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首页> 外文会议>International Conference on Acoustics and Vibration >Early Detection of Gear Faults in Variable Load and Local Defect Size Using Ensemble Empirical Mode Decomposition (EEMD)
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Early Detection of Gear Faults in Variable Load and Local Defect Size Using Ensemble Empirical Mode Decomposition (EEMD)

机译：使用集合经验模式分解（EEMD）的可变负荷和局部缺陷尺寸的齿轮故障的早期检测

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摘要

In stationary condition, when a local gear fault occurs, both amplitude and phase of the tooth meshing vibration are modulated. If the rotating speed of the shaft is invariable, the gear-fault-induced modulation phenomenon manifest as frequency sidebands equally spaced around the meshing frequency and its harmonics in vibration spectra. However, under variable load and rotating speed of the shaft, the meshing frequency and its harmonics and the sidebands vary with time and hence the vibration signal becomes non-stationary. Using Fourier transform doesn't allow detecting the variation of the rotating machine and its harmonics which reflect the gear fault. In this study, we propose to use the ensemble empirical decomposition (EEMD) to decompose signals generated by the variation of load and the size of the defect. This method is particularly suitable for processing non stationary signals. By using EEMD the signal can be decomposed into a number of IMFs which are mono component, we use also the spectrum and spectrogram of each IMF to show and calculate the frequency defect.
机译：在静止状态下，当发生局部齿轮故障时，调制齿啮合振动的两个幅度和相位。如果轴的转速是不变的，齿轮故障感应调制现象表现为频率边带同样周围的啮合频率和其在振动谱谐波间隔开。然而，在轴的可变负载和旋转速度下，啮合频率及其谐波和边带随时间而变化，因此振动信号变得不静止。使用傅立叶变换不允许检测旋转机器的变化及其反映齿轮故障的谐波。在这项研究中，我们建议使用集合经验分解（EEMD）来分解由负载变化和缺陷的尺寸产生的信号。该方法特别适用于处理非静止信号。通过使用EEMD，信号可以分解成多个IMF，这些IMF是单组分，我们也使用每个IMF的频谱和频谱图来显示和计算频率缺陷。

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