基于CEEMD和MOMEDA的滚动轴承故障提取  

Fault extraction of rolleing bearing based on CEEMD and MOMEDA

作　　者：于元滐 杨光永[1] 晏婷 徐天奇[1] 戈一航 Yu Yuanjie;Yang Guangyong;Yan Ting;Xu Tianqi;Ge Yihang(School of Electrical and Information Technology,Yunnan Minzu University,Kunming 650500,China)

机构地区：[1]云南民族大学电气信息工程学院,昆明650500

出　　处：《电子测量技术》2021年第22期96-101,共6页Electronic Measurement Technology

基　　金：国家自然科学基金项目(61761049,61261022)资助。

摘　　要：当滚动轴承发生故障时,故障特征信号会夹杂在振动信号中,造成故障特征信号提取效果不理想。针对这一问题,提出了一种互补集成经验模态分解与多点最优最小熵(CEEMD-MOMEDA)的滚动轴承故障提取方法。首先通过CEEMD算法对采集到的振动信号进行处理,然后通过峭度准则对非故障冲击成分进行筛除,最后利用MOMEDA算法对重组后的信号进行处理从而抑制噪声的影响,从中提取出故障特征。并与单一的MOMEDA算法进行对比。结果表明,提出的CEEMD-MOMEDA算法故障提取能力、抗干扰能力有较大提升。When the rolling bearing fails,the fault characteristic signal will be mixed in the vibration signal,resulting in unsatisfactory extraction effect of the fault characteristic signal.To solve this problem,a fault extraction method for rolling bearings based on complementary integration of empirical mode decomposition and multi-point optimal minimum entropy(CEEMD-MOMEDA)is proposed.At first,the collected vibration signals are processed by CEEMD algorithm,and then the non-fault impact components are screened out by kurtosis criterion.finally,the recombined signals are processed by MOMEDA algorithm to suppress the influence of noise and extract fault features.And compared with the single MOMEDA algorithm.The results show that the fault extraction ability and anti-interference ability of the proposed CEEMD-MOMEDA algorithm are greatly improved.

关 键 词：滚动轴承 故障提取 CEEMD算法 MOMEDA算法 

分 类 号：TH133.33[机械工程—机械制造及自动化]