基于智能护帮装置的煤壁变形监测和集中载荷反演技术  

Coal wall deformation monitoring and concentrated load inversion technology based on intelligent support devices

作　　者：于翔[1,2,3,4] 王国法 王家臣 李良晖[1,3,4] 吴昊阳 王雨兵 YU Xiang;WANG Guofa;WANG Jiachen;LI Lianghui;WU Haoyang;WANG Yubing(School of Energy and Mining Engineering,China University of Mining and Technology-Beijing,Beijing 100083,China;CCTEG Coal Mining Research Institute,Beijing 100013,China;Coal Industry Engineering Research Center of Top-coal Caving Mining,Beijing 100083,China;Engineering Research Center of Green and Intelligent Mining for Thick Coal Seam,Ministry of Education,Beijing 100083,China;China Coal Technology and Engineering Group,Beijing 100013,China)

机构地区：[1]中国矿业大学(北京)能源与矿业学院,北京100083 [2]中煤科工开采研究院有限公司,北京100013 [3]放顶煤开采煤炭行业工程研究中心,北京100083 [4]厚煤层绿色智能开采教育部工程研究中心,北京100083 [5]中国煤炭科工集团有限公司,北京100013

出　　处：《工矿自动化》2025年第2期9-18,26,共11页Journal Of Mine Automation

基　　金：国家重点研发计划项目(2023YFC2907505,2023YFC2907503,2023YFC2907501);国家自然科学基金资助项目(52121003,51934008,52374106);中央高校基本科研业务费资助项目(2023YQTD02)。

摘　　要：煤壁片帮是制约煤炭安全高效开发的主要因素,精准监测煤壁支护状态是提高煤壁稳定性、保证回采安全的基础。针对传统煤壁变形监测技术精度低、范围小、实时性差等问题,开发了一种基于光纤布拉格光栅(FBG)的智能护帮装置,创新性地将FBG嵌入液压支架护帮板中,构建了基于粒子群优化(PSO)−径向基函数(RBF)神经网络的煤壁集中载荷反演模型,以监测煤壁支护状态。针对复杂工况下变截面结构件受力情况理论计算的局限,基于分布式光栅应变数据,通过PSO−RBF神经网络模型建立微观力学参数与宏观力学参数的对应关系,实现了对煤壁护帮板集中载荷位置及大小的高精度反演。实验结果表明:该模型在多点标定实验样本上的预测误差较低,拟合优度较高;水平、竖直方向位置坐标及载荷大小在训练集上的平均绝对误差分别为0.4606,0.2487,0.9732;相比于载荷大小预测,模型对集中载荷位置预测的误差更小。研究成果为煤壁支护状态监测与回采安全保障提供了重要的理论与技术基础,也为液压支架其他结构件所受围岩载荷的精准测量提供了一种可靠方法。Coal wall spalling is a major factor restricting the safe and efficient development of coal mining.Accurate monitoring of coal wall support conditions is essential to improving wall stability and ensuring safe mining.To address the issue of low precision,limited range,and poor real-time performance in traditional coal wall deformation monitoring technologies,an intelligent support device based on Fiber Bragg Grating(FBG)was developed.The FBG was innovatively embedded into the hydraulic support shield plate,and a concentrated load inversion model for the coal wall based on Particle Swarm Optimization(PSO)-Radial Basis Function(RBF)neural networks was established to monitor the support conditions of the coal wall.To overcome the limitations of theoretical calculations for stress conditions of structural components with varying cross-sections under complex working conditions,a corresponding relationship between micro-mechanical and macro-mechanical parameters was established using distributed grating strain data based on the PSO-RBF neural network model.This enabled high-precision inversion of the position and magnitude of concentrated load on the support shield plate of the coal wall.Experimental results showed that the model achieved low prediction errors and high goodness of fit in multi-point calibration samples.The average absolute errors of position coordinates and load magnitude in the training set were 0.4606,0.2487,and 0.9732,respectively.Compared to the load magnitude prediction,the model exhibited smaller errors in predicting the position of concentrated loads.The research findings provide an important theoretical and technical foundation for monitoring coal wall support conditions and ensuring safe mining,as well as a reliable method for accurate measurement of surrounding rock loads on other structural components of hydraulic supports.

关 键 词：煤壁变形监测 智能护帮装置 液压支架护帮板 集中载荷反演 光纤布拉格光栅 PSO−RBF神经网络 

分 类 号：TD355[矿业工程—矿井建设]