小波变换在滚动轴承故障信号分析中的应用  

Application of Wavelet Transform in Fault Signal Analysis of Rolling Bearings

作　　者：王舒玮 

机构地区：[1]山西大同大学机电工程学院,山西大同037003

出　　处：《山西大同大学学报（自然科学版）》2018年第1期69-71,共3页Journal of Shanxi Datong University(Natural Science Edition)

摘　　要：通过使用快速傅立叶变换对滚动轴承正常运转时和滚动轴承内圈滚道有裂纹缺陷时的信号进行处理,发现快速傅立叶变换的频谱结果对滚动轴承的故障特征提取不理想,故采用小波变换对故障信号进行分解,随后对分级的细节信号和近似信号进行Hilbert包络频谱处理。结果发现,轴承故障信号在bior2.4小波分解后得到的a3近似信号的Hilbert包络谱中故障特征较明显,而其余的细节信号和近似信号则几乎难以识别相应的故障频率特征。In this paper, the fast Fourier transform(FFT) is used to deal with the signal in the normal operation of the rolling bearing and the crack in the inner race of the rolling bearing. It is found that the frequency spectrum of the fast Fourier transform is not ideal for the fault feature extraction of the rolling bearing. So this paper uses wavelet transform to decompose the fault signal, and then the Hilbert envelope spectral is applied to process the detailed detail signals and the approximated signals. The results show that the fault features of the a3 signal are more obvious in the Hilbert envelope spectrum of the bior 2.4 wavelet decomposition, while the rest of the detail signals and the approximate signals are almost impossible to identify the corresponding fault frequency characteristics.

关 键 词：快速傅立叶变换 滚动轴承 小波变换 

分 类 号：TH164[机械工程—机械制造及自动化]