基于EEMD降噪和1.5维能量谱的滚动轴承故障诊断研究

将1.5维谱分析和Teager能量算子相结合，提出了1.5维能量谱的分析方法，并针对滚动轴承故障诊断问题，从提高故障信号信噪比的角度出发，提出基于EEMD降噪和1.5维能量谱的故障诊断新方法。该方法首先对故障信号进行聚合经验模态分解(Ensemble Empirical Mode Decomposition，EEMD)运算，得到一组本征模态函数(Intrinsic Mode Function，IMF)分量后运用相关系数-峭度准则对其进行筛选，并利用筛选出的IMF分量重构信号，最后计算重构信号的1.5维能量谱，从而获得轴承故障特征频率信息。利用该方法对滚动轴承内圈故障的模拟数据以及实测数据分别进行分析，诊断结果令人满意。

Research on Fault Diagnosis for Roller Bearings Based on EEMD De-noising and 1.5-dimensional Energy Spectrum

A method was presented based on 1.5-dimensional energy spectrum. This method combines 1.5-dimensional spectrum and teager energy operator. To solve the fault diagnosis problem of rolling bearings and improve the signal to noise ratio, a new diagnosis method based on EEMD de-noising and 1.5-dimensional energy spectrum was proposed. Firstly, the fault signal was decomposed by ensemble empirical mode decomposition method and a set of intrinsic mode function components were obtained. Secondly, filtered the components by correlation coefficient - kurtosis criteria and the selected IMF components were used to reconstruct signal. Finally, in order to get bearing fault characteristic frequency information, the 1.5-dimensional energy spectrum of reconstructed signal was calculated. Simulation inner fault signal and measured inner fault signal of rolling bearings were analyzed by the proposed method, and the diagnosis results were satisfactory.