Research on Hydraulic Support Attitude Monitoring Method Merging FBG Sensing Technology and AdaBoost Algorithm

Ningning Chen, Xinqiu Fang (), Minfu Liang, Xiaomei Xue, Fan Zhang, Gang Wu and Fukang Qiao
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Ningning Chen: School of Mines, China University of Mining and Technology, Xuzhou 221116, China
Xinqiu Fang: School of Mines, China University of Mining and Technology, Xuzhou 221116, China
Minfu Liang: School of Mines, China University of Mining and Technology, Xuzhou 221116, China
Xiaomei Xue: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
Fan Zhang: School of Mines, China University of Mining and Technology, Xuzhou 221116, China
Gang Wu: School of Mines, China University of Mining and Technology, Xuzhou 221116, China
Fukang Qiao: School of Mines, China University of Mining and Technology, Xuzhou 221116, China

Sustainability, 2023, vol. 15, issue 3, 1-17

Abstract: 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/(°). 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%.

Keywords: fiber Bragg grating (FBG); AdaBoost neural network algorithm; hydraulic support; attitude monitoring; intelligent monitoring (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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