基于改进经验模态分解的激光测振信号数据处理方法

A data processing method for laser measured vibration signals based on improved EMD

  • 摘要: 针对由激光多普勒测振仪测量得到的固体火箭发动机振动信号包含高频噪声的问题,提出一种基于经验模态分解的数据处理方法。通过对传统经验模态分解方法存在的端点效应的抑制,以及对本征模态函数的判定准则的修改,得到一种适用于固体火箭发动机振动信号数据处理的改进经验模态分解方法;同时基于相关系数以及自相关函数的特点对本征模态函数进行筛选,以备后续数据处理。对比显示,基于改进经验模态分解的数据处理方法的运行速度比传统经验模态分解方法提高了5%,去噪效果比传统经验模态分解和小波分解算法分别提高了7%和17%。这说明该数据处理方法高效、稳定,具有较高的工程应用价值。

     

    Abstract: To address the issue that the vibration signals of solid rocket motor measured by laser Doppler vibrometer contains high frequency noise, a data processing method based on empirical mode decomposition (EMD) was proposed. By suppressing the endpoint effect of traditional EMD methods and modifying the criterion of the intrinsic mode function (IMF), an improved EMD method more suitable for the vibration signal data processing of solid rocket motors was developed. In addition, the IMFs were screened based on the characteristics of correlation coefficient and autocorrelation function for subsequent data processing. The comparison shows that data processing based on the improved EMD method is 5% faster than that of the traditional EMD method, and the denoising effect is 7% or 17% better compared with the traditional EMD or the wavelet decomposition algorithm, respectively. This indicates that the proposed data processing method is efficient, stable, and has good engineering application value.

     

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