摘要
在爆破振动监测过程中,为解决低频趋势成分干扰所引起的基线漂移问题,提出了一种基于鲁棒局部均值分解(robust local mean decomposition, RLMD)和均值判比(mean ratio, MR)方法的爆破振动信号基线校正方法。首先,利用RLMD对包含趋势项的振动信号进行自适应分解,生成一系列乘积函数(product functions, PF);随后,通过MR方法筛选出低频趋势项分量,去除这些成分并重构剩余信号,以校正基线漂移。仿真信号分析结果表明,与传统的最小二乘拟合法(ordinary least squares, OLS)和局部均值分解(local mean decomposition, LMD)相比,RLMD方法在提取趋势项方面具有更高的准确性和稳定性,有效避免了模态混叠现象。现场爆破振动监测试验结果显示,与远区振动信号相比,近区实测爆破振动信号受到低频趋势项的干扰更为严重。通过RLMD-MR方法进行基线校正后,信号波形能够有效恢复至基线中心附近,解决了基线漂移问题。
In the process of blasting vibration monitoring,a baseline correction method for blasting vibration signals based on robust local mean decomposition(RLMD)and mean ratio(MR)methods was proposed to solve the problem of baseline drift caused by low-frequency trend component interference.Firstly,RLMD was used to adaptively decompose the vibration signals containing trend terms,generating a series of product functions(PF).Subsequently,the MR method was employed to filter out low-frequency trend components,remove these components,and reconstruct the remaining signal to correct the baseline drift.The results of the simulation signal analysis indicate that RLMD method demonstrates higher accuracy and stability in trend component extraction compared to traditional methods such as ordinary least squares(OLS)and local mean decomposition(LMD),effectively avoiding mode mixing.In field blast vibration monitoring tests,it reveals that near-field measured blast vibration signals are more severely affected by low-frequency trend components than far-field signals.After applying RLMD-MR method for baseline correction,the signal waveform effectively returns near the baseline center,solving the problem of baseline drift.
作者
刘志龙
张祚富
盖俊鹏
张文辉
孙斌
徐振洋
LIU Zhilong;ZHANG Zuofu;GAI Junpeng;ZHANG Wenhui;SUN Bin;XU Zhenyang(Guanbaoshan Mining Co.,Ltd.,Ansteel Group,Liaoning Anshan,114044;School of Mining Engineering,University of Science and Technology Liaoning,Liaoning Anshan,114051)
出处
《爆破器材》
北大核心
2025年第3期48-56,64,共10页
Explosive Materials
关键词
爆破振动
信号处理
鲁棒局部分解
去趋势
均值判比法
blasting vibration
signal processing
robust local mean decomposition
detrend
mean ratio method
