Infrared precursor of pre-cracked coal failure based on critical slowing down

Hao, Tianxuan; Li, Fan; Tang, Yiju; Zhao, Lizhen; Wang, Zehua

doi:10.1080/19475705.2022.2098068

Cited by 11 publications

(3 citation statements)

References 18 publications

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“…During the disturbance process, the parameter's recovery rate is denoted as λ. A regression model can be formulated to approximate the relationship, which is expressed as follows [39,40]:…”

Section: Ae Multi-parameter Precursor Responsementioning

confidence: 99%

Mechanical Behaviors and Precursory Characteristics of Coal-Burst in Deep Coal Mining for Safety-Sustainable Operations: Insights from Experimental Analysis

Wang,

Zhou

et al. 2024

Sustainability

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Coalburst, a frequent and severe dynamic disaster, poses significant challenges to the safety and sustainable development of coal mines during deep excavation. To investigate the mechanical behaviors and precursory characteristics of coalburst subjected to in situ stress conditions, multiaxial cyclic loading experiments were conducted on cubic coal specimens, and the effects of different confining pressures on the mechanical parameters and energy evolution were analyzed. Acoustic emission (AE) technology was utilized to study the accumulation process of stress-induced damage and identify the source modes of microcracks. Then, nonlinear fractal theory and critical slowing theory were used to investigate the time-varying precursory characteristics of catastrophic failure in coalburst. The results show that as the confining pressure increases, the coal samples exhibit higher levels of elastic strain energy and dissipative energy, indicating an enhancement of plasticity. The AE count and accumulated energy show a strong correlation with cyclic loads. With an increasing number of cycles, the AE Felicity ratio gradually decreases, indicating a progressive increase in irreversible damage. Shear-mode microcracks also become more prominent with applied stress and confining pressures, as supported by varying AF/RA values of AE signals. The AE signals also follow the Hurst statistical law, and increasing applied stress and confining pressure strengthen this statistical pattern with a higher Hurst index. Throughout the cyclic loading process, certain AE varying trends were observed: the autocorrelation coefficient increased, the fractal dimension gradually decreased, and the variance suddenly increased. These trends serve as early, middle, and short–imminent warning signals, respectively, for the catastrophic failure of the loaded coal sample. These research findings contribute to a deeper understanding of coal failure evolution and provide a basis for early detection and warning of coalburst disasters, which are also essential for promoting the safe and sustainable development of deep coal mining operations.

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Section: Ae Multi-parameter Precursor Responsementioning

confidence: 99%

Mechanical Behaviors and Precursory Characteristics of Coal-Burst in Deep Coal Mining for Safety-Sustainable Operations: Insights from Experimental Analysis

Wang,

Zhou

et al. 2024

Sustainability

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“…The fundamental cause underlying these disasters lies in the heightened degree of internal damage within coal and rock formations as a consequence of mining activities. Research findings underscore that the process of coal rock damage involves the simultaneous release and alteration of various forms of energy, including acoustic emission and infrared radiation [ [5] , [6] , [7] , [8] ]. A crucial proposition emerges: the scientific monitoring and early warning of early-stage coal rock damage could be instrumental in effectively controlling the onset of disasters and ensuring the overall safety of production processes.…”

Section: Introductionmentioning

confidence: 99%

Method for rock fracture prediction and early warning: Insight from fusion of multi-physics field information

Gao,

Ma,

Liu

et al. 2024

Heliyon

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“…Based on the previous research, acoustic emission technology has achieved many excellent results with regard to early warning potential rupture indicators [32,33] in the monitoring and application [34] of rock [35,36], but there are few research results on the critical slowing down of coal specimens [37,38]. Therefore, the research on critical slowing down of coal is particularly important for forewarning and prevention of dynamic catastrophes in the coal mining industry.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Infrared precursor of pre-cracked coal failure based on critical slowing down

Cited by 11 publications

References 18 publications

Mechanical Behaviors and Precursory Characteristics of Coal-Burst in Deep Coal Mining for Safety-Sustainable Operations: Insights from Experimental Analysis

Mechanical Behaviors and Precursory Characteristics of Coal-Burst in Deep Coal Mining for Safety-Sustainable Operations: Insights from Experimental Analysis

Method for rock fracture prediction and early warning: Insight from fusion of multi-physics field information

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

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