SVD-LESE在滚动轴承微弱故障识别中的应用研究  

Application Study of SVD-LESE in Weak Fault Identification of Rolling Bearing

作　　者：韩春福 李明哲 郭栋 李龙龙 毛玉鹏 HAN Chunfu;LI Mingzhe;GUO Dong;LI Longlong;MAO Yupeng(Shaanxi Shanmei Coal Caojiatan Mining Co.,Ltd.,Yulin Shaanxi 719000,China;School of Mechanical and Precision Instrument Engineering,Xi’an University of Technology,Xi’an Shaanxi 710048,China;Xi’an Sikeda Mechanical Manufacture Co.,Ltd.,Xi’an Shaanxi 710005,China)

机构地区：[1]陕西陕煤曹家滩矿业有限公司,陕西榆林719000 [2]西安理工大学机械与精密仪器工程学院,陕西西安710048 [3]西安斯克达机械制造有限公司,陕西西安710005

出　　处：《机床与液压》2023年第10期210-214,共5页Machine Tool & Hydraulics

摘　　要：矿用电机运行过程中环境噪声强且复杂,其滚动轴承的早期故障特征容易被淹没。提出一种有限包络谱熵(LESE)引导的振动信号奇异值分解方法,用于滚动轴承早期故障特征提取。根据待分解信号中频率和奇异值之间的对应关系,将对应同一振动信号成分的奇异分量进行累加作为一个信号子分量进行输出;提出LESE用来解决轴承微弱故障信号经SVD处理后故障敏感信号分量的筛选;最后通过对故障敏感信号分量进行包络谱分析从而确定滚动轴承的故障类型。实验结果表明:上述方法能够实现对轴承早期故障特征提取,有利于及时发现轴承问题,避免设备进一步劣化。The environmental noise is strong and complex during the operation of mining motor,and the early fault characteristics of the rolling bearing are easily overwhelmed.A singular value decomposition method of vibration signal was proposed with limited envelope spectral entropy(LESE)for the extraction of early fault characteristics of rolling bearings.According to the correspondence between the frequency and the singular value in the signal to be decomposed,the singular components corresponding to the same vibration signal component were accumulated as the output of a signal subcomponent;the entropy LESE was applied to solve the screening of the fault-sensitive signal components after the weak fault signal of the bearing was processed by SVD.Finally,the fault type of the rolling bearing was determined by envelope spectral analysis of the fault-sensitive signal components.The experimental results show that the proposed method can be used to realize the extraction of early fault characteristics of bearings,which is conducive to timely find bearing problems and avoid further deterioration of motor.

关 键 词：奇异值分解 滚动轴承 包络谱熵 微弱故障识别 

分 类 号：TH133.33[机械工程—机械制造及自动化]