Feature extraction and classification method of coal gangue acoustic signal during top coal caving
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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 %。Abstract: To achieve the intelligent recognition of coal gangue in fully mechanized caving face, a coal caving acoustic signal collection device is designed, which can sense the movement of the tail beam and automatically trigger data collection.Field data is collected at the fully mechanized caving face 3106 of Gucheng Coal Mine, Shandong Energy, and it is manually labeled to construct a sample library of acoustic signal classification for top coal caving.Then, six machine learning classification methods are applied in the time domain, frequency domain and time-frequency domain, and the classification effect of them are evaluated by different frame lengths and different feature vector dimensions.The results show that: the classification effect based on time-frequency domain features is the most stable, and its accuracy rate is the highest by different frame lengths.The classification accuracy rate of random forest, K-nearest neighbor, decision tree and multi-layer perceptron model is above 80 %.Among them, the classifier performance based on wavelet packet decomposition and random forests are the best, and the classification accuracy is 93.06 %.There is a correlation between the time-frequency domain feature vectors and higher dimensions.Through dimensionality reduction, a small number of comprehensive features can be extracted and the amount of system calculations can be reduced.The principal component analysis is used to reduce the time-frequency domain feature vector to 20.Thus, the classification accuracy rate is further improved to 94.51 %.
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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 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 $IF = \frac{{|\max [x(n)]|}}{{\frac{1}{N}\sum\limits_{n = 1}^N | x(n)|}}$ 
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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 ${X_{{\rm{VF}}}} = \frac{{\sum\limits_{k = 1}^N {\left({k - {X_{{\rm{FC}}}}} \right)} X(k)}}{{\sum\limits_{k = 1}^N X (k)}}$
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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
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