基于LSTM改进Transformer的煤自燃温度预测模型  

Prediction Model of Coal Spontaneous Combustion Temperature Based on Transformer Improved by LSTM

作　　者：童保国 姜福领 毕寸光 王亮 田坤云[3] TONG Baoguo;JIANG Fuling;BI Cunguang;WANG Liang;TIAN Kunyun(Anyang Zhujiao Coal Industry Co.,Ltd.,Anyang 455000,China;Dongpang Mine,Jizhong Energy Resources Co.,Ltd.,Xingtai 054200,China;School of Resources and Safety Engineering,Henan University of Engineering,Zhengzhou 451191,China)

机构地区：[1]安阳市主焦煤业有限责任公司,河南安阳455000 [2]冀中能源股份有限公司东庞矿,河北邢台054200 [3]河南工程学院资源与安全工程学院,河南郑州451191

出　　处：《金属矿山》2024年第12期275-280,共6页Metal Mine

基　　金：河南省高校科技创新团队计划项目(编号:22IRTSTHN009)。

摘　　要：及时准确地预测煤自燃温度对于预防煤矿安全事故的发生至关重要,传统预测方法存在数据特征提取不充分的问题,提出了一种基于LSTM改进Transformer的煤自燃温度预测模型。首先,利用长短时记忆神经网络(Long Short Term Memory,LSTM)从时间维度捕获给定煤自燃温度文本数据中的时序特征,强化模型对于时间序列数据的长距离依赖性建模能力;然后,将LSTM网络的输出作为Transformer网络的输入,充分利用Transformer网络的多头注意力机制对时序特征的上下文位置信息进行编码压缩,细粒度地捕获煤自燃温度文本数据在时序维度的特征全局关联性;最后,将集成LSTM上下文时序和Transformer位置编码强化后的特征作为全连接神经网络的输入,预测下一时刻的煤自燃温度。通过在开源的某矿煤自燃发火试验数据集上进行测试,所提模型在平均绝对误差(Mean Absolute Error,MAE)、平均百分比误差(Mean Absolute Percentage Error,MAPE)和均方根误差(Root Mean Square Error,RMSE)3个指标上分别实现了10.62、6.04%和25.19%的性能值,优于当前主流的时间序列神经网络LSTM、GRU、BiLSTM,有一定的应用潜力。Timely and accurate prediction of coal spontaneous combustion temperature is crucial to prevent coal mine safety accidents.Traditional prediction methods have the problem of insufficient data feature extraction.A coal spontaneous combustion temperature prediction model based on LSTM and improved Transformer is proposed.Firstly,a Long Short Term Memory(LSTM)neural network is adopted to capture the time sequence features of given text data of spontaneous combustion temperature of coal from time dimension to strengthen the long-distance dependence modeling ability of the model for time series data.Then,the output of LSTM network is taken as the input of Transformer network,and the multi-head attention mechanism of Transformer network is fully utilized to encode and compress the context location information of the timing characteristics,and the global feature correlation of the text data of spontaneous combustion temperature in the timing dimension is captured in a fine granularity.Finally,the features integrated with LSTM context timing and Transformer position coding are used as the input of the fully connected neural network to predict the spontaneous combustion temperature of coal at the next moment.Through testing on the open source coal spontaneous combustion experimental data set of a mine,the proposed model achieves 10.62,6.04%and 25.19%performance values under Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE)and Root Mean Square Error(RMSE),respectively,which is superior to the current mainstream time series neural networks LSTM,GRU and Bi LSTM,and has certain application potentical.

关 键 词：煤自燃温度预测 TRANSFORMER LSTM 时序维度 位置编码 

分 类 号：TD75[矿业工程—矿井通风与安全]