摘要
由于滚动轴承早期故障信号特征微弱,变分模态分解(Variational Mode Decomposition,VMD)的性能易受模态数和惩罚因子设置的影响,提出了一种自适应优化VMD参数的方法。基于中心频率判断本征模态函数(Band Limited Intrinsic Mode Functions,BIMF)是否混叠的思想提出中心频率混叠商算法,利用最小中心频率差与次小中心频率差的比值确定模态数。利用模糊熵原理,提出求和模糊熵算法优化惩罚因子。利用相关系数筛选模态分量,重构信号提取故障信息。通过对强噪声下外圈故障信号、内圈故障信号的分析,表明该方法能自适应确定模态数和惩罚因子,抑制模态混叠,能够从强噪声下有效地提取出故障信号特征,实现滚动轴承故障诊断。
The early fault signal characteristics of the rolling bearing are weak.The performance of traditional variational mode de⁃composition(VMD)depends on the parameters,which include mode number and penalty coefficient.To solve this problem,an adaptive method to determine parameters of VMD was proposed.The minimum center frequency quotient algorithm was proposed based on the idea of the center frequency to judge whether the bend limited intrinsic mode function(BIMF)is overlapped or not,and the mode number was determined by the ratio of the minimum frequency and the sub small frequency.By using the fuzzy entro⁃py principle,the sum fuzzy entropy(SFE)was proposed to optimize the penalty coefficient.The correlation coefficient was used to select the BIMFs.The method can adaptively determine the mode number and penalty factor,suppress the mode aliasing phenome⁃non.it can extract the fault signal features from the strong noise to judge the bearing state.
作者
李可
牛园园
宿磊
顾杰斐
卢立新
LI Ke;NIU Yuan-yuan;SU Lei;GU Jie-fei;LU Li-xin(Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology,School of Mechanical Engineering,Jiangnan University,Wuxi 214122,China)
出处
《振动工程学报》
EI
CSCD
北大核心
2023年第1期280-287,共8页
Journal of Vibration Engineering
基金
国家自然科学基金资助项目(51775243,11902124)
山东省泰山产业领军人才计划资助项目
高等学校学科创新引智计划(111计划)(B18027)。
