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<p>The pressure signal can indicate the characteristics of the turbulence pulse in oil. The analysis of the oil’s pulsating flow pressure signal was conducted using the Hilbert-Huang transform (HHT) method. Through the empirical mode decomposition (EMD) technique, the energy features across various frequency bands, as well as the features of amplitude and frequency modulation were examined. The results demonstrate that as the number of decomposed intrinsic mode function (IMF) components increases, the amplitude values of each IMF component and their instantaneous frequencies gradually decrease, leading to a smoother trend over time. The corresponding mean values of the instantaneous frequencies also decline, and the energy features diminish. The majority of the pressure signal’s energy is concentrated in the high-frequency IMF1-3 components, indicating a more pronounced modulation in this region. Additionally, after applying the envelope demodulation via the Hilbert transform, the carrier signal clearly exhibits frequency modulation characteristics. The extracted features provide a quantitative basis for characterizing the state of oil pulsating flow. This method offers a potential tool for improving the monitoring of oil transport systems and early fault detection by identifying changes in the energy features and modulation patterns of pressure signals.</p>

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HHT-based feature extraction of pressure signals in oil pulsating flow

  • 期刊论文

  • 发表日期：

    2026年5月13日

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  • Chen Bin,
  • Liu Ge

摘要

The pressure signal can indicate the characteristics of the turbulence pulse in oil. The analysis of the oil’s pulsating flow pressure signal was conducted using the Hilbert-Huang transform (HHT) method. Through the empirical mode decomposition (EMD) technique, the energy features across various frequency bands, as well as the features of amplitude and frequency modulation were examined. The results demonstrate that as the number of decomposed intrinsic mode function (IMF) components increases, the amplitude values of each IMF component and their instantaneous frequencies gradually decrease, leading to a smoother trend over time. The corresponding mean values of the instantaneous frequencies also decline, and the energy features diminish. The majority of the pressure signal’s energy is concentrated in the high-frequency IMF1-3 components, indicating a more pronounced modulation in this region. Additionally, after applying the envelope demodulation via the Hilbert transform, the carrier signal clearly exhibits frequency modulation characteristics. The extracted features provide a quantitative basis for characterizing the state of oil pulsating flow. This method offers a potential tool for improving the monitoring of oil transport systems and early fault detection by identifying changes in the energy features and modulation patterns of pressure signals.