郭航伸;郭连军;杨巍;潘博;徐振洋;
GUO Hang-shen;GUO Lian-jun;YANG Wei;PAN Bo;XU Zhen-yang;School of Mining Engineering, University of Science and Technology Liaoning;School of Architecture and Civil Engineering, Shenyang University of Technology;Chengyuan Mining Development Co., Ltd.;School of Civil and Resource Engineering, University of Science and Technology Beijing;

• 1:辽宁科技大学矿业工程学院
• 2:沈阳工业大学建筑与土木工程学院
• 3:成远矿业开发股份有限公司
• 4:北京科技大学土木与资源工程学院

摘要(Abstract)：

为了解决爆破振动信号的处理精度极易受到噪声信号的干扰的问题,在局部均值分解法(LMD)和总体平均局部均值分解法(ELMD)的基础上,提出了利用自适应互补集合局部均值分解方法(CELMDAN)分解原始信号,然后采用小波阈值法对获得的乘积函数(PF)分量进行去噪,提高了爆破振动信号去噪的精度。结果表明CELMDAN-WT方法有效地识别破振动信号中的噪声信号,实现了信号与噪声的分离,具有良好的自适应性,在爆破信号去噪中有良好的效果。
In order to solve the problem that the accuracy of blasting vibration signal processing is easily affected by noise signals, based on the local mean decomposition(LMD) and the ensemble local mean decomposition(ELMD), the complete ensemble local mean decomposition method with adaptive noise(CELMDAN) is proposed to decompose original signals and the wavelet threshold method is applied to denoise the product function(PF) component obtained, which enhances the accuracy of decomposition. The results show that the CELMDAN-WT method can effectively recognize and separate the noise signal from the blasting vibration signal, and has a good adaptability in blasting vibration signal denoising.

关键词(KeyWords)： 爆破振动;信号;去噪;局部均值分解
blasting vibration;signal;denoising;local mean decomposition

Abstract:

Keywords:

基金项目(Foundation): “十三五”国家重点研发计划资助项目(2016YFC0801603);; 辽宁科技大学人才资助项目(601011507-25)

作者(Authors): 郭航伸;郭连军;杨巍;潘博;徐振洋;
GUO Hang-shen;GUO Lian-jun;YANG Wei;PAN Bo;XU Zhen-yang;School of Mining Engineering, University of Science and Technology Liaoning;School of Architecture and Civil Engineering, Shenyang University of Technology;Chengyuan Mining Development Co., Ltd.;School of Civil and Resource Engineering, University of Science and Technology Beijing;

DOI: 10.19931/j.EB.20200216

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