基于小波阈值降噪算法的滚动轴承故障诊断  

Rolling bearing fault diagnosis based on the|wavelet threshold denoising algorithm

作　　者：竺德 李鑫 高清维[1] 卢一相[1] 孙冬[1] ZHU De;LI Xin;GAO Qingwei;LU Yixiang;SUN Dong(School of Electrical Engineering and Automation,Anhui University,Hefei 230601,China)

机构地区：[1]安徽大学电气工程与自动化学院,安徽合肥230601

出　　处：《安徽大学学报（自然科学版）》2024年第4期50-56,共7页Journal of Anhui University(Natural Science Edition)

基　　金：国家自然科学基金资助项目(62071001);安徽省自然科学基金资助项目(2208085QF206,2308085QF224,2008085MF183);安徽省高校自然科学重点科研项目(KJ2021A0013)。

摘　　要：滚动轴承因平稳的运行特性广泛用于工业生产领域,其安全稳定运行对工业生产有重要意义.针对滚动轴承的故障诊断问题,提出基于小波阈值降噪(wavelet threshold denoising,简称WTD)算法.研究结果表明:相对于其他3种算法,WTD算法具有较强的故障诊断能力.因此,WTD算法具有有效性.Rolling bearings are widely used in industrial production due to their smooth operation,and their safe and stable operation is of great significance to industrial production.Aiming at the fault diagnosis of rolling bearings,a wavelet threshold denoising(WTD)algorithm was proposed.The results showed that compared with the other three algorithms,WTD algorithm had strong fault diagnosis ability.Therefore,WTD algorithm had effectiveness.

关 键 词：滚动轴承 故障诊断 小波阈值降噪算法 调幅调频模型 北方苍鹰优化算法 支持向量机 

分 类 号：TH133.3[机械工程—机械制造及自动化]