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Coal Mine Rock Burst and Coal and Gas Outburst Perception Alarm Method Based on Visible Light Imagery

Author

Listed:
  • Jijie Cheng

    (School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Yi Liu

    (School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Xiaowei Li

    (School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China)

Abstract

To solve the current reliance of coal mine rock burst and coal and gas outburst detection on mainly manual methods and the problem wherein it is still difficult to ensure disaster warning required to meet the needs of coal mine safety production, a coal mine rock burst and coal and gas outburst perception alarm method based on visible light imagery is proposed. Real-time video images were collected by color cameras in key areas of underground coal mines; the occurrence of disasters was determined by noting when the black area of a video image increases greatly, when the average brightness is less than the set brightness threshold, and when the moving speed of an object resulting in a large increase in the black area is greater than the set speed threshold (V > 13 m/s); methane concentration characteristics were used to distinguish rock burst and coal and gas outburst accidents, and an alarm was created. A set of disaster-characteristic simulation devices was designed. A Φ315 mm white PVC pipe was used to simulate the roadway and background equipment; Φ10 mm rubber balls were used to replace crushed coal rocks; a color camera with a 2.8 mm focal length, 30 FPS, and 110° field angle was used for image acquisition. The results of our study show that the recognition effect is good, which verifies the feasibility and effectiveness of the method.

Suggested Citation

  • Jijie Cheng & Yi Liu & Xiaowei Li, 2023. "Coal Mine Rock Burst and Coal and Gas Outburst Perception Alarm Method Based on Visible Light Imagery," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13419-:d:1235043
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    References listed on IDEAS

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    1. Ji Peng & Shi Shiliang & Lu Yi & Li He & Taoreed Owolabi, 2023. "Research on Risk Identification of Coal and Gas Outburst Based on PSO-CSA," Mathematical Problems in Engineering, Hindawi, vol. 2023, pages 1-12, January.
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