[1]张佳①,赵岩②.基于CEEMDAN法的隧道爆破信号趋势项去除[J].爆破器材,2021,50(06):58-64.[doi:10.3969/j.issn.1001-8352.2021.06.010]
　ZHANG Jia,ZHAO Yan.Removal of Trend Items of Tunnel Blasting Signals Based on CEEMDAN[J].EXPLOSIVE MATERIALS,2021,50(06):58-64.[doi:10.3969/j.issn.1001-8352.2021.06.010]
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基于CEEMDAN法的隧道爆破信号趋势项去除()
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《爆破器材》[ISSN:1001-8352/CN:32-1163/TJ]

卷:
50
期数:
2021年06
页码:
58-64
栏目:
爆破技术
出版日期:
2021-11-26

文章信息/Info

Title:
Removal of Trend Items of Tunnel Blasting Signals Based on CEEMDAN
文章编号:
5598
作者:
张佳①; 赵岩②
①山西工程科技职业大学（山西太原，030031）
②中国矿业大学(北京)力学与建筑工程学院（北京，100083）
Author(s):
ZHANG Jia①;  ZHAO Yan②
① Shanxi Vocational University of Engineering Science (Shanxi Taiyuan, 030031)
② School of Mechanics and Civil Engineering, China University of Mining and Technology (Beijing) (Beijing, 100083)
关键词:
隧道爆破振动信号; 数值仿真;  CEEMDAN; 去趋势项
Keywords:
tunnel blasting vibration signal;  numerical simulation;  CEEMDAN;  removal of trend item
分类号:
O382
DOI:
10.3969/j.issn.1001-8352.2021.06.010
文献标志码:
A
摘要:
以京张高铁某隧道工程为背景，引入一种通过CEEMDAN（complete ensemble empirical mode decomposition with adaptive noise）法消除隧道爆破信号趋势项的方法。首先，利用CEEMDAN法分解实测爆破信号，得到一系列本征模态分量及余项。然后，通过均值比法识别筛选信号趋势项的有效组成部分，并去除含有趋势项的分量。为验证CEEMDAN法去趋势项的可行性，通过数值仿真信号进行校核。结果表明，与现有EMD(empirical mode decomposition)法、EEMD(ensemble empirical mode decomposition)法比较，基于CEEMDAN法筛选得到的趋势项与人为添加的趋势项最为接近。同时，利用此方法处理实测爆破信号，解决了原始信号中存在的基线偏移及低频超高异常等问题。
Abstract:
Based on a tunnel project of the Beijing-Zhangjiakou high-speed railway, a method of eliminating trend items of tunnel blasting signal through CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) was introduced. Firstly, CEEMDAN was used to decompose the measured blasting signal to obtain a series of eigenmode components and residual terms. Then, effective component of trend item of the initial signal was identified by the mean ratio method, and the component containing the trend item was removed. In order to verify the feasibility of removing trend item by CEEMDAN method, it was checked by numerical simulation signal. Results show that, compared with EMD (empirical mode decomposition) method and EEMD (ensemble empirical mode decomposition) method, the trend item obtained by CEEMDAN is closest to the artificially added trend item. At the same time, this method is used to process the measured blasting signals, which solves the problems of baseline offset and lowfrequency superhigh abnormality in original signals.

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备注/Memo

备注/Memo:
收稿日期：2021-04-08
基金项目：山西交通控股集团有限公司科技项目（19-JKKJ-46）；国家自然科学基金（51878242）
第一作者：张佳（1984-），男，硕士，讲师，主要研究方向为隧道与地下工程的施工技术、质量检测以及变形监测等。E-mail:24175702@qq.com
通信作者：赵岩（1991－），男，博士研究生，主要研究方向为爆破振动及损伤。E-mail:304965624@qq.com
更新日期/Last Update: 2021-11-22