Analysis of Similarities and Differences between Acoustic Emission and Charge Signal Based on Fractal Characteristics of Coal Fracture
|Médium:||article de revue|
|Publié dans:||Advances in Civil Engineering, janvier 2020, v. 2020|
Rock burst is a catastrophic dynamic disaster caused by sudden failure and instability of coal, which brings threats to deep coal mining; the AE-charge signals and the fragment distribution are related to both mechanical properties of coal and disaster early warning directly. Hence, the variation of AE and charge induction during coal failure, fractal feature of coal fragments, and their relationship should be studied in depth. In this paper, uniaxial loading test was carried out for coal with bursting tendency samples produced by blocks cored from 800 m depth in Xiaoqing coal mine of the Tiefa Coal Group in northeast China; the fractal characteristics of specimens are obtained by using the statistical fractal method. The mechanics of similarities and differences between acoustic emission and charge signal is investigated by using loading experiments and theoretical analysis. It is found that the fragments of coal have good self-similarity properties; the fractal dimension of the specimens is in the range 2.085–2.521, the maximum range being 2.300–2.468, which is slightly higher than that of rock. The high-amplitude pulses of the acoustic emission and charge are concentrated in the macroscopic fissure development and expansion stage but they have asynchronous characteristics between them. The charge generation process is accompanied by the inhomogeneous deformation and sliding friction; the friction slip is the major one and is analysed theoretically. A theoretical model for the force-electric coupling relationship is established. The statistical results show that both the acoustic emission and the charge signal accumulation have a significantly proportional relationship with the fractal dimension. Both the acoustic emission and charge signal reveal coal breakage evolution process, which will help in obtaining the precursor information on coal failure. Furthermore, the monitoring results can predict the extent of coal mass instability.
|Copyright:||© Xin Ding et al.|
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