IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i3p2239-d1046599.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/3/2239/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/3/2239/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin He & Dongliang Yuan & Lianwei Ren & Ming Huang & Wenyu Zhang & Jie Tan, 2023. "Evaluation Model Research of Coal Mine Intelligent Construction Based on FDEMATEL-ANP," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    2. Olga Zhironkina & Sergey Zhironkin, 2023. "Technological and Intellectual Transition to Mining 4.0: A Review," Energies, MDPI, vol. 16(3), pages 1-37, February.
    3. Jicheng Wan & Xuhui Zhang & Chao Zhang & Wenjuan Yang & Mengyu Lei & Yuyang Du & Zheng Dong, 2023. "Vision and Inertial Navigation Combined-Based Pose Measurement Method of Cantilever Roadheader," Sustainability, MDPI, vol. 15(5), pages 1-16, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2239-:d:1046599. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.