基于CNN的煤岩瓦斯复合动力灾害预测

王凯; 李康楠; 杜锋; 张翔; 王衍海; 周家旭

doi:10.19606/j.cnki.jmst.2023.05.003

基于CNN的煤岩瓦斯复合动力灾害预测

doi: 10.19606/j.cnki.jmst.2023.05.003

王凯^{1, 2,},
李康楠^{1, 2},
杜锋^{1, 2},
张翔^{1, 2},
王衍海^{1, 2},
周家旭²

1.
中国矿业大学(北京)共伴生能源精准开采北京市重点实验室，北京 100083
2.
中国矿业大学(北京)应急管理与安全工程学院，北京 100083

基金项目:

国家自然科学基金 52130409

国家自然科学基金 52004291

详细信息

作者简介:
王凯(1972—)，男，河南遂平人，教授，博士生导师，主要从事安全工程与应急管理、矿井瓦斯及煤岩动力灾害防治、矿井通风等方面的教学与研究工作。Tel：13810850966，E-mail：kaiwang@cumtb.edu.cn

中图分类号: TD713
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- 文章访问数: 96
- HTML全文浏览量: 22
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- 被引次数: 0
出版历程
- 收稿日期: 2023-02-17
- 修回日期: 2023-04-03
- 刊出日期: 2023-10-31

Prediction of coal-gas compound dynamic disaster based on convolutional neural network

Wang Kai^{1, 2
,},
Li Kangnan^{1, 2},
Du Feng^{1, 2},
Zhang Xiang^{1, 2},
Wang Yanhai^{1, 2},
Zhou Jiaxu²

1.
Key Laboratory for Precise Mining of Intergrown Energy and Resources, China University of Mining and Technology-Beijing, Beijing 100083, China
2.
School of Emergency Management and Safety Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

摘要

摘要: 随着我国煤矿开采逐渐进入深部区域，煤岩瓦斯复合动力灾害日益严重，对煤矿的安全生产造成极大威胁。基于某矿现场数据，采用智能预测手段对煤岩瓦斯复合动力灾害进行研究。首先，依据大数据处理流程，应用箱型图分析法(Box-plot)与多重插补法(MI)进行数据清洗，结合灰色关联度分析法(GRA)建立煤岩瓦斯复合动力灾害指标体系；然后应用主成分分析法(PCA)进行数据降维，结合深度学习中的卷积神经网络(CNN)建立基于BMGP-CNN的煤岩瓦斯复合动力灾害预测模型；运用现场案例数据将此模型与BP模型、随机森林(RF)模型、支持向量机(SVM)模型及人工神经网络(ANN)模型进行对比验证，发现BMGP-CNN模型预测结果的准确率最高，且该模型的收敛速度较快，能够在数秒内完成预测。研究结果对于煤岩瓦斯复合动力灾害的预测和防控具有重要意义。
- 煤岩瓦斯复合动力灾害 /
- 深度学习 /
- 大数据 /
- 指标体系 /
- 预测模型
Abstract: As deep mining becomes prevalent in China's coal mining industry, coal-gas compound dynamic disasters pose increasing threat to the safety production of coal mines. This paper adopts the field data of Pingmei No. 8 coal mine for analysis, with the attempt to predict coal-gas compound dynamic disaster through convolutional neural network. Following the routine of the big data processing, we first employed Box-plot analysis and multiple interpolation method(MI)to clean the data. Combined with grey relation analysis(GRA), we established a coal-gas compound dynamic disaster index system. Then, principal component analysis(PCA)is used for dimensionality reduction of the data. Combined with the convolution neural network(CNN)in deep learning, we established the coal-gas compound dynamic disaster prediction model based on BMGP-CNN. The field data is used to compare and verify this model with BP, random forest(RF), support vector machine(SVM)and artificial neural network(ANN). It is found that BMGP-CNN model yields prediction results with satisfactory accuracy and quick convergence. The results offer implications for the prediction and prevention of coal-gas compound dynamic disasters.
- coal-gas compound dynamic disaster /
- deep learning /
- big data /
- index system /
- prediction model

HTML全文

图 1 煤岩瓦斯复合动力灾害预测模型建立流程

Figure 1. Prediction model of coal-gas compound dynamical disaster

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图 2 卷积神经网络结构示意图

Figure 2. Structure of convolutional neural network

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图 3 煤岩瓦斯复合动力灾害预测指标体系

Figure 3. Prediction index system of coal-gas compound dynamical disasters

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图 4 碎石图

Figure 4. Broken stone diagram

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图 5 卷积层参数优化

Figure 5. Parameter optimization of convolution layer

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图 6 其他参数优化

Figure 6. Other parameter optimization

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图 7 样本预测结果和实际结果

Figure 7. Sample prediction results

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表 1 部分完整的初始数据

Table 1. Initial data(part)

序号	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀	X₁₁	X₁₂	X₁₃	X₁₄	X₁₅	X₁₆	结果
1	0.17	34.15	2.62	0	830	4.5	14.30	4	0.5	58.03	22.98	9	1.5	0.22	0	0	1
2	0.50	28.10	0.20	1	434	4.5	2.22	2	0.5	59.87	8.67	10	0.6	0.07	0.47	0	0
3	0.24	29.06	0.50	1	589	3.2	6.21	3	0.5	76.79	11.05	18	0.8	0.10	0	0	0
4	0.44	29.06	0.78	1	588	2.9	6.98	3	0.5	43.44	9.29	19	1.2	0.47	0	0	2
5	0.81	29.06	0.42	1	566	3.5	5.84	2	0.5	45.78	9.00	16	0	0	0	0	2
6	0.17	34.15	2.65	0	840	4.5	14.10	4	0.5	35.05	23.52	11	1.2	0.19	0	0	1
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
105	0.22	29.06	0.42	1	557	3.1	5.67	3	0.5	73.13	21.08	21	0	0	0.36	0.17	1

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表 2 关联度与关联度排序

Table 2. Relevance and relevance ranking

影响因素	关联度	关联度排名
X₁	0.879 48	1
X₂	0.879 45	2
X₃	0.874 15	3
X₄	0.872 95	4
X₅	0.872 83	5
X₆	0.869 74	6
X₇	0.867 43	7
X₈	0.867 35	8
X₉	0.849 27	9
X₁₀	0.849 00	10
X₁₁	0.836 547	11
X₁₂	0.821 76	12
X₁₃	0.788 63	13
X₁₄	0.786 68	14
X₁₅	0.776 99	15
X₁₆	0.762 00	16

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表 3 成分矩阵

Table 3. Component matrix

原始指标	成分
原始指标	1	2	3	4
埋深	0.887	-0.276	0.073	-0.062
软分层变化	0.001	0.468	0.729	0.045
煤体破坏类型	0.603	0.402	-0.088	0.223
煤厚	-0.029	0.108	0.142	0.837
断层数量	0.689	0.449	-0.284	0.093
坚固性系数	-0.483	-0.608	0.402	0.066
瓦斯压力	0.580	-0.209	0.393	0.220
瓦斯含量	0.810	-0.319	0.112	-0.213
顶板抗压强度	-0.042	0.547	0.386	-0.474
最大主应力	0.828	-0.237	0.095	-0.075

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表 4 计算后的部分公因子数据

Table 4. Calculated common factor data(part)

序号	Y₁	Y₂	Y₃	Y₄
1	16.122	-1.956	1.166	-0.143
2	-5.264	0.002	0.055	0.229
3	1.120	0.789	0.024	-1.968
4	0.906	-1.686	-0.191	-1.054
5	-2.115	-3.156	0.760	-0.670
6	16.336	-2.853	0.500	0.690
⋮	⋮	⋮	⋮	⋮
105	1.487	-1.247	0.025	0.504

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表 5 卷积神经网络预测模型参数

Table 5. Structure of CNN prediction model

模型参数	取值
卷积核尺寸	1×5
卷积核数量	128
步长	1
卷积层数	1
激活函数	ReLU
池化层参数	1×5，1
Dropout概率	0

下载: 导出CSV

表 6 各个模型的预测结果与准确率

Table 6. Prediction results and accuracy of each model

序号	实际结果	BP	RF	SVM	ANN
1	0	0	1	0	0
2	2	2	2	0	2
3	1	1	0	2	1
4	2	2	2	2	2
5	0	0	0	0	1
6	0	1	2	0	0
7	2	2	2	2	2
8	1	1	1	1	1
9	0	0	0	0	2
10	2	2	2	0	2
11	1	1	1	1	1
12	2	0	2	2	2
13	1	2	1	1	2
14	1	2	1	1	1
15	2	1	2	1	2
测试集准确率/%		66.7	80	73.3	80

下载: 导出CSV

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基于CNN的煤岩瓦斯复合动力灾害预测

doi: 10.19606/j.cnki.jmst.2023.05.003

作者简介:
王凯(1972—)，男，河南遂平人，教授，博士生导师，主要从事安全工程与应急管理、矿井瓦斯及煤岩动力灾害防治、矿井通风等方面的教学与研究工作。Tel：13810850966，E-mail：kaiwang@cumtb.edu.cn

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Prediction of coal-gas compound dynamic disaster based on convolutional neural network

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基于CNN的煤岩瓦斯复合动力灾害预测

doi: 10.19606/j.cnki.jmst.2023.05.003

作者简介: 王凯(1972—)，男，河南遂平人，教授，博士生导师，主要从事安全工程与应急管理、矿井瓦斯及煤岩动力灾害防治、矿井通风等方面的教学与研究工作。Tel：13810850966，E-mail：kaiwang@cumtb.edu.cn

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Prediction of coal-gas compound dynamic disaster based on convolutional neural network

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作者简介:
王凯(1972—)，男，河南遂平人，教授，博士生导师，主要从事安全工程与应急管理、矿井瓦斯及煤岩动力灾害防治、矿井通风等方面的教学与研究工作。Tel：13810850966，E-mail：kaiwang@cumtb.edu.cn