煤矿安全2025，Vol.56Issue(3)：242-249,8.DOI:10.13347/j.cnki.mkaq.20240658
煤矿井下钻进工况智能识别算法研究与应用
Research and application of intelligent identification algorithm for underground drilling accidents in coal mines
摘要
Abstract
An analysis was conducted on the identification methods of underground drilling conditions in coal mines from three as-pects:parameter collection,data processing,and abnormal condition recognition.A framework for identifying common underground drilling conditions in coal mines was proposed,consisting of a data collection layer,a processing layer,and a condition recognition layer.Among them,the data acquisition layer can collect drilling parameters;the data processing layer includes data cleaning of out-lier points,extraction of feature parameters,and fusion of multi-source information from sensors;the working condition recognition layer adopts classification algorithms and optimization algorithms in machine learning,and combines two or more recognition al-gorithms to form a hybrid intelligent working condition recognition algorithm.It learns data and trains models for drilling paramet-ers with working condition classification labels,ultimately achieves intelligent recognition of drilling working conditions.Based on typical drilling parameters such as torque,pump pressure,and drilling speed in a coal mine in Huainan,Anhui Province,a nuclear extreme learning machine(KELM)recognition model optimized using the whale algorithm(WOA)was constructed to identify typic-al working conditions.By learning from the training set samples,the WOA-KELM model with higher recognition accuracy than SVM,KNN,and other recognition models was adopted to achieve intelligent recognition of typical working conditions.关键词
井下钻探/钻进工况/钻进参数/工况智能识别算法/机器学习Key words
underground drilling/drilling condition/drilling parameter/intelligent condition identification algorithm/machine learning分类
矿业与冶金引用本文复制引用
陈韬,张幼振,许超..煤矿井下钻进工况智能识别算法研究与应用[J].煤矿安全,2025,56(3):242-249,8.基金项目
陕西省自然科学基础研究重点资助项目(2024JC-ZDXM-30) （2024JC-ZDXM-30）
天地科技股份有限公司科技创新重点资助项目(2023-2-TD-ZD002) （2023-2-TD-ZD002）
