采煤机自适应截割轨迹预测的设计  

Design of Adaptive Cutting Trajectory Prediction for Coal Mining Machine

作　　者：宋园园 Song Yuanyuan(Yang Coal Group Shouyang Kaiyuan Mining Co.,Ltd.,Shouyang Shanxi 045400,China)

机构地区：[1]阳煤集团寿阳开元矿业有限责任公司,山西寿阳045400

出　　处：《机械管理开发》2025年第1期263-265,共3页Mechanical Management and Development

摘　　要：为了提升煤矿采煤机截割作业质量,设计并开发出一种采煤机自适应截割轨迹预测方法。首先,对LSTM神经网络进行了简单介绍,然后以此为基础,详细分析了自适应截割轨迹预测模型的构建方法,最后,选择传统截割轨迹预测方法作为对比,通过两种方法预测结果的对比,以此判断自适应截割轨迹预测模型的应用效果。通过对比分析可以发现,采用原预测方法时,预测结果误差约为2.848%,而采用自适应截割轨迹预测模型后,预测结果误差约为2.135%,低于原预测方法预测结果的误差,表明自适应截割轨迹预测模型应用效果良好,能够显著提升截割作业的精确性,可将该模型大规模推广。In order to improve the quality of cutting operation of coal mining machine,an adaptive cutting trajectory prediction method for coal mining machine is designed and developed.Firstly,LSTM neural network is briefly introduced,and then the construction method of adaptive cutting trajectory prediction model is analyzed in detail on the basis of it.Finally,the traditional cutting trajectory prediction method is chosen as a comparison,and the prediction results of the two methods are compared to judge the application effect of the adaptive cutting trajectory prediction model.Through comparative analysis,it can be found that when the original prediction method is used,the error of prediction results is about 2.848%,and after the adaptive cutting trajectory prediction model is used,the error of prediction results is about 2.135%,which is lower than the error of the original prediction results,indicating that the adaptive cutting trajectory prediction model has a good application effect,which can significantly improve the accuracy of the cutting operation,and can be used to promote the model on a large scale.

关 键 词：采煤机 自适应截割轨迹预测模型 LSTM神经网络 

分 类 号：TD421.6[矿业工程—矿山机电] TP18[自动化与计算机技术—控制理论与控制工程]