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

付晓强; 崔秀琴; 杨立云; 雷　振; 蔡雪霁; 李　阳

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

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出版历程
- 发布日期: 2020-10-19

Application of Variational Mode Decomposition Algorithm in Elimination of Trend Term of Coal Mine Shaft Blasting Signal

摘要

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。
- 竖井爆破 /
- 爆破振动 /
- 变分模态分解 /
- 时频分析 /
- 特征提取
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.
- shaft blasting /
- blasting vibration /
- variational mode decomposition /
- time frequency analysis /
- feature extraction

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参考文献(11)

[1]	付晓强,雷振,崔秀琴,等.立井爆破振动信号混沌特征研究[J].煤矿安全,2019,50(11):63-66.
[2]	杨仁树,付晓强,杨立云,等.冻结立井爆破冻结壁成形控制与井壁减振研究[J].煤炭学报,2016,41(12):2975-2985.
[3]	龙源,谢全民,钟明寿,等.爆破震动测试信号预处理分析中趋势项去除方法研究[J].工程力学,2012,29(10):63-68.
[4]	张胜,凌同华,曹峰,等.模式自适应连续小波去除趋势项方法在爆破振动信号分析中的应用[J].爆炸与冲击,2017,37(2):255-261.
[5]	付晓强,张仁巍,雷振,等.煤矿立井爆破信号趋势项和噪声消除方法[J].兰州工业学院学报,2020,27(1):57-62.
[6]	韩亮,刘殿书,辛崇伟,等.深孔台阶爆破近区振动信号趋势项去除方法[J].爆炸与冲击,2018,38(5):1006-1012.
[7]	秦喜文,郭佳静,史磊,等.基于变分模态分解与随机森林的滚动轴承故障诊断[J].制造业自动化,2020, 42(1):1-6.
[8]	胡伟鹏,邹孝,刘备,等.基于多迭代变分模态分解与复合多尺度散布熵的生物组织变性识别方法[J].传感技术学报,2019,32(12):1856-1863.
[9]	沈健,赵文涛,丁建明.基于变分模态分解和奇异值分解的结构模态参数识别方法[J].交通运输工程学报,2019,19(6):77-90.
[10]	许子非,岳敏楠,李春.优化递归变分模态分解及其在非线性信号处理中的应用[J].物理学报,2019,68(23):292-305.
[11]	付晓强,杨立云,陈程,等.煤矿冻结立井爆破雷管微差延时识别研究[J].煤矿安全,2017,48(4):55-58