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顶煤放落过程煤矸声信号特征提取与分类方法

袁源 汪嘉文 朱德昇 王家臣 王统海 杨克虎

袁源, 汪嘉文, 朱德昇, 王家臣, 王统海, 杨克虎. 顶煤放落过程煤矸声信号特征提取与分类方法[J]. 矿业科学学报, 2021, 6(6): 711-720. doi: 10.19606/j.cnki.jmst.2021.06.010
引用本文: 袁源, 汪嘉文, 朱德昇, 王家臣, 王统海, 杨克虎. 顶煤放落过程煤矸声信号特征提取与分类方法[J]. 矿业科学学报, 2021, 6(6): 711-720. doi: 10.19606/j.cnki.jmst.2021.06.010
Yuan Yuan, Wang Jiawen, Zhu Desheng, Wang Jiachen, Wang Tonghai, Yang Kehu. Feature extraction and classification method of coal gangue acoustic signal during top coal caving[J]. Journal of Mining Science and Technology, 2021, 6(6): 711-720. doi: 10.19606/j.cnki.jmst.2021.06.010
Citation: Yuan Yuan, Wang Jiawen, Zhu Desheng, Wang Jiachen, Wang Tonghai, Yang Kehu. Feature extraction and classification method of coal gangue acoustic signal during top coal caving[J]. Journal of Mining Science and Technology, 2021, 6(6): 711-720. doi: 10.19606/j.cnki.jmst.2021.06.010

顶煤放落过程煤矸声信号特征提取与分类方法

doi: 10.19606/j.cnki.jmst.2021.06.010
基金项目: 

国家自然科学基金 61936008

国家自然科学基金 61973307

详细信息
    作者简介:

    袁源(1997—)男,陕西榆林人,硕士研究生,主要从事煤矿智能开采技术等方面的研究工作。Tel:15600686609,E-mail:accioyy@163.com

    通讯作者:

    杨克虎(1982—)男,湖北仙桃人,教授,博士生导师,主要从事煤矿智能开采、电力电子变换器建模与控制等方面的研究工作。Tel:010-62339695,E-mail:ykh@cumtb.edu.cn

  • 中图分类号: TP399

Feature extraction and classification method of coal gangue acoustic signal during top coal caving

  • 摘要: 针对综放工作面煤矸智能识别问题,设计了能感应尾梁动作并自动触发数据采集的放煤声信号采集装置,在山东能源古城煤矿3106综放工作面采集现场数据并进行人工标注,构建了放顶煤声信号分类样本库。研究了6种常用机器学习分类方法在时域、频域和时频域中的特征,以及在不同帧长、不同特征向量维度下的分类效果。结果表明:在不同帧长下,基于时频域特征的分类效果最稳定、准确率最高,随机森林、K近邻、决策树、多层感知器模型分类准确率均达到80 % 以上,其中基于小波包分解与随机森林算法的分类器性能最好,分类准确率为93.06 %。维度较高的时频域特征向量之间存在相关性,降维可以提取少量的综合特征并降低系统的运算量,利用主成分分析法将时频域特征向量降维至20后,分类准确率进一步提高至94.51 %。
  • 图  1  数据采集装置电路结构

    Figure  1.  Circuit structure diagram of data acquisition device

    图  2  放顶煤液压支架尾梁声振数据采集装置

    Figure  2.  Acoustic and vibration data acquisition device for tail beam of caving coal hydraulic support

    图  3  不同噪声影响下的放煤声信号

    Figure  3.  Coal sound signal under the influence of different noises

    图  4  不同噪声影响下的放矸声信号

    Figure  4.  Gangue sound signal under the influence of different noises

    图  5  放煤声信号小波包分解频带能量占比

    Figure  5.  Frequency band energy ratio of wavelet packet decomposition of coal caving acoustic signal

    图  6  放矸声信号小波包分解频带能量占比

    Figure  6.  Frequency band energy ratio of wavelet packet decomposition of gangue acoustic signal

    图  7  时域特征提取下不同帧长的煤矸识别准确率

    Figure  7.  Accuracy of coal gangue recognition in different frame lengths under time-domain feature extraction

    图  8  频域特征提取下不同帧长的煤矸识别准确率

    Figure  8.  Accuracy of coal gangue recognition in different frame lengths under frequency-domain feature extraction

    图  9  时频域特征提取下不同帧长的煤矸识别准确率

    Figure  9.  Accuracy of coal gangue recognition in different frame lengths under time-domain feature extraction

    图  10  不同特征的煤矸识别准确率

    Figure  10.  Recognition accuracy of coal and gangue with different characteristics

    图  11  不同维度下多种分类模型的煤矸识别准确率

    Figure  11.  Accuracy of coal and gangue recognition by various classification models in different dimensions

    表  1  放顶煤声信号的时域特征及其计算公式

    Table  1.   Time-domain characteristics of caving coal acoustic signal and its calculation formula

    特征类型 计算公式
    有效值 ${X_{{\rm{rms}}}} = \sqrt {\frac{1}{N}\sum\limits_{n = 1}^N x {{(n)}^2}} $
    标准差 $X_{\mathrm{std}}=\sqrt{\frac{1}{N} \sum\limits_{n=1}^{N}[x(n)-\bar{X}]^{2}}$
    偏度 $X_{\text {ske }}=\frac{1}{N} \sum\limits_{n=1}^{N}[x(n)-\bar{X}]^{3}$
    峭度 $X_{\mathrm{kur}}=\frac{1}{N} \sum\limits_{n=1}^{N}[x(n)-\bar{X}]^{4}$
    峰峰值 $X_{p-p}=\max [x(n)]-\min [x(n)] $
    波形因数 $C F=\frac{|\max [x(n)]|}{X_{\mathrm{rms}}}$
    脉冲因数 $SF = \frac{{{X_{{\rm{rms}}}}}}{{\frac{1}{N}\sum\limits_{n = 1}^N | x(n)|}}$
    峰值因数 $IF = \frac{{|\max [x(n)]|}}{{\frac{1}{N}\sum\limits_{n = 1}^N | x(n)|}}$
    下载: 导出CSV

    表  2  放顶煤声信号的频域特征及其计算公式

    Table  2.   Frequency-domain characteristics of caving coal acoustic signal and its calculation formula

    特征类型 计算公式
    重心频率 ${{X_{{\rm{FC}}}} = \frac{{\sum\limits_{k = 1}^N k X(k)}}{{\sum\limits_{k = 1}^N X (k)}}}$
    均方频率 ${{X_{{\rm{MSF}}}} = \frac{{\sum\limits_{k = 1}^N {{k^2}} X(k)}}{{\sum\limits_{k = 1}^N X (k)}}}$
    频率方差 ${X_{{\rm{VF}}}} = \frac{{\sum\limits_{k = 1}^N {\left({k - {X_{{\rm{FC}}}}} \right)} X(k)}}{{\sum\limits_{k = 1}^N X (k)}}$
    下载: 导出CSV

    表  3  多种分类模型的参数

    Table  3.   Parameters of multiple classification models

    分类模型 SVM RF KNN NBC DT MLP
    识别时间/ms 3.150 0 2.922 9 2.890 8 2.980 1 2.650 9 3.049 7
    模型大小/MB 2.945 0 6.602 0 6.975 0 0.003 0 0.053 0 0.008 0
    下载: 导出CSV
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  • 收稿日期:  2021-01-07
  • 修回日期:  2021-05-20
  • 刊出日期:  2021-12-01

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