摘要
针对滚动轴承工作环境恶劣、故障信号难以提取的问题,提出一种基于EEMD-GWO-VMD的滚动轴承双重降噪方法。首先,利用集合经验模态分解(EEMD)对采集到的信号进行分解,通过相关系数和峭度指标组合筛选富含故障信息的分量并进行重构。然后,以包络熵为目标函数,采用灰狼算法(GWO)优化变分模态分解(VMD)的惩罚因子和模态分解层数,并采用仿真信号对比分析VMD、GWO-VMD和EEMD-GWO-VMD这3种方法的降噪效果。最后,结合CWRU数据集和高速列车轴箱轴承台架试验数据,进一步验证EEMD-GWO-VMD降噪方法的有效性。
Based on EEMD-GWO-VMD,a dual noise-reduction method for rolling bearings is proposed to address the problem of poor working environment and difficulty in extracting fault signals.Firstly,utilizing ensemble empirical mode decomposition(EEMD)to decompose the collected signals,filtering out components rich in fault information through a combination of correlation coefficients and kurtosis indicators and reconstructing them.Then,with envelope entropy as the objective function,t he grey wolf optimizer(GWO)algorithm is used to optimize the penalty factor and number of modal decomposition layers of variational mode decomposition(VMD),and the noise reduction effects of VMD,GWO-VMD,and EEMD-GWO-VMD are compared and analyzed using simulation signals.Finally,the effectiveness of the EEMD-GWO-VMD noise reduction method was further verified by combining the CWRU dataset and high-speed train axle box bearing bench test data.
作者
张涛
张振彬
谢剑龙
ZHANG Tao;ZHANG Zhenbin;XIE Jianlong(School of Mechanical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,Gansu,China)
出处
《中国工程机械学报》
北大核心
2025年第3期470-475,共6页
Chinese Journal of Construction Machinery
基金
甘肃省青年科技基金资助项目(20JR10RA270)
甘肃省教育科技创新基金资助项目(2022A-044)。
