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Generalized empirical mode decomposition and its applications to rolling element bearing fault diagnosis

机译：广义经验模式分解及其在滚动轴承故障诊断中的应用

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As an adaptive time-frequency-energy representation analysis method, empirical mode decomposition (EMD) has the attractive feature of robustness in the presence of nonlinear and non-stationary data. It is evident that an appropriate definition of baseline (or called mean curve) of data plays a crucial role in EMD scheme. By defining several baselines, an adaptive data-driven analysis approach called generalized empirical mode decomposition (GEMD) is proposed in this paper. In the GEMD method, different baselines are firstly defined and separately subtracted from the original data, and then different pre-generated intrinsic mode functions (pre-GIMFs) are obtained. The GIMF component is defined as the optimal pre-GIMF among the obtained ones with the smallest rate of frequency bandwidth to center frequency. Next, the GIMF is subtracted from the original data and a residue is obtained, which is further regarded as the original data to repeat the sifting process until a constant or monotonic residue is derived. Since the GIMF in each frequency-band is the best among different pre-GIMFs derived from EMD and other EMD like methods, the GEMD results are best as well. Besides, a demodulating method called empirical envelope demodulation (EED) is introduced and employed to analyze the GIMFs in time-frequency domain. Furthermore, GEMD and EED are contrasted with the original Hilbert-Huang Transform (HHT) by analyzing simulation and rolling bearing vibration signals. The analysis results indicate that the proposed method consisting of GEMD and EED is superior to the original HHT at least in restraining the boundary effect, gaining a better frequency resolution and more accurate components and time frequency distribution.
机译：作为一种自适应的时频能量表示分析方法，经验模态分解（EMD）具有非线性和非平稳数据存在时的鲁棒性。显然，数据的基线（或称为平均曲线）的适当定义在EMD方案中起着至关重要的作用。通过定义几个基线，本文提出了一种自适应数据驱动的分析方法，称为广义经验模式分解（GEMD）。在GEMD方法中，首先定义不同的基线，并分别从原始数据中减去它们，然后获得不同的预生成的固有模式函数（pre-GIMF）。 GIMF分量被定义为在获得的频率带宽到中心频率的比率最小的最优GIMF前。接下来，从原始数据中减去GIMF并获得残差，将其进一步视为原始数据以重复筛选过程，直到得出恒定或单调残差为止。由于从EMD和其他类似EMD的方法得出的不同的前GIMF中，每个频带中的GIMF最好，因此GEMD结果也最好。此外，介绍了一种称为经验包络解调（EED）的解调方法，并将其用于分析时频域中的GIMF。此外，通过分析模拟和滚动轴承振动信号，将GEMD和EED与原始的希尔伯特-黄变换（HHT）进行了对比。分析结果表明，所提出的由GEMD和EED组成的方法至少在抑制边界效应，获得更好的频率分辨率，更精确的分量和时频分布方面优于原始的HHT。

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