深孔台阶爆破近区振动信号趋势项去除方法

韩亮; 刘殿书; 辛崇伟; 梁书锋; 凌天龙; 武宇; 李晨

doi:10.11883/bzycj-2016-0194

深孔台阶爆破近区振动信号趋势项去除方法

doi: 10.11883/bzycj-2016-0194

1.
华北科技学院安全工程学院, 北京 101601
2.
中国矿业大学(北京)力学与建筑工程学院, 北京 100083
3.
北京科技大学土木与环境工程学院, 北京 100083

基金项目:

中央高校基本科研业务费资助项目 3142017092

廊坊市科技支撑计划项目 2017013149

详细信息

作者简介:
韩亮(1985-), 男, 博士研究生, hanliang_1985@sina.com

中图分类号: O389
计量
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- PDF下载量: 114
- 被引次数: 0
出版历程
- 收稿日期: 2016-06-30
- 修回日期: 2016-09-20
- 刊出日期: 2018-09-25

Trend removing methods of vibration signals of deep hole bench blasting in near field

1.
Safety Engineering College, North China Institute of Science and Technology, Beijing 101601, China
2.
School of Mechanics and Civil Engineering, China University of Mining and Technology, Beijing 100083, China
3.
School of Civil and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China

摘要

摘要: 基于深孔台阶爆破近区大量实测振动信号，总结了趋势项产生的原因主要为大振幅脉冲输入下的非线性失真及低频干扰叠加，在此基础上以测试仪器有效监测范围作为识别趋势项组成部分的判别准则。利用集合经验模态分解（ensemble empirical mode decomposition，EEMD）、小波分解等信号分析手段，提出了以固有模态函数（intrinsic mode function，IMF）的频带分布为指标、人工判别的趋势项去除方法，以及基于自相关分析识别噪声特征的小波阈值去噪方法。实例证明该方法切实有效，可实现爆破信号的批量化预处理。
- 近区 /
- 爆破震动 /
- 趋势项 /
- 集合经验模态分解 /
- 自相关分析 /
- 小波 /
- 去噪
Abstract: Based on a large number of measured vibration signals of deep hole bench blasting in near field, this paper has contributed the trend mainly to the nonlinear distortion and the low frequency interference superposition with a large amplitude pulse input. On this basis, the effective monitoring range of test instruments has been chosen as criteria to identify the part of the trend. Using ensemble empirical mode decomposition (EEMD), the wavelet analysis, and other signal analysis methods, a trend elimination method is proposed here, which is based on the combination of the frequency band distribution of each intrinsic mode function component and artificial identification. In addition, a wavelet threshold denoising method is also proposed based on autocorrelation analysis to identify noise characteristics. Examples show that the methods are effective and can be realized by batch pretreatment of blasting signals.
- near field /
- blasting vibration /
- trend /
- EEMD /
- autocorrelation analysis /
- wavelet /
- denoising

HTML全文

图 1 趋势项去除流程

Figure 1. Removal process of trend

下载: 全尺寸图片幻灯片

图 2 EEMD方法分解结果

Figure 2. Decomposition results by EEMD method

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图 3 IMF分量频谱图

Figure 3. Frequency spectra of IMF component

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图 4 原始波形与重构波形

Figure 4. Original and reconstruction waveforms

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图 5 原始波形与重构波形频谱

Figure 5. Spectra of original and reconstruction waveforms

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图 6 IMF分量自相关函数图

Figure 6. Autocorrelation function graphs of IMF components

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图 7 小波阈值去噪前后波形对比

Figure 7. Contrast of wavelet thresholds before and after denoising

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表 1 信号测试条件

Table 1. Conditions of the test signal

测区	爆心距/m	最大段药量/kg
近区	65	2280

下载: 导出CSV

表 2 各IMF分量主频

Table 2. Dominant frequency of each IMF component

IMF分量	主频/Hz
IMF1	33.40
IMF2	14.40
IMF3	39.20
IMF4	32.40
IMF5	36.40
IMF6	19.40
IMF7	10.40
IMF8	7.60
IMF9	1.60
IMF10	1.00
IMF11	0.80
IMF12	0.60
IMF13	0.40
IMF14	0.20
r	0.00

下载: 导出CSV

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