小波分析和小波包分析在轴承故障诊断中的对比分析  

Comparative Analysis of Wavelet Analysis and Wavelet Packet Analysis in Bearing Fault Diagnosis

作　　者：于岩[1] 许继秀 张梦超 石浩[1] Yu Yan;Xu Jixiu;Zhang Mengchao;Shi Hao(College of Mechanical and Electronics Engineering,Shandong University of Science and Technology,Qingdao 266590,China)

机构地区：[1]山东科技大学机械电子工程学院

出　　处：《煤矿机械》2019年第12期170-173,共4页Coal Mine Machinery

基　　金：国家重点研发计划资助(2018YFC0604702);山东省自然科学基金资助项目(ZR2018BEE014)

摘　　要：结合轴承故障振动特点,以轴承滚动体故障为例,对比了小波分析和小波包分析在应用时的相同和不同之处。利用MATLAB仿真软件作为分析工具,通过定量分析和定性分析比较了它们在轴承故障信号降噪时的效果,通过计算功率谱、绘制功率谱图的方式比较了它们在轴承故障诊断方面的能力。结果表明,使用相同小波基和相同分解层数条件下,在降噪和故障诊断方面,小波包分析都优于小波分析,为选择合适的轴承故障诊断方式提供了参考。Combining the characteristics of bearing fault vibration,taking the bearing rolling element fault as an example,compared the similarities and differences between wavelet analysis and wavelet packet analysis in application,and used MATLAB simulation software as an analysis tool to compare their effect on the noise reduction of bearing fault signals by quantitative analysis and qualitative analysis.Compared their ability to diagnose bearing fault by calculating the power spectrum and plotting the power spectrum.The results show that the wavelet packet analysis results are superior to the wavelet analysis ability in noise reduction and fault diagnosis under the same wavelet base and the same decomposition layer number,which provides reference for selecting suitable bearing fault diagnosis methods.

关 键 词：小波分析 小波包分析 降噪 故障诊断 

分 类 号：TH133.33[机械工程—机械制造及自动化]