变分模态分解算法在煤矿井筒爆破信号趋势项消除中的应用
Application of Variational Mode Decomposition Algorithm in Elimination of Trend Term of Coal Mine Shaft Blasting Signal
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摘要: 爆破信号测试过程中,受测试环境和仪器自身原因的影响,在爆破近区监测到的信号往往含有趋势项干扰,无法实现信号特征的精细化提取。对现场爆破信号进行有效采集,采用变分模态分解(Variational mode decomposition,VMD)对其进行了趋势项消除并进行了时频特征提取。结果表明:井筒爆破振动信号中的趋势项具有高幅、低频特征,在时间轴上的分布更为广泛,信号中的真实成分具有宽频、低幅特征,在时间轴上聚集性更强,两者具有显著的区分度,变分模态分解对于爆破信号的自适应性强,可有效避免模态混叠效应的产生。Abstract: In the test process of blasting signal, influenced by the test environment and the instrument, the signals detected near blasting area often contain the trend item interference, which cannot achieve the fine extraction of signal characteristics. The blasting signals are effectively collected, and the trend items are eliminated and the time-frequency characteristics are extracted using the variational mode decomposition(VMD). The results show that: the trend term of blasting vibration signal has the characteristics of high amplitude and low frequency; it’s more broadly distributed in time. The real components in the signal have the characteristics of wide frequency and low amplitude, and the aggregation is stronger on the time axis, there is a significant degree of differentiation between them. The variational mode decomposition is highly adaptive to the blasting signal and can effectively avoid the generation of the mode aliasing effect.