摘要
将改进的形态学滤波方法与集合经验模态分解(EEMD)方法相结合,提出一种新的滚动轴承故障诊断策略。设计一种基于递归最小二乘算法的自适应形态滤波器对轴承故障振动信号进行消噪处理,利用EEMD自适应地将信号分解为多个分量,通过相关系数方法消除EEMD分解结果中的虚假分量后,从而得到更准确的Hilbert-Huang谱,由此提取故障信息,判断故障类型。通过轴承故障诊断实例证明了该方法的有效性。
A novel method for the fault diagnosis of rolling bearing based on the improved morphological filter and the ensemble empirical mode decomposition（ EEMD） is presented. The noise of the bearing fault signal is reduced by an adaptive morphological filter based on the recursive least squares algorithm,and then the de-noised signal is decomposed into several intrinsic mode functions（ IMFs） via EEMD adaptively. Pseudo-components are removed by using the correlation coefficient method. Finally the more accurate Hilbert-Huang spectrum of IMFs is obtained,and the characteristic frequencies are extracted,thereby the fault is diagnosed. The experiment results shows that the proposed method is effective for extracting fault feature.
出处
《江南大学学报（自然科学版）》
CAS
2015年第5期532-537,共6页
Joural of Jiangnan University (Natural Science Edition)
基金
国家自然科学基金项目(61104183
61174032)
高等学校博士学科点专项科研基金项目(20130093110011)
关键词
集合经验模态分解
形态滤波
RLS算法
滚动轴承
故障诊断
ensemble empirical mode decomposition
morphological filter
RLS algorithm
rolling bearing
fault diagnosis
