| Citation: |
JI Wenli，DAN Xin，MA Chenyang，et al. Multi-step prediction of coal mine adit deformation based on time convolutional long short-term memory[J]. Coal Science and Technology，2025，53（4）：176−190. DOI: 10.12438/cst.2023-1480
|
Coal main adit is susceptibility to external factors, so it is crucial to monitor and predict its deformation. Based on the application of fiber optic monitoring for adit deformation, A multi-step prediction model is proposed which is built with Ensemble Empirical Mode Decomposition (EEMD) integrated with TCN-LSTM deep learning network for adit deformation. Firstly, the monitoring data containing noise is decomposed into several Intrinsic Mode Functions (IMF) components using the EEMD method. Then, the fuzzy entropy of each IMF component series is calculated, and effective IMF components are selected. Finally, the TCN network is used to extract long-term features from different effective component series, while the LSTM network captures nonlinear features. The prediction results of each component are combined. The multi-output strategy is adopted in the training process of the prediction model, and the output is the fiber optic monitoring value for multiple times in the future. Experimental results on different fiber optic grating sensors show that the EEMD combined with fuzzy entropy method can filter out more noise while retaining the roadway deformation information. Compared with existing methods, the proposed prediction method has a coefficient of determination (R2) of 0.99, and the root mean square error (RMSE) and mean absolute error (MAE) are reduced by 3.0%−10.0% and 5.0%−20.0% in single-step prediction, respectively, resulting in more accurate predictions. Under the multi-output strategy, the average R2 of this proposed method for three steps ahead is 0.95, and the RMSE and MAE values of the strain gauge are reduced by at least 75.0% and 31.5%. The RMSE and MAE values of the displacement meter were reduced by at least 50.0% and 66.7%, respectively, while the RMSE and MAE values of the pressure gauge were reduced by at least 85.7% and 62.3%. the proposed prediction method with multi-output strategy has the lowest error accumulation. The EEMD-TCN-LSTM multi-step prediction method for adit deformation provides a technical basis for predicting the deformation of roadway surrounding rock.
| [1] |
朱磊,袁超峰,吴玉意,等. 覆土偏载对浅埋主平硐衬砌支护结构影响研究[J]. 采矿与安全工程学报,2023,40(2):263−273.
ZHU Lei,YUAN Chaofeng,WU Yuyi,et al. Study on the influence of the static load of the surface covering soil on the stability of the shallow-buried main adit[J]. Journal of Mining & Safety Engineering,2023,40(2):263−273.
|
| [2] |
杨艳国,范楠. 基于单孔声波法测试巷道围岩松动圈试验研究[J]. 煤炭科学技术,2019,47(3):93−100.
YANG Yanguo,FAN Nan. Experimental study on surrounding rock loosing circle by single-hole acoustic wave testing method[J]. Coal Science and Technology,2019,47(3):93−100.
|
| [3] |
周辉,渠成堃,王竹春,等. 深井巷道掘进围岩演化特征模拟与扰动应力场分析[J]. 岩石力学与工程学报,2017,36(8):1821−1831.
ZHOU Hui,QU Chengkun,WANG Zhuchun,et al. Simulating the variation of surrounding rock and analyzing the disturbed stress field during excavation of deep mine roadway[J]. Chinese Journal of Rock Mechanics and Engineering,2017,36(8):1821−1831.
|
| [4] |
柴敬,张丁丁,李毅. 光纤传感技术在岩土与地质工程中的应用研究进展[J]. 建筑科学与工程学报,2015,32(3):28−37. doi: 10.3969/j.issn.1673-2049.2015.03.005
CHAI Jing,ZHANG Dingding,LI Yi. Research progress of optical fiber sensing technology in geotechnical and geological engineering[J]. Journal of Architecture and Civil Engineering,2015,32(3):28−37. doi: 10.3969/j.issn.1673-2049.2015.03.005
|
| [5] |
CHENG L,PAN P S,SUN Y K,et al. A distributed fibre optic monitoring method for ground subsidence induced by water pipeline leakage[J]. Optical Fiber Technology,2023,81:103495. doi: 10.1016/j.yofte.2023.103495
|
| [6] |
侯公羽,韩育琛,谢冰冰,等. 定点式布设光纤在隧道结构健康监测中的预拉应变损失研究[J]. 岩土力学,2019,40(10):4120−4128.
HOU Gongyu,HAN Yuchen,XIE Bingbing,et al. Pretension strain loss of fixed-point optical fiber in tunnel structural health monitoring[J]. Rock and Soil Mechanics,2019,40(10):4120−4128.
|
| [7] |
侯公羽,胡志宇,李子祥等. 分布式光纤及光纤光栅传感技术在煤矿安全监测中的应用现状及展望[J]. 煤炭学报,2023,48(S1):96−110.
HOU Gongyu,HU Zhiyu,LI Zixiang,et al. Present situation and prospect of coal mine safety monitoring based on fiber bragg grating and distributed optical fiber sensing technology[J]. Journal of China Coal Society,2023,48(S1):96−110.
|
| [8] |
程刚,王振雪,施斌,等. DFOS在矿山工程安全开采监测中的研究进展[J]. 煤炭学报,2022,47(8):2923−2949.
CHENG Gang,WANG Zhenxue,SHI Bin,et al. Research progress of DFOS in safety mining monitoring of mines[J]. Journal of China Coal Society,2022,47(8):2923−2949.
|
| [9] |
杜文刚,柴敬,张丁丁,等. 采动覆岩导水裂隙发育光纤感测与表征模型试验研究[J]. 煤炭学报,2021,46(5):1565−1575.
DU Wengang,CHAI Jing,ZHANG Dingding,et al. Optical fiber sensing and characterization of water flowing fracture development in mining overburden[J]. Journal of China Coal Society,2021,46(5):1565−1575.
|
| [10] |
李延河,杨战标,朱元广,等. 基于弱光纤光栅传感技术的围岩变形监测研究[J]. 煤炭科学技术,2023,51(6):11−19.
LI Yanhe,YANG Zhanbiao,ZHU Yuanguang,et al. Research on deformation monitoring of surrounding rock based on weak fiber grating sensing technology[J]. Coal Science and Technology,2023,51(6):11−19.
|
| [11] |
柴敬,刘泓瑞,张丁丁,等. 覆岩载荷扰动下平硐围岩变形分析及支护优化[J]. 工矿自动化,2023,49(3):13−22.
CHAI Jing,LIU Hongrui,ZHANG Dingding,et al. Deformation analysis and support optimization of adit surrounding rock under overburden load disturbance[J]. Industry and Mine Automation,2023,49(3):13−22.
|
| [12] |
马晋美. 基于功率谱密度筛选IMF噪声分量的光纤陀螺振动噪声降噪方法[J]. 自动化与仪表,2022,37(12):75−78,102.
MA Jinmei. Vibration noise reduction method of fiber optic gyroscope based on power spectral density screening IMF noise component[J]. Automation & Instrumentation,2022,37(12):75−78,102.
|
| [13] |
ZHAO H M,SUN M,DENG W,et al. A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing[J]. Entropy,2017,19(1):14.
|
| [14] |
QIN Q,LAI X,ZOU J. Direct multistep wind speed forecasting using LSTM neural network combining EEMD and fuzzy entropy[J]. Applied Sciences,2019,9(1):126.
|
| [15] |
YAO B Z,YANG C Y,YAO J B,et al. Tunnel surrounding rock displacement prediction using support vector machine[J]. International Journal of Computational Intelligence Systems,2010,3(6):843−852.
|
| [16] |
张志强,李化云,阚呈,等. 大相岭隧道断层破碎带围岩变形的GA-BP神经网络预测技术[J]. 现代隧道技术,2014,51(2):83−89. doi: 10.3969/j.issn.1009-6582.2014.02.014
ZHANG Zhiqiang,LI Huayun,KAN Cheng,et al. Prediction of surrounding rock deformation of the daxiangling tunnel in fault zones using the GA-BP nerve network technique[J]. Modern Tunnelling Technology,2014,51(2):83−89. doi: 10.3969/j.issn.1009-6582.2014.02.014
|
| [17] |
文明,张顶立,房倩,等. 隧道围岩变形的非线性自回归时间序列预测方法研究[J]. 北京交通大学学报,2017,41(4):1−7. doi: 10.11860/j.issn.1673-0291.2017.04.001
WEN Ming,ZHANG Dingli,FANG Qian,et al. Research on nonlinear auto regressive time series method for predicting deformation of surrounding rock in tunnel[J]. Journal of Beijing Jiaotong University,2017,41(4):1−7. doi: 10.11860/j.issn.1673-0291.2017.04.001
|
| [18] |
YAO B,YAO J,ZHANG M,et al. Improved support vector machine regression in multi-step-ahead prediction for rock displacement surrounding a tunnel[J]. Scientia Iranica,2014,21(4):1309−1316.
|
| [19] |
周冠南,孙玉永,贾蓬. 基于遗传算法的BP神经网络在隧道围岩参数反演和变形预测中的应用[J]. 现代隧道技术,2018,55(1):107−113.
ZHOU Guannan,SUN Yuyong,JIA Peng. Application of genetic algorithm based BP neural network to parameter inversion of surrounding rock and deformation prediction[J]. Modern Tunnelling Technology,2018,55(1):107−113.
|
| [20] |
卜庆为. 基于ARMA 时序分析模型的巷道围岩变形预测[J]. 采矿技术,2014,14(1):56−58. doi: 10.3969/j.issn.1671-2900.2014.01.022
BU Qingwei. Deformation prediction of roadway surrounding rock based on ARMA time series analysis model[J]. Mining Technology,2014,14(1):56−58. doi: 10.3969/j.issn.1671-2900.2014.01.022
|
| [21] |
方新秋,梁敏富,李爽,等. 智能工作面多参量精准感知与安全决策关键技术[J]. 煤炭学报,2020,45(1):493−508.
FANG Xinqiu,LIANG Minfu,LI Shuang,et al. Key technologies of multi-parameter accurate perception and security decision in intelligent working face[J]. Journal of China Coal Society,2020,45(1):493−508.
|
| [22] |
MENG X R,CHANG H Q,WANG X Q. Methane concentration prediction method based on deep learning and classical time series analysis[J]. Energies,2022,15(6):2262. doi: 10.3390/en15062262
|
| [23] |
赵毅鑫,杨志良,马斌杰,等. 基于深度学习的大采高工作面矿压预测分析及模型泛化[J]. 煤炭学报,2020,45(1):54−65.
ZHAO Yixin,YANG Zhiliang,MA Binjie,et al. Deep learning prediction and model generalization of ground pressure for deep longwall face with large mining height[J]. Journal of China Coal Society,2020,45(1):54−65.
|
| [24] |
司垒,王忠宾,熊祥祥等. 基于改进U-Net网络模型的综采工作面煤岩识别方法[J]. 煤炭学报,2021,46(S1):578−589.
SI Lei,WANG Zhongbin,XIONG Xiangxiang,et al. Coal-rock recognition method of fully-mechanized coal mining face based on improved U-net network model[J]. Journal of China Coal Society,2021,46(S1):578−589.
|
| [25] |
郗刘涛,基于LSTM的采动覆岩变形监测数据预测方法研究[D]. 西安:西安科技大学,2019:32−47.
XI Liutao. Research on monitoring data forecasting method of mining-induced overburden deformation based on LSTM[D]. Xi’an:Xi’an University of Science and Technology,2019:32−47.
|
| [26] |
LIU W Y,PIAO C D,ZHOU Y Z,et al. Predictive model of overburden deformation:Based on machine learning and distributed optical fiber sensing technology[J]. Engineering Computations,2021,38(5):2207−2227. doi: 10.1108/EC-05-2020-0281
|
| [27] |
HAN C,GONG M,SUN J,et al. Heat load prediction for district heating systems with temporal convolutional network and CatBoost[J]. Thermal Engineering,2023,70(9):719−726. doi: 10.1134/S0040601523090045
|
| [28] |
熊波,李肖霖,王宇晴,等. 基于长短时记忆神经网络的中国地区电离层TEC预测[J]. 地球物理学报,2022,65(7):2365−2377. doi: 10.6038/cjg2022P0557
XIONG Bo,LI Xiaolin,WANG Yuqing,et al. Prediction of ionospheric TEC over China based on long and short-term memory neural network[J]. Chinese Journal of Geophysics,2022,65(7):2365−2377. doi: 10.6038/cjg2022P0557
|
| [29] |
向玲,刘佳宁,苏浩,等. 基于CEEMDAN二次分解和LSTM的风速多步预测研究[J]. 太阳能学报,2022,43(8):334−339.
XIANG Ling,LIU Jianing,SU Hao,et al. Research on multi-step wind speed forecast based on ceemdan secondary decomposition and lstm[J]. Acta Energiae Solaris Sinica,2022,43(8):334−339.
|
| [30] |
覃梦娇. 基于深度学习的海洋环境时空预测方法[D]. 杭州:浙江大学,2021:45−60.
TAN Mengjiao. Research on deep learning-based marine spatiotemporal prediction[D]. Hangzhou:Zhejiang University,2021.
|
| [31] |
柴敬,刘永亮,袁强,等. 矿山围岩变形与破坏光纤感测理论技术及应用[J]. 煤炭科学技术,2021,49(1):208−217.
CHAI Jing,LIU Yongliang,YUAN Qiang,et al. Theory-technology and application of optical fiber sensing on deformation and failure of mine surrounding rock[J]. Coal Science and Technology,2021,49(1):208−217.
|
| [32] |
XIE Lirong,WANG Bin,BAO Hongyin,et al. Super-short-term wind power forecasting based on EEMD-WOA-LSSVM[J]. Acta Energiae Solaris Sinica,2021,42(7):290−297.
|
| [33] |
DE LUCA A,TERMINI S. A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory[J]. Information and Control,1972,20(4):301−312. doi: 10.1016/S0019-9958(72)90199-4
|
| [34] |
刘志慧,徐兴平,牛怀磊,等. 基于EEMD的立管涡激振动响应最优降噪光滑模型参数识别研究[J]. 振动与冲击,2022,41(12):254−260.
LIU Zhihui,XU Xingping,NIU Huailei,et al. A study on parameter identification of optimal noise reduction smooth model for vortex-induced vibration response of riser based on EEMD[J]. Journal of Vibration and Shock,2022,41(12):254−260.
|
| [35] |
LI B,WANG E Y,SHANG Z,et al. Deep learning approach to coal and gas outburst recognition employing modified AE and EMR signal from empirical mode decomposition and time-frequency analysis[J]. Journal of Natural Gas Science and Engineering,2021,90:103942. doi: 10.1016/j.jngse.2021.103942
|
| [36] |
LIU T,LU C,LIU Q Y,et al. Coal and rock hardness identification based on EEMD and multi-scale permutation entropy[J]. Entropy,2021,23(9):1113. doi: 10.3390/e23091113
|
| [1] | YIN Yanchun, ZHENG Wuwei, ZHAO Tongbin, REN Wentao, ZHANG Wei, ZHAO Zhigang. Automatic measurement method of drilling-cuttings of boreholes in the coal seam and test study[J]. COAL SCIENCE AND TECHNOLOGY, 2023, 51(11): 23-32. DOI: 10.12438/cst.2022-2006 |
| [2] | JIANG Yanhang, BAI Gang, ZHOU Xihua, WANG Yuxi, FU Tianyu, HU Kun. Test and analysis of coal adsorption volume of CH4[J]. COAL SCIENCE AND TECHNOLOGY, 2022, 50(12): 144-152. DOI: 10.13199/j.cnki.cst.2021-0617 |
| [3] | ZHANG Jinhua, ZHANG Mengyuan, CHEN Yanpeng, CHEN Zhenhong, CHEN Hao, DONG Zhen, CHEN Shanshan, XUE Junjie. Progresses and revelation of underground coal gasification field test[J]. COAL SCIENCE AND TECHNOLOGY, 2022, 50(2): 213-222. |
| [4] | TANG Jupeng, REN Lingran, PAN Yishan, ZHANG Xin. Simulation test study on coal and gas outburst under conditions of high in-situ stress[J]. COAL SCIENCE AND TECHNOLOGY, 2022, 50(2): 113-121. |
| [5] | LIU Zhenling, ZHENG Zhongya. Simulation test study on temperature field evolution of coal spontaneous combustion in gob[J]. COAL SCIENCE AND TECHNOLOGY, 2020, 48(8): 114-120. |
| [6] | SONG Jinxing, YU Shiyao, SU Xianbo. Study on velocity sensitivity damage mechanism and its proof test of coal reservoir[J]. COAL SCIENCE AND TECHNOLOGY, 2018, (6). |
| [7] | Qin Hongxing Dai Guanglong Zhang Shuchuan Tang Mingyun, . Optimal selection and application of mark gas based on coal low temperature oxidation test[J]. COAL SCIENCE AND TECHNOLOGY, 2015, (6). |
| [8] | Study on Model Test of Underground Gasification of Coking Coal[J]. COAL SCIENCE AND TECHNOLOGY, 2013, (5). |
| [9] | Study on Coal Oxidized Dynamics Test with Low Temperature Based on CO Density[J]. COAL SCIENCE AND TECHNOLOGY, 2012, (3). |
| [10] | Study on Permeability Comparison Tests with Two Different Gas Content Coal Samples[J]. COAL SCIENCE AND TECHNOLOGY, 2011, (8). |
| 1. |
徐乔木，赵星杰，卜侃侃. 旋转多斗装车系统受限空间内复杂粒度散料堆积特性研究. 煤. 2025(04): 86-91 .
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: