Volume 50 Issue 6
Jun.  2024
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YU Qiongfang, YANG Pengfei, TANG Gaofeng. Spatiotemporal multi-step prediction of hydraulic support pressure based on LSTM-Informer model[J]. Journal of Mine Automation,2024,50(6):30-35.  doi: 10.13272/j.issn.1671-251x.2023120009
Citation: YU Qiongfang, YANG Pengfei, TANG Gaofeng. Spatiotemporal multi-step prediction of hydraulic support pressure based on LSTM-Informer model[J]. Journal of Mine Automation,2024,50(6):30-35.  doi: 10.13272/j.issn.1671-251x.2023120009

Spatiotemporal multi-step prediction of hydraulic support pressure based on LSTM-Informer model

doi: 10.13272/j.issn.1671-251x.2023120009
  • Received Date: 2023-12-04
  • Rev Recd Date: 2024-05-25
  • Available Online: 2024-06-24
  • Currently, most multi-step hydraulic support pressure predictions are cumulative predictions of single step hydraulic support pressure. The more times a single step accumulates, the greater the cumulative error, which affects the prediction precision. In order to solve the above problems, a spatiotemporal multi-step prediction method of hydraulic support pressure based on long short term memory (LSTM)-Informer model is proposed. After using Kalman filtering to eliminate vibration noise in hydraulic support pressure data, two spatiotemporal datasets (Dataset 1 and Dataset 2) are established by selecting 5 adjacent hydraulic support pressure data at the end and middle of the working face. The spatiotemporal data is standardized and preprocessed. The method inputs spatiotemporal data into the LSTM model to extract spatiotemporal features, and inputs the extracted spatiotemporal features into the encoder of the Informer model. After position encoding, the method outputs multi head probability sparse self attention to focus on the changing features of the pressure sequence. After maximum pooling and one-dimensional convolution, the method eliminates the redundant combination of output feature map. By utilizing multi head probability sparse self attention to further focus on pressure sequence features, the decoder of the Informer model is changed to a fully connected layer to obtain the prediction results of hydraulic support pressure. The experimental results show that compared with prediction methods based on gated recurrent unit (GRU), LSTM, and Informer models, prediction methods based on LSTM-Informer model has the smallest root mean square error (RMSE) and mean absolute error (MAE) in predicting hydraulic support pressure at 6, 12, and 24 step sizes. The RMSE of the 6-step hydraulic support pressure predicted based on dataset 1 decreases by 41.63%, 49.74%, and 11.85%, and the MAE decreases by 41.75%, 50.00%, and 12.00%, respectively. The RMSE of the 6-step hydraulic support pressure predicted based on dataset 2 decreases by 48.15%, 59.86%, and 19.88%, and MAE decreases by 49.87%, 54.90%, and 13.16%, respectively.

     

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  • [1]
    WANG Tong,WANG Qingwei,SHAO Longyi,et al. Current status of the research on coal geology in China[J]. Acta Geologica Sinica(English Edition),2016,90(4):1284-1297. doi: 10.1111/1755-6724.12770
    [2]
    LIU Yue. Research on the operating mechanism of China's coal market[J]. Agro Food Industry Hi Tech,2017,28(1):3075-3077.
    [3]
    LIN Jiang,FRIDLEY D,LU Hongyou,et al. Has coal use peaked in China:near-term trends in China's coal consumption[J]. Energy Policy,2018,123:208-214. doi: 10.1016/j.enpol.2018.08.058
    [4]
    TANG Xu,JIN Yi,MCLELLAN B C,et al. China's coal consumption declining-impermanent or permanent?[J]. Resources,Conservation & Recycling,2018,129:307-313.
    [5]
    ZHANG Yujiang,FENG Guorui,ZHANG Min,et al. Residual coal exploitation and its impact on sustainable development of the coal industry in China[J]. Energy Policy,2016,96:534-541. doi: 10.1016/j.enpol.2016.06.033
    [6]
    张吉雄,张强,巨峰,等. 深部煤炭资源采选充绿色化开采理论与技术[J]. 煤炭学报,2018,43(2):377-389.

    ZAHNG Jixiong,ZHANG Qiang,JU Feng,et al. Theory and technique of greening mining integrating mining,separating and backfilling in deep coal resources[J]. Journal of China Coal Society,2018,43(2):377-389.
    [7]
    王世斌,侯恩科,王双明,等. 煤炭安全智能开采地质保障系统软件开发与应用[J]. 煤炭科学技术,2022,50(7):13-24.

    WANG Shibin,HOU Enke,WANG Shuangming,et al. Development and application of geological guarantee system software for safe and intelligent coal mining[J]. Coal Science and Technology,2022,50(7):13-24.
    [8]
    程建远,朱梦博,王云宏,等. 煤炭智能精准开采工作面地质模型梯级构建及其关键技术[J]. 煤炭学报,2019,44(8):2285-2295.

    CHENG Jianyuan,ZHU Mengbo,WANG Yunhong,et al. Cascade construction of geological model of longwall panel for intelligent precision coal mining and its key technology[J]. Journal of China Coal Society,2019,44(8):2285-2295.
    [9]
    YANG Li,BIRHANE G E,ZHU Junqi,et al. Mining employees safety and the application of information technology in coal mining:review[J]. Frontiers in Public Health,2021,9. DOI: 10.3389/FPUBH.2021.709987.
    [10]
    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.
    [11]
    李泽萌. 基于LSTM的采煤工作面矿压预测方法研究[D]. 西安:西安科技大学,2020.

    LI Zemeng. Research on the prediction method of mining pressure in coal mining face based on LSTM[D]. Xi'an:Xi'an University of Science and Technology,2020.
    [12]
    曾庆田,吕珍珍,石永奎,等. 基于Prophet+LSTM模型的煤矿井下工作面矿压预测研究[J]. 煤炭科学技术,2021,49(7):16-23.

    ZENG Qingtian,LYU Zhenzhen,SHI Yongkui,et al. Research on prediction of underground coal mining face pressure based on Prophet+LSTM model[J]. Coal Science and Technology,2021,49(7):16-23.
    [13]
    李泽西. 基于可变时序移位Transformer−LSTM的集成学习矿压预测方法[J]. 工矿自动化,2023,49(7):92-98.

    LI Zexi. Ensemble learning mine pressure prediction method based on variable time series shift Transformer-LSTM[J]. Journal of Mine Automation,2023,49(7):92-98.
    [14]
    LIU Yaping,DONG Lihong,YE Ou. Mine pressure prediction model of fully mechanized mining face based on Improved Transformer[C]. IEEE International Conference on Signal Processing,Communications and Computing,Xi'an,2022:1-5.
    [15]
    DONG Jianjun,XIE Zhengquan,JIANG Hao,et al. Multiple regression method for working face mining pressure prediction based on hydraulic support monitoring dataset[J]. Frontiers in Earth Science,2023. DOI: 10.3389/FEART.2023.1114033.
    [16]
    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.
    [17]
    潘超,李润宇,王典,等. 基于风速时空关联的多步预测方法[J]. 太阳能学报,2022,43(2):458-464.

    PAN Chao,LI Runyu,WANG Dian,et al. Multi-step wind speed prediction method based on wind speed spatial-time correlation[J]. Acta Energiae Solaris Sinica,2022,43(2):458-464.
    [18]
    JINAH K,TAEKYUNG K,JOON-GYU R,et al. Spatiotemporal graph neural network for multivariate multi-step ahead time-series forecasting of sea temperature[J]. Engineering Applications of Artificial Intelligence,2023,126. DOI: 10.1016/J.ENGAPPAI.2023.106854.
    [19]
    严恭敏,邓瑀. 传统组合导航中的实用Kalman滤波技术评述[J]. 导航定位与授时,2020,7(2):50-64.

    YAN Gongmin,DENG Yu. Review on practical Kalman filtering techniques in traditional integrated navigation system[J]. Navigation Positioning and Timing,2020,7(2):50-64.
    [20]
    ZHOU Haoyi,ZHANG Shanghang,PENG Jieqi,et al. Informer:beyond efficient transformer for long sequence time-series forecasting[C]. AAAI Conference on Artificial Intelligence,Vancouve,2021:11106-11115.
    [21]
    VASWANI A,SHAZEER N,PARMAR N,et al. Attention is all you need[C]. The 31st International Conference on Neural Information Processing Systems,Long Beach,2017:5998-6008.
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