改进SVD与EEMD的TEO伺服压机滚动轴承故障提取  

Fault Feature Extraction of Rolling Bearings in Servo Press Based on Improved SVD-EEMD and TEO

作　　者：陆伟 陈长征[1] LU Wei;CHEN Changzheng(School of Mechanical Engineering,Shenyang University of Technology,Shenyang 110870,China)

机构地区：[1]沈阳工业大学机械工程学院,沈阳110870

出　　处：《机械工程师》2021年第9期31-33,37,共4页Mechanical Engineer

摘　　要：针对伺服电动机滚动轴承故障信号受噪声干扰不易识别的问题,提出一种基于改进SVD-EEMD与Teager能量算法相结合的故障诊断方法。该方法首先用改进SVD方法进行信号降噪,随后利用集合经验模态分解(EEMD)对降噪信号进行故障特征提取,最后利用Teager能量算法对故障信号特征进行增强。实验结果表明,文中提出的方法能够有效地去除噪声干扰,对伺服冲压电动机轴承的故障特征信息起到了增强的作用。Aiming at the problem that the fault signal of servo motor rolling bearing is difficult to be identified due to noise interference,a fault diagnosis and detection method based on the combination of improved SVD-EEMD and Teager energy algorithm is proposed.The method first uses the improved SVD method to reduce the noise of the signal.Then,the ensemble empirical mode decomposition(EEMD)is used to extract the fault characteristics of the noise reduction signal.Finally,the Teager energy algorithm is used to further enhance the fault signal.The experimental results show that the method proposed in this paper can effectively remove the surrounding noise interference,and has an enhanced effect on extracting the fault characteristic information of the servo stamping motor bearing.

关 键 词：伺服电动机滚动轴承 故障特征提取 改进SVD方法 集合经验模态分解 Teager能量算法 

分 类 号：TH12[机械工程—机械设计及理论]