Coal mine gas and coal dust explosion sound recognition method based on wavelet packet energy
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摘要: 针对目前煤矿瓦斯和煤尘爆炸监测漏报率和误报率高，难以满足瓦斯和煤尘爆炸事故应急救援需求的问题，提出了一种基于小波包能量的煤矿瓦斯和煤尘爆炸声音识别方法。在煤矿井下重点监测区域安装矿用拾音器，实时采集煤矿井下设备工作声音及环境音等；通过小波包分解提取声音的小波包能量占比，构成表征声音信号的特征向量；将特征向量输入BP神经网络中，训练得到煤矿瓦斯和煤尘爆炸声音识别模型；提取待测声音信号的小波包能量占比，并构成特征向量输入模型中,识别待测声音信号的类型。根据特征向量和输出结果要求，建立了输入层、隐含层和输出层节点数分别为8，8，1的BP神经网络用于识别模型的训练；通过分析煤矿井下声音信号小波包分解结果，确立了采用Haar小波函数，选择小波包分解层数为3。实验结果表明：瓦斯和煤尘爆炸声音通过小波包分解后的能量占比与其他声音差异明显，且不同时长的同一声音信号的小波包能量占比分布稳定，因此小波包能量占比可有效表征声音信号特征，且具有较强的鲁棒性；BP神经网络训练速度快，仅需较少的训练迭代次数就能达到期望误差，且在煤矿井下众多干扰声音信号存在的情况下识别准确率达95%，与极限学习机模型、支持向量机模型相比，BP神经网络识别效果最优。Abstract: At present, it is difficult to meet the emergency rescue needs of gas and coal dust explosion accidents due to the rate of missing and false alarms in coal mine gas and coal dust explosion monitoring. In order to solve the above problems, a coal mine gas and coal dust explosion sound recognition method based on wavelet packet energy is proposed. This method installs mine-used pickups in the key monitoring areas of the coal mine to collect the working sound and environmental sound of the coal mine equipment in real-time. The wavelet packet energy ratio of sound is extracted through wavelet packet decomposition, and the feature vector characterizing the sound signal is formed. The feature vector is input into the BP neural network to obtain the sound recognition model of coal mine gas and coal dust explosion. The wavelet packet energy ratio of the sound signal to be measured is extracted and input into the model as the feature vector to recognize the type of sound signal to be measured. According to the requirements of feature vectors and output results, a BP neural network with 8, 8 and 1 nodes in the input layer, hidden layer and output layer is established to train the recognition model. By analyzing the results of wavelet packet decomposition of underground acoustic signals in coal mines, it is confirmed that the Haar wavelet function is used and the number of wavelet packet decomposition layers is chosen to be 3. The experimental results show that the energy proportion of gas and coal dust explosion sound decomposed by wavelet packet is obviously different from other sounds. The wavelet packet energy proportion distribution of the same sound signal with different time is stable. Therefore, the wavelet packet energy proportion can effectively represent the features of the sound signal and has strong robustness. BP neural network training speed is fast, and only a small number of training iterations can achieve the expected error. The recognition accuracy is up to 95% in the presence of many disturbing sound signals in the coal mine. BP neural network has the best recognition effect compared with the extreme learning machine model and support vector machine model.
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图 1 基于小波包能量的煤矿瓦斯和煤尘爆炸声音识别方法原理
Figure 1. Principle of coal mine gas and coal dust explosion sound recognition method based on wavelet packet energy
图 2 经小波包分解的声音高频分量小波包能量占比差值分布
Figure 2. Distribution of wavelet packet energy proportion difference of high frequency sound component decomposed by wavelet packet
图 3 基于Haar小波函数的声音信号小波包分解结果及小波包系数分布
Figure 3. Wavelet packet decomposition results and wavelet packet coefficient distribution of sound signals based on Haar wavelet function
图 4 基于db4小波函数的声音信号小波包分解结果及小波包系数分布
Figure 4. Wavelet packet decomposition results and wavelet packet coefficient distribution of sound signals based on db4 wavelet function
图 5 不同时长下声音小波包能量占比分布
Figure 5. Wavelet packet energy proportion distribution of sound under different time
表 1 煤矿井下声音小波包能量占比分布
Table 1. Wavelet packet energy proportion distribution of sound in underground coal mine
% 声音 能量占比 d1 d2 d3 d4 d5 d6 d7 d8 瓦斯爆炸 87.280 8.081 1.013 2.581 0.230 0.539 0.048 0.228 煤尘爆炸 90.100 6.316 0.810 1.969 0.185 0.411 0.037 0.173 采煤机 99.798 0.151 0.036 0.004 0.009 0.001 0 0 刮板输送机 96.802 2.366 0.548 0.110 0.134 0.025 0.008 0.006 转载机 94.162 4.021 0.836 0.567 0.198 0.119 0.037 0.060 破碎机 97.182 1.961 0.403 0.260 0.096 0.053 0.016 0.029 乳化液泵 95.135 3.106 0.690 0.602 0.162 0.131 0.080 0.093 掘进机 94.002 3.944 0.792 0.749 0.181 0.149 0.077 0.105 锚杆机 57.115 9.755 8.843 7.940 2.054 2.898 7.320 4.075 风镐 37.388 21.513 10.640 13.989 1.537 3.236 6.919 4.778 馈电开关设备 99.801 0.140 0.036 0.007 0.009 0.003 0.003 0.002 高爆开关设备 98.624 1.029 0.251 0.022 0.062 0.006 0.003 0.002 移动变电站 99.199 0.604 0.147 0.009 0.037 0.002 0.001 0.001 通风机 85.903 9.983 2.096 1.112 0.507 0.237 0.055 0.108 水泵 91.574 5.867 1.235 0.733 0.287 0.152 0.069 0.083 胶带 90.865 6.463 1.290 0.780 0.310 0.165 0.046 0.079 胶轮车 98.605 1.053 0.255 0.017 0.063 0.004 0.001 0 
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表 2 不同模型识别结果
Table 2. Recognition results of different models
% 模型 识别率 召回率 精确率 BP神经网络模型 95 75 100 SVM模型 91 69 100 ELM模型 84 20 100
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