Coal wall deformation monitoring and concentrated load inversion technology based on intelligent support devices
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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0.460 6，0.248 7，0.973 2；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。
Abstract: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 0.460 6, 0.248 7, and 0.973 2, 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.
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图 1 FBG受拉伸前后波长特征
Figure 1. Wavelength characteristics of Fiber Bragg Grating (FBG) before and after tension
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图 8 PSO−RBF神经网络模型构建流程
Figure 8. Construction process of Particle Swarm Optimization（PSO）-Radial Basis Function（RBF ）
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图 10 智能护帮装置变形监测与载荷反演实验平台
Figure 10. Experimental platform for deformation monitoring and load inversion of intelligent face guard
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图 11 FBG传感器位置及光栅轴线方向
Figure 11. FBG sensor positions and direction of the grating axis
表 1 PSO优化算法参数
Table 1 Parameters of PSO optimization algorithm
算法参数 值 备注 dim 1 优化参数个数 lb 0.01 优化参数目标下限 ub 1000 优化参数目标上限 pop 10 种群数量 Max_iteration 45 最大迭代次数 
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[1] 王家臣. 厚煤层开采理论与技术[M]. 北京:冶金工业出版社,2009. WANG Jiachen. Theory and technology of thick coal seam mining[M]. Beijing:Metallurgical Industry Press,2009.
[2] 许永祥,王国法,李明忠,等. 超大采高综放工作面板裂化片帮特征及合理护帮控制[J]. 煤炭学报,2021,46(2):357-369. XU Yongxiang,WANG Guofa,LI Mingzhong,et al. Investigation on coal face slabbed spalling features and reasonable control at the longwall face with super large cutting height and longwall top coal caving method[J]. Journal of China Coal Society,2021,46(2):357-369.
[3] 王鹤理,李金刚. 大采高智能化综采工作面关键技术研究[J]. 内蒙古煤炭经济,2019(23):39,41. WANG Heli,LI Jingang. Research on key technologies of intelligent fully mechanized mining face with large mining height[J]. Inner Mongolia Coal Economy,2019(23):39,41.
[4] 雷照源,姚一龙,李磊,等. 大采高智能化工作面液压支架自动跟机控制技术研究[J]. 煤炭科学技术,2019,47(7):194-199. LEI Zhaoyuan,YAO Yilong,LI Lei,et al. Research on automatic follow-up control technology of hydraulic support in intelligent working face with large mining height[J]. Coal Science and Technology,2019,47(7):194-199.
[5] 刘俊峰,唐恩贤,许永祥,等. 大采高智能化开采煤壁全过程精准控制技术研究[J]. 煤炭科学技术,2019,47(10):131-135. LIU Junfeng,TANG Enxian,XU Yongxiang,et al. Study on full process precision control technology of coal wall under large mining height intelligent mining condition[J]. Coal Science and Technology,2019,47(10):131-135.
[6] ALZAHRANI A A M. Detection of mine roof failure using inexpensive LiDAR technology[D]. Rolla:Missouri University of Science and Technology,2017.
[7] 尤秀松,葛世荣,郭一楠,等. 智采工作面三机数字孪生驱动控制架构[J]. 煤炭学报,2024,49(7):3265-3275. YOU Xiusong,GE Shirong,GUO Yinan,et al. Digital twin-driven control construction for three machines of smart coal mining face[J]. Journal of China Coal Society,2024,49(7):3265-3275.
[8] 于远祥,廖安全. 中深埋缓倾斜梯形切眼围岩变形规律及支护技术[J]. 西安科技大学学报,2023,43(6):1137-1148. YU Yuanxiang,LIAO Anquan. Surrounding rock deformation law and support technology of moderately deep buried gently inclined trapezoidal cut[J]. Journal of Xi'an University of Science and Technology,2023,43(6):1137-1148.
[9] 杜文刚. 基于光纤感测的采动覆岩变形演化特征试验研究[D]. 西安:西安科技大学,2020. DU Wengang. Basic experimental study on deformation evolution characteristics of mining overburden based on optical fiber sensing[D]. Xi'an:Xi'an University of Science and Technology,2020.
[10] 柴敬,王佳琪,杨健锋,等. 区段煤柱变形光纤光栅监测应用研究[J]. 煤炭科学技术,2024,52(1):126-137. CHAI Jing,WANG Jiaqi,YANG Jianfeng,et al. Research on the application of fiber Bragg grating monitoring for deformation of coal pillar in sections[J]. Coal Science and Technology,2024,52(1):126-137.
[11] 宋扬. 智能工作面刮板输送机形态高精度光纤感测机制研究[D]. 徐州:中国矿业大学,2023. SONG Yang. Research on high-precision optical fiber sensing mechanism of scraper conveyor shape in intelligent working face[D]. Xuzhou:China University of Mining and Technology,2023.
[12] 张璠. 智能工作面刮板输送机运行轨迹的三维重构研究[D]. 徐州:中国矿业大学,2023. ZHANG Fan. Research on three-dimensional reconstruction of scraper conveyor running trajectory in intelligent working face[D]. Xuzhou:China University of Mining and Technology,2023.
[13] 丁自伟,巩欣伟,张杰,等. 煤层群下行开采底板应力演化规律与合理巷道错距研究[J]. 西安科技大学学报,2024,44(2):213-225. DING Ziwei,GONG Xinwei,ZHANG Jie,et al. Study on the evolution law of bottom plate stress and reasonable roadway misalignment in downstream mining of coal seam group[J]. Journal of Xi'an University of Science and Technology,2024,44(2):213-225.
[14] 宋佳佳. 基于光纤光栅的顶板离层监测系统的研究应用[D]. 邯郸:河北工程大学,2021. SONG Jiajia. Research and application of roof separation monitoring system based on fiber Bragg grating[D]. Handan:Hebei University of Engineering,2021.
[15] 夏嘉斌. 飞机起落架光纤监测信号智能解调方法与载荷重构关键技术研究[D]. 合肥:合肥工业大学,2023. XIA Jiabin. Research on intelligent demodulation methods for aircraft landing gear optical fiber monitoring signals and key technologys in load reconstruction[D]. Hefei:Hefei University of Technology,2023.
[16] 黄鹏宇,陈诗,刘元凤,等. 基于FBG的某舰载机前起落架载荷监测技术研究[J]. 激光杂志,2023,44(7):68-75. HUANG Pengyu,CHEN Shi,LIU Yuanfeng,et al. Research on FBG-based front landing gear load monitoring technology of a carrier aircraft[J]. Laser Journal,2023,44(7):68-75.
[17] 朱敏. 基于柔性机翼形变分析的分布式传递对准方法[D]. 南京:东南大学,2023. ZHU Min. Distributed transfer alignment method based on flexible wing deformation analysis[D]. Nanjing:Southeast University,2023.
[18] 李明辉. 基于FBG的风力发电机塔筒焊缝应变监测[J]. 焊接技术,2023,52(8):98-104. LI Minghui. Weld strain monitoring of wind turbine tower based on FBG[J]. Welding Technology,2023,52(8):98-104.
[19] 孙智鹏. 基于光纤光栅的风机叶片监测系统研究与设计[D]. 恩施:湖北民族大学,2023. SUN Zhipeng. Research and design of fan blade monitoring system based on fiber Bragg grating[D]. Enshi:Hubei Minzu University,2023.
[20] 刘祥和. 基于光纤光栅的输电导线拉力传感器设计及应用研究[D]. 哈尔滨:哈尔滨工程大学,2022. LIU Xianghe. Design and application of transmission line tension sensor based on fiber Bragg grating[D]. Harbin:Harbin Engineering University,2022.
[21] 梁庄. 基于光纤光栅传感的火箭形变重构研究及在姿态控制中的应用[D]. 长沙:国防科技大学,2021. LIANG Zhuang. Research on deformation reconstruction of rocket based on fiber Bragg grating sensing and its application in attitude control[D]. Changsha:National University of Defense Technology,2021.
[22] 张俊,陈光辉,倪国新,等. FBG传感技术在飞机机翼动态形变监测中的应用[J]. 仪器仪表学报,2023,44(11):252-260. ZHANG Jun,CHEN Guanghui,NI Guoxin,et al. Application of FBG sensing technology in dynamic deformation monitoring of aircraft wings[J]. Chinese Journal of Scientific Instrument,2023,44(11):252-260.
[23] HOCKER G B. Fiber-optic sensing of pressure and temperature[J]. Applied Optics,1979,18(9):1445-1448. DOI: 10.1364/AO.18.001445
[24] 梁敏富. 煤矿开采多参量光纤光栅智能感知理论及关键技术[D]. 徐州:中国矿业大学,2019. LIANG Minfu. Intelligent sensing theory and key technology of multi-parameter fiber Bragg grating in coal mining[D]. Xuzhou:China University of Mining and Technology,2019.
[25] DI S R. Fibre optic sensors for structural health monitoring of aircraft composite structures:recent advances and applications[J]. Sensors,2015,15(8):18666-18713.
[26] 吴晶,吴晗平,黄俊斌,等. 光纤光栅传感器在舰船结构健康监测中的应用[J]. 舰船科学技术,2014,36(10):50-54. WU Jing,WU Hanping,HUANG Junbin,et al. Studying the application of fiber Bragg grating sensor on ship structures health monitoring[J]. Ship Science and Technology,2014,36(10):50-54.
[27] 郭师苡,龙建辉,李旭鹏,等. 直角台阶式不规则加筋土挡墙静力荷载试验及FBG监测研究[J/OL]. 太原理工大学学报:1-11. https://link.cnki.net/urlid/14.1220.N.20240510.1043.004. GUO Shiyi,LONG Jianhui,LI Xupeng,et al. Study on static load test and FBG monitoring of right-angle stepped irregular reinforced earth retaining wall[J/OL]. Journal of Taiyuan University of Technology: 1-11. https://link.cnki.net/urlid/14.1220.N.20240510.1043.004.
[28] 吉晓朋,张志创,李克,等. 分布式光纤监测技术在基坑监测中的应用[J]. 中原工学院学报,2022,33(6):49-53. JI Xiaopeng,ZHANG Zhichuang,LI Ke,et al. Application of distributed optical fiber monitoring technology in monitoring of excavation[J]. Journal of Zhongyuan University of Technology,2022,33(6):49-53.
[29] 蔡明,王子菡,陶雷,等. 基于分布式光纤传感技术的风电叶片变形监测研究[J]. 科技创新与应用,2024,14(16):16-19. CAI Ming,WANG Zihan,TAO Lei,et al. Research on blade deformation monitoring for wind turbines based on distributed fiber optic sensing technology[J]. Technology Innovation and Application,2024,14(16):16-19.
[30] LI Dongsheng. Strain transferring analysis of fiber Bragg grating sensors[J]. Optical Engineering,2006,45(2). DOI: 10.1117/1.2173659.
[31] DERKEVORKIAN A,MASRI S F,ALVARENGA J,et al. Strain-based deformation shape-estimation algorithm for control and monitoring applications[J]. AIAA Journal,2013,51(9):2231-2240. DOI: 10.2514/1.J052215
[32] 司亚文. 基于分布式光纤传感器的变形监测与应变场重构方法研究[D]. 南京:南京航空航天大学,2019. SI Yawen. Research on deformation monitoring and strain field reconstruction based on distributed optical fiber sensor[D]. Nanjing:Nanjing University of Aeronautics and Astronautics,2019.
[33] 田欢,周平章. 弹性梁动载荷辨识方法研究[J]. 空天技术,2023(2):84-96. TIAN Huan,ZHOU Pingzhang. Research on dynamic load identification method of elastic beams[J]. Aerospace Technology,2023(2):84-96.
[34] 张赐宝,李涛,滕申科,等. 基于应变响应叠加原理的飞行载荷测量建模研究[J]. 应用力学学报,2018,35(1):22-27,224. ZHANG Cibao,LI Tao,TENG Shenke,et al. Research on modeling of flight load measurement based on strain response superposition principle[J]. Chinese Journal of Applied Mechanics,2018,35(1):22-27,224.
[35] 尚琳,何发东,杨华保,等. 基于神经网络的垂尾飞行载荷模型研究[J]. 强度与环境,2011,38(5):49-53. SHANG Lin,HE Fadong,YANG Huabao,et al. Research on flight loads model of vertical tail based on neural networks[J]. Structure & Environment Engineering,2011,38(5):49-53.
[36] WANG Dongshu,TAN Dapei,LIU Lei. Particle swarm optimization algorithm:an overview[J]. Soft Computing,2018,22(2):387-408. DOI: 10.1007/s00500-016-2474-6
[37] 刘佳玮,于明鑫,祝连庆,等. 基于深度学习的机翼蒙皮载荷计算方法[J]. 电子测量与仪器学报,2022,36(4):1-8. LIU Jiawei,YU Mingxin,ZHU Lianqing,et al. Calculation method of wing skin load based on deep learning[J]. Journal of Electronic Measurement and Instrumentation,2022,36(4):1-8.
[38] 侯乔乔,张清勇. 一种改进的载荷标定试验数据回归算法[J]. 工程与试验,2018,58(4):22-24. HOU Qiaoqiao,ZHANG Qingyong. An improved regression algorithm for load calibration test data[J]. Engineering & Test,2018,58(4):22-24.
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