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Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis

机译：基于集合局部均值分解和快速峰图的时频分析在旋转机械故障诊断中的应用

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摘要

A time-frequency analysis method based on ensemble local mean decomposition (ELMD) and fast kurtogram (FK) is proposed for rotating machinery fault diagnosis. Local mean decomposition (LMD), as an adaptive non-stationary and nonlinear signal processing method, provides the capability to decompose multicomponent modulation signal into a series of demodulated mono-components. However, the occurring mode mixing is a serious drawback. To alleviate this, ELMD based on noise-assisted method was developed. Still, the existing environmental noise in the raw signal remains in corresponding PF with the component of interest. FK has good performance in impulse detection while strong environmental noise exists. But it is susceptible to non-Gaussian noise. The proposed method combines the merits of ELMD and FK to detect the fault for rotating machinery. Primarily, by applying ELMD the raw signal is decomposed into a set of product functions (PFs). Then, the PF which mostly characterizes fault information is selected according to kurtosis index. Finally, the selected PF signal is further filtered by an optimal band-pass filter based on FK to extract impulse signal. Fault identification can be deduced by the appearance of fault characteristic frequencies in the squared envelope spectrum of the filtered signal. The advantages of ELMD over LMD and EEMD are illustrated in the simulation analyses. Furthermore, the efficiency of the proposed method in fault diagnosis for rotating machinery is demonstrated on gearbox case and rolling bearing case analyses.
机译：提出了一种基于整体局部平均分解（ELMD）和快速峰图（FK）的时频分析方法，用于旋转机械故障诊断。局部均值分解（LMD）作为一种自适应的非平稳和非线性信号处理方法，具有将多分量调制信号分解为一系列解调后的单分量的能力。但是，发生模式混合是一个严重的缺点。为了减轻这种情况，开发了基于噪声辅助方法的ELMD。尽管如此，原始信号中现有的环境噪声仍与感兴趣的分量保持在对应的PF中。 FK在脉冲检测中表现出色，同时存在强烈的环境噪声。但是它容易受到非高斯噪声的影响。所提出的方法结合了ELMD和FK的优点来检测旋转机械的故障。首先，通过施加ELMD，原始信号被分解为一组乘积函数（PF）。然后，根据峰度指标选择最能表征故障信息的PF。最后，选择的PF信号由基于FK的最佳带通滤波器进一步滤波，以提取脉冲信号。故障识别可以通过在滤波信号平方包络频谱中出现故障特征频率来推断。仿真分析显示了ELMD优于LMD和EEMD的优势。此外，通过齿轮箱和滚动轴承箱的分析，证明了该方法在旋转机械故障诊断中的有效性。

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