An experimental study on precursor identification for gas-bearing coal failure from electric potential responses in the loading process. (15th June 2022) - Niu, Yue; Li, Zhonghui; Wang, Enyuan; Gao, Feng; Wang, Fengchao; Zhang, Zhizhen; Deng, Qi; Long, Zhaoxi; Zhang, Xin; Tian, He
Graphical abstract: Statistical results of multi-fractal spectra of EP data at different loading stress levels Highlights: Changes of EP intensity are basically consistent with the loading stress during the deformation and fracture process of gas-bearing coal. Before the failure of gas-bearing coal, the multi-fractal parameter Δα increases rapidly and reaches the peak, and Δ fm changes significantly, while the variance V shows more timely abnormal responses 0.3 s ahead. The combination of multi-fractal and critical slowing down parameters can identify the identify the precursor information for coal's failure with better Interference suppression. Abstract: The occurrence of coal and rock dynamic disasters threatens and restricts the safe production of underground coal mining activities seriously. For the prevention and control of the dynamic disasters, it is the foundation and premise to monitor the damage evolution and forecast the failure of coal mass under joint action of loading stress and gas pressure. For this, the stress loading and electric potential (EP) measuring experimental system of gas-bearing coal specimen was established, to test and analyze the precursor information for the failure of gas-bearing coal from EP response. The results show that the EP intensity of gas-bearing coal is positively correlated with loading stress level. The EP signals show step-like changes once the specimen is severely damaged at local moment. Based on mathematical statistics, the multifractal and critical slowing down paratemers of temporal EP data were analyzed, to reveal the nonlinear characteristics of gas-bearing coal during the loading and damaging process. In the critical failure stage, the multifractal parameter Δ α rapidly increases to the peak and another parameter Δ fm changes significantly to a negative value. Furthermore, the variance ( V ), as a critical slowing down parameter, rapidly rises to the peak value very shortly (0.3 s) before the failure occurs. Simultaneously, the autocorrelation coefficient ( ac ) also exhibits abnormal responses. Analysis combining the above indexes enables comprehensive monitoring and identification of precursor information for the failure of gas-bearing coal from temporal EP data.