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

Author

Listed:
  • Ningning Chen

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    Research Center of Intelligent Mining, China University of Mining and Technology, Xuzhou 221116, China)

  • Xinqiu Fang

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    Research Center of Intelligent Mining, China University of Mining and Technology, Xuzhou 221116, China)

  • Minfu Liang

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    Research Center of Intelligent Mining, 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
    Research Center of Intelligent Mining, China University of Mining and Technology, Xuzhou 221116, China)

  • Gang Wu

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    Research Center of Intelligent Mining, China University of Mining and Technology, Xuzhou 221116, China)

  • Fukang Qiao

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    Research Center of Intelligent Mining, China University of Mining and Technology, Xuzhou 221116, China)

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%.

Suggested Citation

  • Ningning Chen & Xinqiu Fang & Minfu Liang & Xiaomei Xue & Fan Zhang & Gang Wu & Fukang Qiao, 2023. "Research on Hydraulic Support Attitude Monitoring Method Merging FBG Sensing Technology and AdaBoost Algorithm," Sustainability, MDPI, vol. 15(3), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2239-:d:1046599
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    References listed on IDEAS

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    1. Xiaofang Wo & Guichen Li & Yuantian Sun & Jinghua Li & Sen Yang & Haoran Hao, 2022. "The Changing Tendency and Association Analysis of Intelligent Coal Mines in China: A Policy Text Mining Study," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
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    Cited by:

    1. Longlong He & Lianwei Sun & Yue Wu & Zidi Zhao & Zhaoqiang Yuan & Haoqian Cai & Jiale Li & Xiangang Cao & Xuhui Zhang, 2025. "Research on Dynamic Spatial Pose and Load of Hydraulic Support Under Inclined–Declined and Large-Dip-Angle Working Conditions for Product Design," Mathematics, MDPI, vol. 13(18), pages 1-27, September.

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