Bibliographic Details
| Title: |
基于双路径动态融合的液压支架压力时序预测模型. |
| Alternate Title: |
Dual-path dynamic fusion framework for hydraulic support pressure time Series prediction. |
| Authors: |
张帆1,2 zf@cumtb.edu.cn, 裴澳航1, 闫帅帅1, 吴泓瑾1, 李欣雨1, 郑睿涵1 |
| Source: |
Coal Science & Technology (0253-2336). Jun2026, Vol. 54 Issue 6, p333-347. 15p. |
| Subject Terms: |
*Forecasting, *Patterns (Mathematics) |
| Abstract (English): |
The hydraulic support working resistance sequence in coal mine working faces during operations such as shifting, elevating, and conveyor pushing exhibits pronounced characteristics of strong non-stationarity, periodic disturbances, and complex multi-scale patterns, posing significant challenges for dynamic modeling and real-time pressure prediction. To address the modeling difficulties arising from long-term trend non-stationarity, short-term fluctuations, and abrupt changes in hydraulic support pressure time series, this study proposes a dual-path feature extraction and dynamic fusion prediction model. The model consists of a long-term trend modeling path based on an Informer encoder and a local path leveraging a multi-head differential attention-enhanced temporal convolutional network (MHDA-TCN). The Informer encoder introduces a probabilistic sparse attention mechanism to effectively reduce the computational complexity of self-attention and incorporates a self-attention distillation mechanism to capture long-sequence trend features. Meanwhile, the MHDA-TCN branch enhances sensitivity to high-frequency disturbances and abrupt structures via multi-head differential attention, combined with temporal convolutional layers to extract multi-scale local features. The features from both paths are adaptively integrated through a dynamic fusion module, balancing long-term trends with short-term perturbations to generate contextual representations for joint modeling. A parallel decoding strategy is adopted to predict multiple time steps simultaneously, significantly improving multi-step forecasting efficiency and long-sequence modeling capability. Experimental results demonstrate that, with a fixed window length of 128, the proposed model achieves optimal performance across multi-step prediction tasks. Specifically, for 20-step forecasting, the ERMS and EMA are reduced to 1.842 MPa and 1.362 MPa, while EMAP and ESMAP are kept below 8%, indicating robust long-term predictive accuracy. Comparative experiments further show that the proposed approach consistently outperforms baseline models such as Informer, Reformer, GRU-Informer, and XLSTM-Informer in short-, mid-, and long-term prediction tasks. In particular, for long-term forecasting, ERMS, EMAP, and ESMAP decrease by 37.4%, 33.4%, and 32.3%, respectively, compared to the second-best model (XLSTM-Informer). These results demonstrate the proposed method's strong real-time capability and robustness in modeling complex multi-scale characteristics of hydraulic support pressure sequences, providing technical support for intelligent roof pressure monitoring and early warning in coal mine working faces. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
矿井工作面液压支架在移架−升架−推移刮板输送机等过程中工作阻力时序具有强非平稳性、 周期性扰动和复杂多尺度特征等显著特性, 因此对矿井液压支架动态建模及其压力实时预测带来巨 大挑战。针对矿井液压支架压力时序数据存在长期趋势非平稳性、短期扰动与突变特性的建模难题, 提出双路径特征提取动态融合的液压支架压力时序预测模型。该模型由基于 Informer 编码器的长期 趋势建模路径和差分注意力增强的时序卷积网络 (MHDA-TCN) 局部路径协同组成。Informer 编码器 引入概率稀疏注意力机制, 有效降低自注意力计算复杂度, 并结合自注意力蒸馏机制提取长序列趋 势特征; MHDA-TCN 分支通过多头差分注意力机制强化对高频扰动和突变结构的感知能力, 并结合 时序卷积网络提取多尺度局部特征。双路径特征经动态融合模块自适应加权整合, 平衡长期趋势与 短期扰动信息, 生成上下文特征表示, 据此实现长期趋势与短期扰动特征的联合建模。解码器采用 并行生成策略, 同时预测多时间步压力序列, 显著提升多步预测效率与长期序列建模能力。结果表 明: 将窗口长度固定为 128 时, 模型在多步预测任务中均取得最佳性能。其中, 在 20 步预测任务中, 均方根误差 ERMS、平均绝对误差 EMA 分别降至 1.842 MPa 和 1.362 MPa, 平均绝对百分比误差 EMAP 和对称平均绝对百分比误差 ESMAP 均控制在 8% 以内, 表明模型能够在长期预测中保持较高精度。横 向对比结果显示, 该方法在短期、中期及长期预测任务中均优于 Informer、Reformer、GRU-Informer 和 XLSTM-Informer 等基线模型; 尤其在长期预测任务中, 相较于次优模型 XLSTM-Informer, ERMS、EMAP、ESMAP 分别降低 37.4% 、33.4% 、32.3%。所提方法在液压支架压力序列复杂多尺度特征 建模中具有较强的实时性与鲁棒性, 为矿井工作面顶板周期来压智能监测与超前预警提供技术 支撑。 [ABSTRACT FROM AUTHOR] |
| Database: |
Energy & Power Source |
