Precursor Signal Identification and Acoustic Emission Characteristics of Coal Fracture Process Subjected to Uniaxial Loading

In deep underground mine engineering, the critical warning signals before the sudden failure of coal are crucial to predict coal or rock dynamic catastrophes and to help the coal industry grow sustainably. Therefore, with the objective of accurately identifying the precursor signals of coal fracture, a uniaxial compression test was adopted. Tests were performed on multiple sets of raw coal samples, and acoustic emission (AE) technology was used to capture the deformation and destruction courses of the coal samples. Furthermore, the signal intensity of AE energy was discussed. Based on the critical slowing down theory, the AE energy sequence was processed. The results indicate that there are significant discrepancies in the strength of coal affected by initial pore fissures. During the whole loading process, the AE energy signals showed obvious stage characteristics, and there was a high risk of rapid coal energy storage during the unstable rupture development (URD) stage, which predicted the imminent destruction of the coal. The variance mutation point that was not affected by the lag step selection was easier to identify than that of the autocorrelation coefficient, and the precursor points were all in the URD stage, which is more accurate than using the AE cumulative energy curve slope.

Automated summary

In deep underground mine engineering, accurately identifying precursor signals of coal fracture is crucial. A uniaxial compression test was conducted to capture the deformation and destruction courses of coal samples using acoustic emission (AE) technology. The results show significant discrepancies in coal strength affected by initial pore fissures. During the loading process, AE energy signals exhibited stage characteristics, with a high risk of rapid coal energy storage during the unstable rupture development (URD) stage, predicting coal destruction. The variance mutation point was easier to identify than the autocorrelation coefficient, and all precursor points were in the URD stage, providing more accurate predictions than the AE cumulative energy curve slope.