工矿自动化2026，Vol.52Issue(5)：13-22,127,11.DOI:10.13272/j.issn.1671-251x.2026030013
综采工作面滞后距演化特性分析与混合深度学习预测模型
Evolution characteristics of lag distance in fully mechanized mining face and hybrid deep learning prediction model
摘要
Abstract
The cooperative operation state of the shearer and hydraulic supports in a fully mechanized mining face directly affects roof support safety and production continuity.Mismatch between shearer advancement and hydraulic support follow-up movement can expand the unsupported roof area and increase the risks of roof instability and support failure.To address problems such as abnormal increase of support-moving lag distance and difficulty in timely and accurate identification of lag distance categories when the shearer advancement rhythm does not match the hydraulic support follow-up movement rhythm during field operation,this paper took a fully mechanized mining face in a medium-thick coal seam in Inner Mongolia as the engineering background.The distribution characteristics of lag distance and its correlation with support pressure and shearer traction speed were analyzed,and lag distance was divided into three categories:small,normal,and large lag distance.A TCN-BiLSTM-Attention hybrid deep learning model was proposed.The TCN branch extracted local features from multidimensional time-series data and captured short-term fluctuation characteristics of support pressure.The BiLSTM branch mined long-term dependencies in time-series data and captured dynamic variation trends of lag distance under operating conditions.The Attention branch learned importance weights of feature dimensions and highlighted influence of key features.Through multi-branch feature fusion,the model realized zone prediction of different lag distance categories.Experimental results showed that overall prediction accuracy of the TCN-BiLSTM-Attention model reached 87.13％,and recall for high-risk large-lag-distance conditions reached 81.58％,outperforming similar models.Field application results showed that under model-guided control,the proportion of large lag distance occurrences and passive shutdown frequency decreased by 59.96％and 80.00％,respectively,providing effective support for adaptive cooperative control of shearer and hydraulic supports and reduction of roof support risk.关键词
综采工作面/采煤机-液压支架协同/跟机移架/移架滞后距离/滞后距预测Key words
fully mechanized mining face/cooperative operation of shearer and hydraulic supports/shearer-following support/support-moving lag distance/lag distance prediction分类
矿业与冶金引用本文复制引用
牛鹏昊,付翔,邢轲轲,贾一帆,李浩杰,杨宇琪,张磊磊,马志涛..综采工作面滞后距演化特性分析与混合深度学习预测模型[J].工矿自动化,2026,52(5):13-22,127,11.基金项目
国家自然科学基金项目(52274157,52574199) （52274157,52574199）
山西省基础研究计划联合资助项目(202403011241002). （202403011241002）
