摘要
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EEMD-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。
A sensitive feature extraction method is proposed to describe the weak state of rolling bearing.It combines the singular value decomposition(SVD)with the ensemble empirical mode decomposition(EEMD).The Hankel matrix is reconstructed by phase space reconstruction of the collected signal to improve the quality of signal failure.The order of noise reduction is determined according to the singular value difference spectrum of the matrix.The noise-reduced signals are decomposed into 11 intrinsic mode functions(IMF)and one residual using EEMD.According to the established kurtosis-mean square error criterion,one of the most effective states sensitive IMF is selected,and its corresponding Teager energy operator(TEO)is calculated,The identification of weak failure mode of rolling bearing is realized by Fourier transform of TEO.The new method is compared with the traditional EEMD-Hilbert method and EEMDTEO method in case of the opening rolling bearing fault signal of the US west reserve university.The results show that the sensitive features extracted by this method can accurately identify the cycle frequency of rolling bearing fault and accurately identify the fault type,which provides an effective method for the weak feature extraction of rolling bearing.
作者
张琛
赵荣珍
邓林峰
吴耀春
ZHANG Chen;ZHAO Rongzhen;DENG Linfeng;WU Yaochun(School of Mechanical & Electronic Engineering,Lanzhou University of Technology Lanzhou,730050,China;Armored Vehicle Technology Department ,Engineering University of PAP ,Campus of Urumqi Urumqi ,830049 ,China)
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2019年第4期720-726,900,共8页
Journal of Vibration,Measurement & Diagnosis
基金
国家重点研发计划资助项目(2016YFF0203303)
国家自然科学基金资助项目(51675253)
关键词
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
ensemble empirical mode decomposition(EEMD)method
singular value decomposition
Teager energy spectrum
feature extraction
