SVD-LMD联合降噪和TEO的滚动轴承故障诊断  

Fault Diagnosis of Rolling Bearings based on SVD-LMD Joint De-noising and TEO

作　　者：谢小正[1] 李俊 赵荣珍[1] 崔振琦 Xie Xiaozheng;Li Jun;Zhao Rongzhen;Cui Zhenqi(School of Mechanical&Electronic Engineering,Lanzhou University of Technology,Lanzhou 730050,China;Gansu Zhuding Construction Co.,Ltd.Jiayuguan 735100,China)

机构地区：[1]兰州理工大学机电工程学院,甘肃兰州730050 [2]甘肃筑鼎建设有限责任公司,甘肃嘉峪关735100

出　　处：《机械传动》2021年第6期104-112,共9页Journal of Mechanical Transmission

基　　金：国家自然科学基金面上项目(51675253)。

摘　　要：针对随机噪声背景下滚动轴承局部损伤信息提取困难的问题,提出了一种奇异值分解(Singular value decomposition,SVD)和局部均值分解(Local mean decomposition,LMD)联合降噪,并结合Teager能量算子(Teager energy operator,TEO)的特征提取新方法。首先,利用SVD方法对滚动轴承故障振动信号进行处理,初步剔除背景噪声;然后,使用LMD方法分解降噪后的信号,依据相关系数指标筛分出敏感乘积函数(Product function,PF)并加以重构;最后,对重构的信号进行TEO解调分析,将解调谱中幅值突出的频率成分与故障特征频率理论值进行对比,提取故障信息。结果表明,该方法可有效提取轴承局部损伤的特征频率,最终实现故障诊断。Aiming at the difficulty extracting the local damage information of rolling bearings under thebackground of random noise,a new feature extraction method based on singular value decomposition(SVD)andlocal mean decomposition(LMD)joint de-noising combined with Teager energy operator(TEO)is proposed.First-ly,by using the SVD method,the fault vibration signal of rolling bearings is processed to eliminated the back-ground noise preliminarily.Then,the signal which is denoised by using LMD method is reconstructed after thesensitive product function(PF)is screened out according to the correlation coefficient index.Finally,the recon-structed signal is analyzed by TEO demodulation,the frequency component which amplitude prominent in de-modulation spectrum is compared with the theoretical value of fault characteristic frequency to extract fault infor-mation.The experimental results demonstrate that the method can effectively extract the characteristic frequencyof the local damage information of rolling bearings and the fault diagnosis is realized.

关 键 词：滚动轴承 奇异值分解 局部均值分解 TEAGER能量算子 故障诊断 

分 类 号：TH133.33[机械工程—机械制造及自动化]