基于LSTM网络的矿山压力时空混合预测  

Mixed Prediction of Mine Pressure Time and Space Based on LSTM Network

作　　者：余琼芳[1,2] 牛冬阳 YU Qiongfang;NIU Dongyang(School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454000,China;Postdoctoral Research Workstation of Beijing Research Institute,Dalian University of Technology,Beijing 100000,China)

机构地区：[1]河南理工大学电气工程与自动化学院,河南焦作454000 [2]大连理工大学北京研究院博士后科研工作站,北京100000

出　　处：《电子科技》2023年第2期67-72,共6页Electronic Science and Technology

基　　金：国家自然科学基金(61601172);中国博士后科学基金资助项目(2018M641287)。

摘　　要：矿山压力失衡引起的顶板事故是矿山重大事故之一,超前感知综采工作面矿山压力的变化对保证煤层安全高效具有重要意义。为了提高矿压预测准确性,文中提出了一种基于LSTM网络的时空混合预测模型。该模型采用两个独立的LSTM网络分别提取采空区侧和支架移架侧的压力特征,然后将得到的数据通过全连接层融合,从而实现对矿压的共同预测。文中以MSE和MAE来评估基于LSTM的时空混合模型的预测效果,实验结果表明MSE和MAE分别下降了24.49%和35.24%,说明基于LSTM的时空混合预测模型优于传统LSTM预测模型,且时空混合模型预测方法较传统模型具有更高的可靠性和准确性,能够实现工作面推进过程中对矿压变化的有效预测。The roof accident caused by the unbalanced pressure of the mine is one of the major mine accidents,so it is of great significance to perceive the change of the mine pressure in the fully mechanized mining face in advance to ensure the safety and efficiency of the coal seam.To improve the accuracy of mine pressure prediction,a spatiotemporal hybrid prediction model based on LSTM network is proposed in this study.Two independent LSTM networks are used to extract the pressure characteristics on the goaf side and the support moving side respectively,and the obtained data are merged through the fully connected layer,so as to realize the common prediction of mine pressure.Two indicators,MSE and MAE,are used to evaluate the prediction effect of the spatio-temporal mixture model based on LSTM.The experimental results show that MSE and MAE are reduced by 24.49%and 35.24%,respectively.The spatio-temporal mixture prediction model based on LSTM is excellent when compared with the traditional LSTM prediction model,and the prediction method of the spatio-temporal hybrid model has higher reliability and accuracy than the traditional model,and can predict the changes in the mine pressure during the advancement of the working face.

关 键 词：综采工作面 液压支架 矿压分析 深度学习 矿压预测 时间序列预测 LSTM神经网络 混合LSTM 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TN99[自动化与计算机技术—控制科学与工程]