宏雅与新变：张说颂文管窥

总体经验模式分解 (Ensemble Empirical Mode Decomposition, EEMD) 方法由于其自适应性和抗混叠的特性，在轴承故障诊断领域得到广泛应用。针对总体经验模式分解 (Ensemble Empirical Mode Decomposition, EEMD) 方法中参数难以准确获取的问题，提出了基于改进的EEMD分解和Teager能量算子的滚动轴承故障诊断方法。首先对故障信号进行预处理，自动获取EEMD方法中的加入白噪声大小和总体平均次数两个重要参数。之后对信号进行EEMD分解，得到若干个本征模态分量 (Intrinsic Mode Function, IMF)，利用峭度准则选取其中峭度最大的分量并进行Teager能量算子解调，最后通过能量谱识别出滚动轴承的工作状态和故障类型。将该方法应用到滚动轴承仿真故障数据和实际数据中，实验结果表明，该方法可有效提取滚动轴承故障特征频率信息，验证了所提方法的可行性。

Magnificence, Elegance and New Changes: Focus on Zhang Yue's Songs

With its adaptability and anti aliasing, Ensemble Empirical Mode Decomposition (EEMD) is used widely in rolling bearing fault diagnosis. In order to solve the problem that the parameters of ensemble empirical mode decomposition(EEMD) is difficult to obtain, a method for fault diagnosis of rolling bearing based on modified EEMD and Teager energy operator is proposed. Firstly, the fault signal is preprocessed, the added white noise magnitude and the ensemble times is obtained. Then the fault signal is decomposed into several intrinsic mode function (IMF) by modified EEMD, and the IMF of biggest kurtosis is selected with Kurtosis Criterion and demodulated into Teager energy spectrum with Teager energy operator. Finally, the working status and fault type of rolling bearings is identified through the energy spectrum. The proposed method is applied to simulated signals and actual signals. The results show that the method could extract the weak feature frequency information of incipient fault of rolling bearing effectively.