融合传感器数据和人工调控信息的工作面直线度智能预测  

Intelligent prediction for face straightness based on sensor data and human operation information

作　　者：孙岩 付翔[1,2,3] 王然风 贾一帆[1] 张智星 SUN Yan;FU Xiang;WANG Ranfeng;JIA Yifan;ZHANG Zhixing(College of Mining Engineering,Taiyuan University of Technology,Taiyuan 030024,China;Post-doctoral Workstation,Shanxi Coking Coal Group Co.,Ltd.,Taiyuan 030024,China;State Key Laboratory of Intelligent Mining Equipment Technology,Taiyuan University of Technology,Taiyuan 030024,China)

机构地区：[1]太原理工大学矿业工程学院,山西太原030024 [2]山西焦煤集团有限责任公司博士后工作站,山西太原030024 [3]太原理工大学智能采矿装备技术全国重点实验室,山西太原030024

出　　处：《工矿自动化》2024年第11期84-91,共8页Journal Of Mine Automation

基　　金：国家自然科学基金项目(52274157);“科技兴蒙”行动重点专项项目(2022EEDSKJXM010)。

摘　　要：目前综采工作面直线度调控采用基于工作面推移行程等传感器数据和人工观测调控相结合的方式,但存在传感器数据和人工调控信息得不到有效利用的问题。针对上述问题,提出了融合传感器数据和人工调控信息的工作面直线度智能预测方法。对支架推移油缸行程、支架立柱压力、采煤机位置等进行数据清洗,并按照正常推溜调控距离和调整推溜调控距离进行分类,构建由工作面正常推溜调控距离矩阵和累计推溜调控距离矩阵组成的工作面直线度分析矩阵;通过特征工程对工作面直线度分析矩阵进行特征提取,生成特征矩阵作为样本,将人工调控距离对应的工况类型作为样本标签;采用机器学习分类算法建立工作面直线度预测模型。实验结果表明,采用随机森林算法搭建的工作面直线度预测模型准确率最高,为91.41%。将该模型应用于高河煤矿2312工作面,结果表明,在运行30 d、115次割煤循环的工作面直线度预测过程中,该模型预测准确率达81.4%。Currently,the control of face straightness in fully mechanized mining faces combines sensor data such as advancing stroke with manual observations.However,an issue has been identified where sensor data and human operation information are not effectively utilized.To address this problem,an intelligent prediction method for face straightness that integrates sensor data and human operation information was proposed.The support advancing cylinder stroke data,support column pressure data and shearer position data were cleaned,and classified according to the normal advancing stroke control distance and the adjusted advancing stroke control distance.A face straightness analysis matrix was constructed,consisting of the normal advancing stroke control distance matrix and the accumulated advancing stroke control distance matrix.Through feature engineering,feature extraction was carried out on the straightness analysis matrix of the working face,and the feature matrix was generated as a sample,with the working condition type corresponding to the manual control distance to serve as sample labels.The experimental results show that the accuracy of the working face straightness prediction model built by random forest algorithm is the highest,which was 91.41%.A machine learning classification algorithm was employed to establish a prediction model for the face straightness of the current mining cycle.This prediction model was applied to the 2312 working faces at the Gaohe coal mine.The results indicated that during the 30-day period and 115 cutting cycles of the face straightness prediction,achieving an accuracy rate of 81.4%.

关 键 词：工作面直线度 推移行程 传感器数据 人工调控信息 工作面直线度分析矩阵 

分 类 号：TD355[矿业工程—矿井建设]