摘要
风机在多种工况条件下运行时,利用轴承的振动监测系统所检测到的信号难以实现故障诊断,而大量文献研究的轴承故障诊断多是在恒定转速下进行的。针对变工况下运行的滚动轴承,提出一种基于SHO-VMD分解和多特征参数融合的特征提取方法,使用t-SNE降维可视化,提取出振动信号的故障信息与转速变化信息。变分模态分解(VMD)方法的分解效果取决于分解个数和惩罚因子的取值,采用自私羊群优化算法(SHO)对参数进行优化,将振动信号分解为一些本征模态分量,再对每组分量进行特征参数提取,基于奇异值特征、能量熵、样本熵特征进行多特征量融合,使用t-SNE降维来提取轴承故障信息以及速度变化信息,实验结果表明:提出的方法可以有效提取出轴承的故障和速度信息。
When the fan is running under various working conditions,the signal detected by the vibration monitoring system of the bearing is difficult to realize fault diagnosis,and the fault diagnoses of the bearing studied in a large number of literatures are mostly carried out at constant speed.Aiming at the rolling bearing running under variable working conditions,a feature extraction method based on SHO-VMD decomposition and multi-feature parameter fusion was proposed.The t-SNE dimension reduction visualization was used to extract the fault information of the vibration signal and the speed change information.The decomposition effect of variational mode decomposition(VMD)method depends on the number of decompositions and the value of penalty factors.The parameters were optimized by the selfish sheep swarm optimization(SHO)algorithm.The vibration signal was decomposed into some intrinsic mode components,and then the characteristic parameters of each component were extracted.The multi-feature fusion was carried out based on singular value feature,energy entropy and sample entropy.The t-SNE dimension reduction was used to extract the fault information and speed change information of the bearing.The experimental results show that the proposed method can be used to effectively extract the fault and speed information of the bearing.
作者
刘伟
梁涛
李涛
姜文
LIU Wei;LIANG Tao;LI Tao;JIANG Wen(School of Artificial Intelligence,Hebei University of Technology,Tianjin 300000,China;Hebei Construction Energy Investment Co.,Ltd.,Shijiazhuang Hebei 050011,China)
出处
《机床与液压》
北大核心
2022年第19期185-193,共9页
Machine Tool & Hydraulics
基金
河北省科技支撑计划(19210108D,19214501D,20314501D,F2021202022)资助项目。
关键词
SHO优化算法
变分模态分解
多特征量
滚动轴承
故障诊断
变工况
SHO optimization algorithm
Variational mode decomposition
Multiple features
Rolling bearings
Fault diagnosis
Variable working conditions
