基于小波包熵和数据融合的煤与瓦斯突出预警
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摘要
在深入分析煤与瓦斯突出前的瓦斯浓度变化基础上,提出了基于小波包熵和数据融合的煤与瓦斯突出预警方法。利用基于均值的分批估计融合方法对瓦斯浓度多传感器数据进行处理,以获取煤与瓦斯突出预警所需更为准确、可靠的数据;利用瓦斯浓度变化的小波包熵特征,量化瓦斯浓度变化的无序程度;利用基于小波包熵特征的煤与瓦斯突出预警模型,进行煤与瓦斯突出的实时预警;利用基于重构信号能量的小波包分解层数确定方法,对瓦斯浓度变化的小波包熵特征提取所需的小波包分解层数进行科学确定。通过实例验证表明,煤与瓦斯突出前瓦斯浓度发生忽大忽小的无序变化时,所提方法可以有效探测工作面的煤与瓦斯突出危险性。
By analyzing gas concentration change before coal and gas outburst, an early-warning method based on wavelet packet entropy and data fusion was proposed. The data fusion method based on the arithmetic mean and batch estimation is used to deal with the collected multi-sensor gas data so as to improve the precision and reliability of these data. The wavelet packet entropy feature is used to quantize the disordered degree of gas concentration change. An early-warning model for coal and gas outburst based on the wavelet packet entropy feature is used to carry out real-time early-warning. A selection method of the wavelet packet decomposition level based on restructure signal energy was given to scientifically obtain the wavelet packet decomposition level. The proposed method was validated using practical measured time-series data. The simulation example shows that the proposed method can monitor the risk for coal and gas outburst in the working face when the gas concentration change is abnormal before coal and gas outburst.
By analyzing gas concentration change before coal and gas outburst, an early-warning method based on wavelet packet entropy and data fusion was proposed. The data fusion method based on the arithmetic mean and batch estimation is used to deal with the collected multi-sensor gas data so as to improve the precision and reliability of these data. The wavelet packet entropy feature is used to quantize the disordered degree of gas concentration change. An early-warning model for coal and gas outburst based on the wavelet packet entropy feature is used to carry out real-time early-warning. A selection method of the wavelet packet decomposition level based on restructure signal energy was given to scientifically obtain the wavelet packet decomposition level. The proposed method was validated using practical measured time-series data. The simulation example shows that the proposed method can monitor the risk for coal and gas outburst in the working face when the gas concentration change is abnormal before coal and gas outburst.
引文
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[10]秦汝祥,张国枢,杨应迪.瓦斯浓度序列的煤与瓦斯突出预报方法及应用[J].安徽理工大学学报,2008,28(1):25-29.Qin R X, Zhang G S, Yang Y D. Method of Coal and Gas Outburst Forecast by Sequence of Gas Concentration and Its Application[J].Journal of Anhui University of Science and Technology, 2008, 28(1):25-29.
[11]姜福兴,尹永明,朱权洁,等.基于掘进面应力和瓦斯浓度变化的煤与瓦斯突出预警试验研究[J].岩石力学与工程学报,2014, 33(增2):3581-3589.Jiang F X, Yin Y M, Zhu Q J, et al. Experimental Study of Precaution Technology of Heading Face Coal and Gas Outburst Based on Dynamic Changes of Stress and Methane Concentration[J]. Chinese Journal of Rock Mechanics and Engineering, 2014, 33(Supp. 2):3581-3589.
[12]王栓林,樊少武,马超.突出危险性预测中的瓦斯浓度实时指标研究[J].煤炭科学技术,2010, 38(5):54-57.Wang S L, Fan S W, Ma C. Study on Real Time Index of Gas Content in Prediction of Outburst Danger[J]. Coal Science and Technology,2010,38(5):54-57.
[13]李希建,周炜光.基于瓦斯峰谷比值法的炮掘工作面突出危险性预测[J].煤炭学报,2012,37(增1):104-108.Li X J, Zhou W G. The Risk Forecast of Coal and Gas Outburst on Blasting-working Face by the Method of Gas Peak-to-valley Ratio[J].Journal of China Coal Society. 2012,37(Supp. 1):104-108.
[14]张纯如,汪勇,丁梅生.矿井瓦斯浓度异常变化危险性预警的研究[J].安徽理工大学学报,2011,31(3):62-67.Zhang C R, Wang R, Ding M S. Study on Risk Warning of Gas Concentration Abnormal Changes in Coal Mines[J]. Journal of Anhui University of Science and Technology, 2011,31(3):62-67.
[15]杨艳国,穆永亮,秦洪岩.工作面瓦斯浓度时间序列特征挖掘与预警应用[J].中国安全科学学报,2018,28(3):120-125.Yang Y G, Mu Y L, Qin H Y. Research on Time Series Characteristics of Gas Concentration at Working Face and Application of Them to Early Warning[J]. China Safety Science Journal, 2018,28(3):120-125.
[2] Zhou Q L, Herrera J, Hidalgo A. Development of a Quantitative Assessment Approach for the Coal and Gas Outbursts in Coal Mines Using Rock Engineering Systems[J]. International Journal of Mining Reclamation and Environment, 2017:1-21.
[3]阎馨,付华,屠乃威.基于聚类和案例推理的煤与瓦斯突出动态预测[J].传感技术学报,2016,29(4):545-551.Yan X, Fu H, Tu N W. Dynamic Prediction of Coal and Gas Outburst Based on Clustering and Case-Based Reasoning[J].Chinese Journal of Sensors and Actuators, 2016,29(4):545-551.
[4] Yan Z G, Kan Y, Yang Y X. A Novel Adaptive Differential Evolution SVM Model for Predicting Coal and Gas Outbursts[J]. Journal of Difference Equations and Applications, 2016,23(1-2):238-248.
[5] Tang J, Jiang C L, Chen Y J, et al. Line Prediction Technology for Forecasting Coal and Gas Outbursts during Coal Roadway Tunneling[J]. Journal of Natural Gas Science and Engineering, 2016,34:412-418.
[6] Li Z H, Wang E Y, Ou J C, et al. Hazard Evaluation of Coal and Gas Outbursts in a Coal-mine Roadway Based on Logistic Regression Model[J]. International Journal of Rock Mechanics&Mining sciences, 2015, 80:185-195.[71阎馨,付华,屠乃威.基于PCA和案例推理的煤与瓦斯突出动态预测[J].传感技术学报,2015,28(7):1028-1034.Yan X, Fu H, Tu N W. Dynamic Prediction of Coal and Gas Outburst Based on PCA and Case-Based Reasoning[J].Chinese Journal of Sensors and Actuators, 2015,28(7):1028-1034.
[8]王存文,姜福兴,孙庆国,等.基于覆岩空间结构理论的冲击低压预测技术及应用[J].煤炭学报,2009,34(2):150-155.Wang C W, Jiang F X, Sun Q G, et al. The Forecasting Method of Rock-burst and the Application Based on Overlying Multi-strata Spatial Structure Theory[J]. Journal of China Coal Society, 2009,34(2):150-155.
[9]姜耀东,王涛,宋义敏,等.煤岩组合结构失稳滑动过程的实验研究[J].煤炭学报,2013,38(2):177-182.Jiang Y D, Wang T, Song Y M, et al. Experimental Study on the Stick-slip Process of Coal-rock Composite Samples[J]. Journal of China Coal Society, 2013,38(2):177-182.
[10]秦汝祥,张国枢,杨应迪.瓦斯浓度序列的煤与瓦斯突出预报方法及应用[J].安徽理工大学学报,2008,28(1):25-29.Qin R X, Zhang G S, Yang Y D. Method of Coal and Gas Outburst Forecast by Sequence of Gas Concentration and Its Application[J].Journal of Anhui University of Science and Technology, 2008, 28(1):25-29.
[11]姜福兴,尹永明,朱权洁,等.基于掘进面应力和瓦斯浓度变化的煤与瓦斯突出预警试验研究[J].岩石力学与工程学报,2014, 33(增2):3581-3589.Jiang F X, Yin Y M, Zhu Q J, et al. Experimental Study of Precaution Technology of Heading Face Coal and Gas Outburst Based on Dynamic Changes of Stress and Methane Concentration[J]. Chinese Journal of Rock Mechanics and Engineering, 2014, 33(Supp. 2):3581-3589.
[12]王栓林,樊少武,马超.突出危险性预测中的瓦斯浓度实时指标研究[J].煤炭科学技术,2010, 38(5):54-57.Wang S L, Fan S W, Ma C. Study on Real Time Index of Gas Content in Prediction of Outburst Danger[J]. Coal Science and Technology,2010,38(5):54-57.
[13]李希建,周炜光.基于瓦斯峰谷比值法的炮掘工作面突出危险性预测[J].煤炭学报,2012,37(增1):104-108.Li X J, Zhou W G. The Risk Forecast of Coal and Gas Outburst on Blasting-working Face by the Method of Gas Peak-to-valley Ratio[J].Journal of China Coal Society. 2012,37(Supp. 1):104-108.
[14]张纯如,汪勇,丁梅生.矿井瓦斯浓度异常变化危险性预警的研究[J].安徽理工大学学报,2011,31(3):62-67.Zhang C R, Wang R, Ding M S. Study on Risk Warning of Gas Concentration Abnormal Changes in Coal Mines[J]. Journal of Anhui University of Science and Technology, 2011,31(3):62-67.
[15]杨艳国,穆永亮,秦洪岩.工作面瓦斯浓度时间序列特征挖掘与预警应用[J].中国安全科学学报,2018,28(3):120-125.Yang Y G, Mu Y L, Qin H Y. Research on Time Series Characteristics of Gas Concentration at Working Face and Application of Them to Early Warning[J]. China Safety Science Journal, 2018,28(3):120-125.