基于SVD-EEMD和TEO的滚动轴承弱故障特征提取
张琛1; 赵荣珍2; 邓林峰2; 吴耀春2
2019
Source Publication振动、测试与诊断
ISSN1004-6801
Volume39Issue:4Pages:720-726
Abstract将奇异值分解(singular value decomposition,简称SVD)与集合经验模态分解(ensemble empirical mode decomposition,简称EEMD)进行结合,提出一种适用于滚动轴承弱故障状态描述的敏感特征提取方法。为提高信号故障信息的提取质量,对采集信号进行相空间重构得到一种Hankel矩阵。根据该矩阵的奇异值差分谱,确定降噪阶次进行SVD降燥。用EEMD分解降噪后的信号可获得11个本征模态函数(intrinsic mode function,简称IMF)和1个余项。依据建立的峭度-均方差准则,筛选出一个能够有效描述故障状态的敏感IMF分量,计算其相应的Teager能量算子(Teager energy operator,简称TEO),对此TEO进行Fourier变换,实现了对滚动轴承弱故障模式的有效辨识。用美国凯斯西储大学公开的滚动轴承故障信号对所建立的方法与传统EEMD-Hilbert法和EE-MD-TEO方法进行对比,结果表明:经本方法提取的敏感特征能准确突显滚动轴承故障频率发生的周期性冲击,可准确识别其故障类型。
Keyword集合经验模态分解方法 奇异值分解 Teager能量算子谱 特征提取
Indexed ByCSCD
Language中文
WOS Research AreaEngineering
WOS SubjectENGINEERING MECHANICAL
CSCD IDCSCD:6546259
Document Type期刊论文
Identifierhttp://ir.lut.edu.cn/handle/2XXMBERH/75073
Collection机电工程学院
Affiliation1.兰州理工大学机电工程学院;;武警工程大学(乌鲁木齐校区)装甲车技术系, ;;, 兰州;;乌鲁木齐, ;; 730050;;830049
2.兰州理工大学机电工程学院, 兰州, 甘肃 730050, 中国
First Author AffilicationColl Mechanoelect Engn
First Signature AffilicationColl Mechanoelect Engn
Recommended Citation
GB/T 7714
张琛,赵荣珍,邓林峰,等. 基于SVD-EEMD和TEO的滚动轴承弱故障特征提取[J]. 振动、测试与诊断,2019,39(4):720-726.
APA 张琛,赵荣珍,邓林峰,&吴耀春.(2019).基于SVD-EEMD和TEO的滚动轴承弱故障特征提取.振动、测试与诊断,39(4),720-726.
MLA 张琛,et al."基于SVD-EEMD和TEO的滚动轴承弱故障特征提取".振动、测试与诊断 39.4(2019):720-726.
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