Research on Hydraulic Support Attitude Monitoring Method Merging FBG Sensing Technology and AdaBoost Algorithm
Research on Hydraulic Support Attitude Monitoring Method Merging FBG Sensing Technology and AdaBoost Algorithm
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DOI：
10.3390/su15032239
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发表时间：
2023-02-01
期刊：
影响因子：
3.9
通讯作者：
Qiao, Fukang
中科院分区：
文献类型：
--
作者：
Chen, Ningning;Fang, Xinqiu;Qiao, Fukang
液压支架是采场围岩支护的关键设备，监测液压支架的姿态对工作面的支架选型、操作控制和矿压分析具有重要的指导作用。目前液压支架姿态监测技术主要有惯性测量、接触式测量和视觉测量。针对此类系统姿态感知模型不完善、感知参数不完整、决策能力差等技术缺陷，结合光纤光栅传感理论，自主研制了光纤光栅压力传感器和光纤光栅倾斜传感器。光纤光栅压力传感器的压力灵敏度为35.6 pm/MPa，光纤光栅倾斜传感器的角度灵敏度为31.3 pm/（度）。基于. NET技术和C/S架构，构建了液压支架姿态光纤光栅传感监测信息平台。该信息平台实现了液压支架姿态监测数据的实时监控、数据管理、报表管理、生产信息管理和数据查询。利用MATLAB建立了AdaBoost神经网络液压支架工作阻力预测模型。通过对液压支架工作阻力样本数据的训练，AdaBoost神经网络算法成功地预测了采煤工作面的周期性来压。预测准确率在95%以上。
The hydraulic support is the key equipment of surrounding rock support in a stope, and thus monitoring the attitude of the hydraulic support has an important guiding role in the support selection, operation control and rock pressure analysis of the working face. At present, attitude monitoring technology for hydraulic support mainly includes inertial measurement, contact measurement and visual measurement. Aiming at the technical defects of imperfect attitude perception models, incomplete perception parameters and the low decision-making ability of such systems, the fiber Bragg grating (FBG) pressure sensor and the FBG tilt sensor are developed independently by combining with FBG sensing theory. The pressure sensitivity of the FBG pressure sensor is 35.6 pm/MPa, and the angular sensitivity of the FBG tilt sensor is 31.3 pm/(degrees). Additionally, an information platform for FBG sensing monitoring for hydraulic support attitude is constructed based on. NET technology and C/S architecture. The information platform realizes real-time monitoring, data management, report management, production information management and data querying of hydraulic support attitude monitoring data. An AdaBoost neural network hydraulic support working resistance prediction model is established using MATLAB. The AdaBoost neural network algorithm successfully predicts the periodic pressure of the coal mining face by training with the sample data of the working resistance of the hydraulic support. The predicting accuracy is more than 95%.