[1]谢学斌,王小平,刘涛.基于ICEEMDAN和MC-CNN的矿山声发射信号识别分类方法*[J].中国安全生产科学技术,2022,18(2):113-118.[doi:10.11731/j.issn.1673-193x.2022.02.017]
XIE Xuebin,WANG Xiaoping,LIU Tao.Recognition and classification methods of mine acoustic emission signals based on ICEEMDAN and MC-CNN[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(2):113-118.[doi:10.11731/j.issn.1673-193x.2022.02.017]
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基于ICEEMDAN和MC-CNN的矿山声发射信号识别分类方法*
XIE Xuebin,WANG Xiaoping,LIU Tao.Recognition and classification methods of mine acoustic emission signals based on ICEEMDAN and MC-CNN[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(2):113-118.[doi:10.11731/j.issn.1673-193x.2022.02.017]
《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]
- 卷:
- 18
- 期数:
- 2022年2期
- 页码:
- 113-118
- 栏目:
- 职业安全卫生管理与技术
- 出版日期:
- 2022-02-28
文章信息/Info
- Title:
- Recognition and classification methods of mine acoustic emission signals based on ICEEMDAN and MC-CNN
- 文章编号:
- 1673-193X(2022)-02-0113-06
- 关键词:
- 声发射事件; 模式识别; 改进的完全集合经验模态分解; 多通道卷积神经网络
- Keywords:
- acoustic emission (AE) event; pattern recognition; improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN); multi-channel convolutional neural network (MC-CNN)
- 分类号:
- X936
- 文献标志码:
- A
- 摘要:
- 为精准识别地下矿山声发射事件，采用基于改进的完全集合经验模态分解模型(ＩＣＥＥＭＤＡＮ)和多通道卷积神经网络（ＭＣ－ＣＮＮ）模型对声发射信号进行处理后得到分量图，根据各通道输入分量峭度值赋予不同权重，并利用卷积神经网络对输入数据进行训练，最终采用五折交叉实验方法验证该分类识别方法的可行性及有效性。结果表明：基于ICEEMDAN和MC-CNN模型分类识别正确率为97.64%，与其他传统识别方法相比能精准有效地对地下矿山声发射信号进行识别分类，显著提高卷积神经网络的波形识别正确率。研究结果可为地下矿山声发射事件识别分类提供借鉴。
- Abstract:
- In order to accurately identify the acoustic emission events in underground mines,the acoustic emission signals are processed based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and multi-channel convolutional neural network (MC-CNN) model,and then intrinsic mode function are obtained.Different weights are given according to the kurtosis values of the input components in each channel,and the input data are trained by the convolutional neural network.Finally,the feasibility and effectiveness of the classification and identification method are verified by the Five-fold cross experiment method.The results show that the classification recognition accuracy based on ICEEMDAN and MC-CNN model is 97.64%.Compared with other traditional recognition methods,it can accurately and effectively classify the acoustic emission signals of underground mines,and significantly improve the waveform recognition.
参考文献/References:
［1］李庶林,尹贤刚,王泳嘉，等.单轴受压岩石破坏全过程声发射特征研究［J］.岩石力学与工程学报,2004(15):2499-2503.LI Shulin,YIN Xiangang,WANG Yongjia,et al.Studies on acoustic emission characteristics of uniaxial compeossive rock failure［J］.Chinese Journal of Rock Mechanics and Engineering,2004(15):2499-2503.
［2］赵兴东,李元辉,袁瑞甫，等.基于声发射定位的岩石裂纹动态演化过程研究［J］.岩石力学与工程学报,2007(5):944-950.ZHAO Xingdong,LI Yuanhui,YUAN Ruifu,et al.Study on crack dynamic propagation process of rock samples based on acoustic emission location［J］.Chinese Journal of Rock Mechanics and Engineering,2007(5):944-950.
［3］刘建坡,李元辉,张凤鹏，等.基于声发射监测的深部采场岩体稳定性分析［J］.采矿与安全工程学报,2013,30(2):243-250.LIU Jianpo,LI Yuanhui,ZHANG Fengpeng,et al.Stability analysis of rockmass based on acoustic emission monitoring in deep stope［J］.Journal of Mining & Safety Engineering,2013,30(2):243-250.
［4］晏建洋,吴建星.基于小波变换的微震信号去噪研究［J］.科技通报,2016,32(3):185-188.YAN Jianyang,WU Jianxing.Research on Micro-seismicsignal de-noising based on wavelet transform［J］.Bulletin of Science and Technology,2016,32(3):185-188.
［5］ALVANITOPOULOS P F,PAPAVASILEION M,ANDREADIS I,et al.Seismic intensity feature construction based on the Hilbert-Huang transform［J］.Instrumentation & Measurement,2012,61(2):326-337.
［6］GACI S.The use of wavelet-based denoising techniques to enhance the first-arrival picking on seismic traces［J］.Geoscience & Remote Sensing,2014,52(8):4558-4563.
［7］李晓萍,郑勇.信号的时频分析评述［J］.西安石油学院学报(自然科学版),1996(3):49-51,57.LI Xiaoping,ZHENG Yong.The research on time-frequency analysis of signals［J］.Journal of Xi’an Shiyou Institute (Natural Science Edition),1996(3):49-51,57.
［8］张义平,李夕兵.Hilbert-Huang变换在爆破震动信号分析中的应用［J］.中南大学学报(自然科学版),2005(5):168-173.ZHANG Yiping,LI Xibing.Application of Hilbert Huang transformin blasting vibration signal analysis［J］.Journal of Central South University(Science and Technology),2005(5):168-173.
［9］HUANG N E,ZHENG S,LONG S,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis ［J］.Proceedings Mathematical Physical & Engineering Sciences,1998,454(1971):903-995.
［10］WU Z,HUANG N E.Ensemble empirical mode decomposition:a noise-assisted data analysis method ［J］.Advances in Adaptive Data Analysis,2009,1(1):1-41.
［11］YEH J R,SHIEH J S,HUANG N E.Complementary ensemble empirical mode decomposition:a novel noise enhanced data analysis method ［J］.Advances in Adaptive Data Analsis,2010,2(2):135-156.
［12］TORRES M E,COLOMINAS M A,SCHLOTTHAUER G,et al.A complete ensemble empirical mode decomposition with adaptive noise［C］// Proceedings of the IEEE International Conference on Acoustic,Speech,and Signal Processing,ICASSP 2011.Prague,Czech Republic:IEEE,2011:4144-4147.
［13］尚雪义,李夕兵,彭康,等.基于EMD-SVD的矿山微震与爆破信号特征提取及分类方法［J］.岩土工程学报,2016,38(10):1849-1858.SHANG Xueyi,LI Xibing,PENG Kang,et al.Feature extraction and classification of mine microseism and blast based on EMD-SVD［J］.Chinese Journal of Rock Mechanics and Engineering,2016,38(10):1849-1858.
［14］程铁栋,吴义文,罗小燕,等.基于EWT-Hankel-SVD的矿山微震信号特征提取及分类方法［J］.仪器仪表学报,2019,40(6):181-191.CHENG Tiedong,WU Yiwen,LUO Xiaoyan,et al.Feature extraction and classification method of mine microseismic signals based on EWT-Hankel-SVD［J］.Chinese Journal of Scientific Instrument,2019,40(6):181-191.
［15］JIA F,LEI Y,LIN J,et al.Deep neural networks:a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data［J］.Mechanical Systems and Signal Processing,2016,72:303-315.
［16］叶壮,余建波.基于多通道加权卷积神经网络的齿轮箱振动信号特征提取［J］.机械工程学报,2021,57(1):110-120.YE Zhuang,YU Jianbo.Feature extraction of gearbox vibration signals based on multi-channels weighted convolutional neural network［J］.Journal of Mechanical Engineering,2021,57(1):110-120.
［17］DONG L J,TANG Z,LI X B,et al.Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform ［J］.Journal of Central South University,2020,27(10):3078-3089.
［18］胡茑庆,陈徽鹏,程哲,等.基于经验模态分解和深度卷积神经网络的行星齿轮箱故障诊断方法［J］.机械工程学报,2019,55(7):9-18.HU Niaoqing,CHEN Huipeng,CHENG Zhe,et al.Fault diagnosis for planetary gearbox based on EMD and deep convolutional neural networks［J］.Journal of Mechanical Engineering,2019,55(7):9-18.
［19］MARCELO A C,GASTON S,MARIA E T.Improved complete ensemble EMD:a suitable tool for biomedical signal processing［J］.Biomedical Signal Processing and Control,2014,14:19-29.
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备注/Memo
- 备注/Memo:
-
收稿日期： 2021-06-13；网络首发日期： 2021-11-26
* 基金项目： 国家自然科学基金项目（52174140）；广西重点研发计划项目（AB18294004）
作者简介： 谢学斌，博士，教授，主要研究方向为城市地下空间、矿山安全技术。
通信作者： 王小平，硕士研究生，主要研究方向为矿山安全技术。
