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基于LSTM−Informer模型的液压支架压力时空多步长预测

余琼芳 杨鹏飞 唐高峰

余琼芳,杨鹏飞,唐高峰. 基于LSTM−Informer模型的液压支架压力时空多步长预测[J]. 工矿自动化,2024,50(6):30-35.  doi: 10.13272/j.issn.1671-251x.2023120009
引用本文: 余琼芳,杨鹏飞,唐高峰. 基于LSTM−Informer模型的液压支架压力时空多步长预测[J]. 工矿自动化,2024,50(6):30-35.  doi: 10.13272/j.issn.1671-251x.2023120009
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

基于LSTM−Informer模型的液压支架压力时空多步长预测

doi: 10.13272/j.issn.1671-251x.2023120009
基金项目: 国家自然科学基金资助项目(61601172);中国博士后科学基金资助项目(2018M641287)。
详细信息
    作者简介:

    余琼芳(1978—),女,湖北武汉人,副教授,硕士研究生导师,博士,研究方向为检测技术与自动化、智能检测与控制、深度学习,E-mail:yuqf@hpu.edu.cn

    通讯作者:

    杨鹏飞(1998—),男,河南南阳人,硕士研究生,研究方向为检测技术与自动化、大数据分析、深度学习,E-mail: 18438988486@163.com

  • 中图分类号: TD323

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

  • 摘要: 目前多步液压支架压力预测大多为单步液压支架压力的累计预测,单步累计次数越多,累计误差就越大,影响预测精度。针对该问题,提出了一种基于长短时记忆(LSTM)−Informer模型的液压支架压力时空多步长预测方法。采用卡尔曼滤波消除液压支架压力数据中的振动噪声后,在工作面端部和中部各选取相邻的5台液压支架压力数据建立2个时空数据集(数据集1和数据集2),并对时空数据进行标准化预处理。将时空数据输入LSTM模型提取时空特征,并将提取的时空特征输入Informer模型的编码器,经过位置编码后利用多头概率稀疏自注意力来关注压力序列的变化特征,经过最大池化和一维卷积消除最终输出特征图的冗余组合。利用多头概率稀疏自注意力来关注压力序列的变化特征,将Informer模型的解码器改为全连接层,得到液压支架压力的预测结果。实验结果表明:与基于门控循环单元(GRU)、LSTM和Informer模型的预测方法相比, 基于LSTM−Informer模型的预测方法在预测6,12,24步长液压支架压力时的均方根误差(RMSE)和平均绝对误差(MAE)均最小;其中基于数据集1预测的6步长液压支架压力的RMSE分别降低了41.63%,49.74%,11.85%,MAE分别降低了41.75%,50.00%,12.00%;基于数据集2预测的6步长液压支架压力的RMSE分别降低了48.15%,59.86%,19.88%,MAE分别降低了49.87%,54.90%,13.16%。

     

  • 图  1  液压支架压力数据经过卡尔曼滤波的前后对比

    Figure  1.  Comparison of pressure data of hydraulic support before and after Kalman filtering

    图  2  Informer模型结构

    Figure  2.  Informer model structure

    图  3  LSTM−Informer模型结构

    Figure  3.  LSTM-Informer model structure

    图  4  基于LSTM−Informer模型的液压支架压力预测流程

    Figure  4.  Hydraulic support pressure prediction process based on the LSTM-Informer model

    表  1  基于数据集1的预测结果对比

    Table  1.   Comparison of the prediction results based on dataset 1

    模型 RMSE MAE
    6步长 12步长 24步长 6步长 12步长 24步长
    GRU 0.663 0.932 1.220 0.491 0.683 0.935
    LSTM 0.770 0.987 1.232 0.572 0.735 0.943
    Informer 0.439 0.758 1.117 0.325 0.559 0.845
    LSTM−Informer 0.387 0.599 0.897 0.286 0.451 0.710
    下载: 导出CSV

    表  2  基于数据集2的预测结果对比

    Table  2.   Comparison of prediction results based on dataset 2

    模型 RMSE MAE
    6步长 12步长 24步长 6步长 12步长 24步长
    GRU 0.513 0.671 1.039 0.395 0.518 0.795
    LSTM 0.563 0.697 1.065 0.439 0.541 0.814
    Informer 0.332 0.582 0.840 0.228 0.405 0.625
    LSTM−Informer 0.266 0.457 0.737 0.198 0.353 0.572
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-12-04
  • 修回日期:  2024-05-25
  • 网络出版日期:  2024-06-24

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