在滚动轴承故障诊断研究中的应用  

Application of improved WMRA in diagnosis of rolling bearing defects

作　　者：林思苗 张艳荣[1] 郭丽萍[1] 

机构地区：[1]西南交通大学机械工程学院,四川成都610031

出　　处：《机电工程》2017年第11期1255-1258,1303,共5页Journal of Mechanical & Electrical Engineering

基　　金：国家自然科学基金资助项目(51475387);四川省科技创新苗子工程资助项目(2015102)

摘　　要：针对滚动轴承初期故障诊断时故障特征信号微弱,且传统的包络谱分析方法需要预先依靠经验确定出分析频段的问题,提出了基于经验模态分解(EMD)和改进的小波多分辨率分析(WMRA)的诊断方法。首先通过对滚动轴承故障振动信号进行EMD分解,利用峭度系数和振动固有频率特征参数对分解后的本征模态函数(IMF)分量进行了分类,筛选出了最佳IMF分量,然后通过希尔伯特变换(HT)计算得到了所选IMF分量的包络信号,最后利用改进后的WMRA对包络信号进行了重构,所得到的包络谱明显地突出了故障特征频率。实验结果表明:相比单独的EMD或传统的WMRA,该方法有效地提高了信号分析的准确性。Aiming at the problem of the weak signal of defects and the traditional envelope spectrum needed to be estimated its frequency band through experience,the combination of empirical mode decomposition and optimized WMRA were put forward. Firstly the vibration signal was resolved through EMD,and the kurtosis and the characteristic parameters of vibration natural frequency were used to classify the component of intrinsic mode function,to screen out its the optimal component whose envelope signal can be calculated through Hilbert transform. The wavelet multi-resolution analysis was used to reconstruct the envelope signal to obtain the envelope spectrum which presented the frequency of characteristics of defects obviously. The result of the research indicates that the method proposed can be used to do visualization analysis of time and frequency domain of signal of defects more effectively,when compared with the solo EMD or WMRA.

关 键 词：轴承故障诊断 经验模态分解 小波多分辨率分析 本征模态函数 希尔伯特变换 

分 类 号：TH133.33[机械工程—机械制造及自动化] TH39