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Advancement of the Monitoring System for Arch Support Geometry and Loads

Author

Listed:
  • Mariusz Woszczyński

    (KOMAG Institute of Mining Technology, Pszczyńska 37, 44-101 Gliwice, Poland)

  • Joanna Rogala-Rojek

    (KOMAG Institute of Mining Technology, Pszczyńska 37, 44-101 Gliwice, Poland)

  • Krzysztof Stankiewicz

    (KOMAG Institute of Mining Technology, Pszczyńska 37, 44-101 Gliwice, Poland)

Abstract

As part of the RFCS project, which aimed to improve transport safety in mines, ITG KOMAG proposed a system for monitoring loads and geometric of arch support. The system’s function is to control safety, mainly during suspended monorail runs. This paper presents a hardware model and a measurement method based on the use of vibrating wire strain gauges and draw-wire sensors. The challenge was to properly adapt the vibrating wire strain gauge operation to the requirements of the ATEX directive on the safe use of electrical equipment in underground mines. The signal transducer algorithm and potential mounting locations for the proposed sensors were discussed. The results of tests carried out using the ŁP arc support are presented, reflecting the actual behavior of the casing during loading in accordance with the test methodology proposed by the Central Mining Institute. In order to compare the results with another measurement method, film strain gauges were additionally applied. The results confirm the usefulness of the proposed method for testing in real conditions. The speed and simplicity of installation of vibrating wire strain gauges provides an advantage over the use of film strain gauges, which are very difficult to install in underground conditions.

Suggested Citation

  • Mariusz Woszczyński & Joanna Rogala-Rojek & Krzysztof Stankiewicz, 2022. "Advancement of the Monitoring System for Arch Support Geometry and Loads," Energies, MDPI, vol. 15(6), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2222-:d:774123
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    References listed on IDEAS

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    1. Volker Liermann & Sangmeng Li, 2021. "Methods of Machine Learning," Springer Books, in: Volker Liermann & Claus Stegmann (ed.), The Digital Journey of Banking and Insurance, Volume III, pages 225-238, Springer.
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