基于SSA-VMD的爆破振动信号趋势项去除方法

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摘要针对爆破振动信号存在的趋势项干扰问题，提出一种改进变分模态分解(Variational Mode Decomposition，VMD)的趋势项去除方法。该方法采用麻雀搜索算法（Sparrow Search Algorithm，SSA）优化VMD参数，接着对信号进行VMD分解，得到一组模态分量(Intrinsic Modal Function, IMF)。通过均值比法筛选出趋势项分量，然后对剩余分量重构得到去除趋势项的信号。经过仿真信号分析，SSA-VMD相较EEMD在均方根误差、相对范数和最大误差上分别降低了约73%、49%和82%，SSA-VMD对趋势项提取更为充分，识别趋势项的精度更高。同时，SSA-VMD对实测爆破振动信号进行分析，结果表明：该方法消除了爆破振动信号零点漂移现象，信号波形回归到基线中心，主频率趋于合理，提高了信号频谱分析的精度。

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	莫宏毅1
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	徐振洋1
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	刘鑫1
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	张久洋1
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	姜厦航1
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关键词 ：爆破振动, 麻雀算法（SSA）, 变分模态分解（VMD）, 趋势项

Abstract：Aiming at the interference of trend term in blasting vibration signal, an improved VMD trend term removal method is proposed. This method uses SSA to optimize VMD parameters, and then decompose the signal to a set of IMF. The trend component is screened out by the method of mean ratio, Then the residual component is reconstructed to get the signal to remove the trend term. After simulation signal analysis, compared with EEMD, SSA-VMD reduces the root mean square error, relative norm and maximum error by about 73%, 49% and 82%, respectively. SSA-VMD extracts trend items more fully and identifies trend items more accurately. At the same time, the measured blasting vibration signals are analyzed by SSA-VMD method. The results show that: the method eliminates the phenomenon of zero drift of blasting vibration signals, and the signal waveform returns to the center of baseline, main frequency tends to be reasonable, improving the accuracy of signal spectrum analysis.

Key words： blasting vibration SSA VMD trend term

收稿日期: 2022-05-31 出版日期: 2023-06-15

引用本文:

莫宏毅1,2，徐振洋1,2，刘鑫1,2，张久洋1,2，姜厦航1,2. 基于SSA-VMD的爆破振动信号趋势项去除方法[J]. 振动与冲击, 2023, 42(11): 304-312.
MO Hongyi1,2, XU Zhenyang1,2, LIU Xin1,2, ZHANG Jiuyang1,2, JIANG Xiahang1,2. Trend removal method of blasting vibration signal based on SSA-VMD. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(11): 304-312.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I11/304

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