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基于经验模态分解(EmpiricalModeDecomposition,EMD)的希尔伯特变换(HilbertTransformation,HT),是先把一列时间序列数据通过经验模态分解,然后经过希尔伯特变换获得频谱的信号处理新方法。介绍了该方法的理论和算法。对仿真和旋转机械油膜涡动故障振动信号分别用基于EMD的HT和基于STFT(Short-TimeFourierTransforma tion,STFT)的时频分析进行了比较研究,研究结果说明,用基于EMD的HT方法对旋转机械的振动信号进行时频分析比STFT有效。
Based on Empirical Mode Decomposition (EMD), Hilbert Transform (HT) is a new signal processing method that first decomposes a series of time-series data through empirical mode decomposition and obtains Hilbert transform. The theory and algorithm of this method are introduced. The vibration signals of simulated and whirling fault of rotating machine oil film were compared respectively with time-frequency analysis based on STT (Short-Time Fourier Transform) and HT-EMT. The results show that using the EMD-based HT method, The vibration signal is time-frequency analysis is more effective than STFT.