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Fault Feature Extraction of Hydraulic Pumps Based on Symplectic Geometry Mode Decomposition and Power Spectral Entropy

机译:基于辛几何模式分解和功率谱熵的液压泵故障特征提取

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摘要

Aiming at fault feature extraction of a hydraulic pump signal, a new method based on symplectic geometry mode decomposition (SGMD) and power spectral entropy (PSE) is proposed. First, the SGMD is applied to decompose a multi-component fault signal, then the N symplectic geometry components (SGCs) can be obtained. Second, the N SGCs are reconstructed as a signal of interest and, consequently, the power spectral entropy of each constructed signal is computed to quantify the complexity and uncertainty of their spectra. Finally, the difference value (D-value) between the adjacent entropies is used as a SGCs criterion, whose turning point indicates the most information of reconstructed signal. Hydraulic pump signals are tested and verified, and results demonstrate that the proposed method can extract the richest fault feature information of hydraulic pump signals effectively.
机译:旨在施加液压泵信号的故障特征提取,提出了一种基于辛几何模式分解(SGMD)和功率谱熵(PSE)的新方法。首先,应用SGMD来分解多分量故障信号,然后可以获得N个杂片几何分量(SGC)。其次,N SGC被重建为感兴趣的信号,因此,计算每个构造信号的功率谱熵以量化它们光谱的复杂性和不确定性。最后,相邻熵之间的差值(D值)用作SGCS标准,其转弯点表示重建信号的最多信息。测试和验证液压泵信号,结果表明该方法可以有效地提取液压泵信号的最富有的故障特征信息。

著录项

  • 作者

    Zhi Zheng; Ge Xin;

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  • 年度 2019
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  • 原文格式 PDF
  • 正文语种 eng
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