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

Xiangguo Kong, Mengzhao Zhan (), Yuchu Cai, Pengfei Ji, Di He, Tianshuo Zhao, Jie Hu and Xi Lin
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Xiangguo Kong: College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Mengzhao Zhan: College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Yuchu Cai: College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Pengfei Ji: College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Di He: College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Tianshuo Zhao: College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Jie Hu: College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Xi Lin: College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China

Sustainability, 2023, vol. 15, issue 15, 1-17

Abstract: 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.

Keywords: uniaxial compression; acoustic emission; critical slowing down; precursor signals (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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