基于广义变分模式分解的滚动轴承故障微弱特征提取  

Weak Feature Extraction of Rolling Bearing Fault Based on Generalized Variational Mode Decomposition

作　　者：郭燕飞[1] 陈高华[1] 王清华[1] Guo Yanfei;Chen Gaohua;Wang Qinghua(School of Electronic Information Engineering,Taiyuan University of Science and Technology,Taiyuan 030024,China)

机构地区：[1]太原科技大学电子信息工程学院,山西太原030024

出　　处：《机械传动》2023年第5期150-157,共8页Journal of Mechanical Transmission

基　　金：山西省重点研发计划项目(201903D121137);太原科技大学博士科研启动基金项目(20212038);太原科技大学校教学改革创新项目(JG202286)。

摘　　要：针对变分模式分解(Variational Mode Decomposition,VMD)算法在微弱特征分量按需提取方面存在的不足,提出采用广义变分模式分解(Generalized Variational Mode Decomposition,GVMD)算法提取滚动轴承故障微弱特征。GVMD算法具有优良的频域多尺度定频分解性能,算法频谱分解位置和频域分解尺度可由先验中心频率和尺度参数灵活控制,实现按需分解。仿真和实验分析结果表明,与VMD算法相比,GVMD算法能够充分利用轴承故障频率信息和带宽信息,按需准确提取轴承故障微弱特征分量;且具有较强的噪声鲁棒性。Aiming at the deficiency of variational mode decomposition(VMD)in on-demand extraction of weak feature components,a generalized VMD(GVMD)is proposed to extract the weak features of rolling bearing faults.GVMD has excellent multi-scale and fixed frequency decomposition performance in the frequency domain.The spectrum decomposition positions and frequency domain decomposition scales of the algorithm can be flexibly dominated by prior center frequencies and scale parameters to realize on-demand decomposition.The simulation and experimental results show that,compared with VMD,GVMD can accurately extract weak feature components of bearing faults as desired by taking full advantage of bearing fault frequency information and bandwidth information,and the algorithm is robust to noise.

关 键 词：变分模式分解 滚动轴承故障 微弱信号提取 按需分解 

分 类 号：TH133.33[机械工程—机械制造及自动化]