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<p>To deeply understand the failure process of coal mass, a multi-parameter monitoring and early warning system including stress and strain, acoustic emission (AE), and high-speed camera was built to accurately study the failure process of coal and rock specimens in the laboratory. The test system not only used stress sensor and displacement sensor to record the stress and strain of coal and rock specimen, but also used AE and high-speed camera to capture the signal in the process of crack propagation, and the signal accuracy reached 0.5&#xa0;ms level. A large-scale automated image crack recognition program has been developed, which can not only identify crack-containing images, but also identify crack-changing images. Through crack digitalization technology, the length and area of cracks are quantitatively obtained. Through the signal analysis during the loading process, it is found that the AE signal is ahead of the coal and rock fracture, and the surface crack has the rapid propagation stage and stable propagation stage before the coal and rock fracture. By processing the image of surface crack, the rapid growth rate of cracks was obtained at about 2000&#xa0;mm/s, and the crack forewarning length was proposed as a new early warning signal for coal and rock fracture. Finally, using the AE amplitude and crack forewarning length as the early warning criteria, a multi-parameter comprehensive early warning system for rock bursts is proposed to provide guidance for the early warning of rock bursts at the coal mine site.</p>

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Experimental Study of Surface Crack Propagation Based on AE and Image Processing

  • 期刊论文

  • 发表日期：

    2026年1月6日

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  • Yankui Li,
  • Baisheng Nie,
  • Shiyue Wu,
  • Xinghua Zhang,
  • Chunwei Hui,
  • Yuxi Huang,
  • Tao Zhao,
  • Zhen Li

摘要

To deeply understand the failure process of coal mass, a multi-parameter monitoring and early warning system including stress and strain, acoustic emission (AE), and high-speed camera was built to accurately study the failure process of coal and rock specimens in the laboratory. The test system not only used stress sensor and displacement sensor to record the stress and strain of coal and rock specimen, but also used AE and high-speed camera to capture the signal in the process of crack propagation, and the signal accuracy reached 0.5 ms level. A large-scale automated image crack recognition program has been developed, which can not only identify crack-containing images, but also identify crack-changing images. Through crack digitalization technology, the length and area of cracks are quantitatively obtained. Through the signal analysis during the loading process, it is found that the AE signal is ahead of the coal and rock fracture, and the surface crack has the rapid propagation stage and stable propagation stage before the coal and rock fracture. By processing the image of surface crack, the rapid growth rate of cracks was obtained at about 2000 mm/s, and the crack forewarning length was proposed as a new early warning signal for coal and rock fracture. Finally, using the AE amplitude and crack forewarning length as the early warning criteria, a multi-parameter comprehensive early warning system for rock bursts is proposed to provide guidance for the early warning of rock bursts at the coal mine site.