| 隧道爆破信号主分量特征提取与毫秒延期识别研究 |
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| 引用本文： | 付晓强,刘纪峰,张会芝,张世平,雷振.隧道爆破信号主分量特征提取与毫秒延期识别研究[J].隧道建设,2019,39(Z1):117-124. |
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| 作者姓名： | 付晓强 刘纪峰 张会芝 张世平 雷振 |
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| 作者单位： | (1. 三明学院建筑工程学院， 福建 三明 365004; 2. 工程材料与结构加固福建省高等学校重点实验室, 福建 三明 365004; 3. 太原理工大学矿业工程学院, 山西 太原 030024； 4. 贵州理工学院, 贵州 贵阳 550003) |
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| 基金项目： | 国家自然科学基金面上资助项目（51664007）； 三明学院引进高层次人才科研启动经费资助项目（18YG13） |
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| 摘 要： | 为了准确揭示隧道爆破振动信号所包含的反映爆破特征参数的重要信息，通过EMD分解与交叉小波变换组合分析方法对实测爆破信号进行分析，得到各分量与原信号的相关性系数，从而准确判别出信号的主分量。再依据相关性系数对包含主分量在内的相关分量重组并进行Hilbert变换取模值，精确识别出隧道采用的各段别雷管的毫秒延期时间。结果表明： 组合方法确定的主分量及相关分量重组信号能够突出爆破信号的主要特征，对隧道爆破信号的分析效果好。
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| 关 键 词： | 隧道爆破 爆破振动 交叉小波变换 主分量分析 毫秒延期识别 |
| 收稿时间： | 2019-03-25 |
Principal Component Feature Extraction and Millisecond Delay Recognition of Tunnel Blasting Signal |
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| FU Xiaoqiang,LIU Jifeng,ZHANG Huizhi,ZHANG Shiping,LEI Zhen.Principal Component Feature Extraction and Millisecond Delay Recognition of Tunnel Blasting Signal[J].Tunnel Construction,2019,39(Z1):117-124. |
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| Authors: | FU Xiaoqiang LIU Jifeng ZHANG Huizhi ZHANG Shiping LEI Zhen |
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| Institution: | (1.School of Civil Engineering, Sanming University, Sanming 365004, Fujian, China; 2.Key Laboratory of Engineering Material & Structure Reinforcement in Fujian Province College (Sanming University), Sanming 365004, Fujian, China; 3. College of Mining Technology, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China; 4. Guizhou Institute of Technology, Guiyang 550003, Guizhou, China) |
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| Abstract: | In order to reveal the important information of blasting characteristic parameters contained in tunnel blasting vibration signals accurately, the measured blasting signal is analyzed by EMD decomposition and cross wavelet transform, and the correlation coefficients between each component and the original signal are obtained, so as to accurately identify the principal component of the signal. And then, the correlation components including the principal component is reconstructed and the Hilbert transform is used to obtain the modulus value according to the correlation coefficient, and the millisecond delay time of each detonator used in the tunnel is accurately identified. The results show that the reconstructed signals of principal component and related ones determined by the combination method can highlight the main characteristics of blasting signals, and the analysis effect of the tunnel blasting signal is good. |
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| Keywords: | tunnel blasting blasting vibration cross wavelet transform principal component analysis millisecond delay recognition |
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