基于SVD-EEMD和TEO的滚动轴承弱故障特征提取

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。

Weak Fault Feature Extraction Method for Rolling Bearings Based on SVD EEMD and TEO Energy Spectrum

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 EEMD-TEO 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.