Mudstone and shaly coarse sandstone samples of Jurassic units in northwestern China were collected to study the seepage mechanism of weakly cemented rock affected by underground mining operations. Samples were studied using seepage experiments under triaxial compression considering two processes: complete stress-strain and postpeak loading and unloading. The results show that permeability variations closely correspond to deviatoric stress-axial strain during the process of complete stress-strain. The initial permeability is 7 times its minimum, contrasting with lesser differentials of initial, peak, and residual permeability. The magnitude of permeability ranges from 10−17 to 10−19 m2, representing a stable water-resisting property, and is 1 to 2 orders lower in mudstone than that in shaly coarse sandstone, indicating that the water-resisting property of the mudstone is much better than that of the shaly coarse sandstone. Permeability is negatively correlated with the confining pressure. In response to this pressure, the permeability change in mudstone is faster than that in shaly coarse sandstone during the process of postpeak loading and unloading. Weakly cemented rock has lower permeability according to the comparison with congeneric ordinary rocks. This distinction is more remarkable in terms of the initial permeability. Analyses based on scanning electron microscope (SEM) observations and mineral composition indicate that the samples are rich in clay minerals such as montmorillonite and kaolin, whose inherent properties of hydroexpansiveness and hydrosliming can be considered the dominant factors contributing to the seepage properties of weakly cemented rock with low permeability.
By analyzing nonlinear and nonstationary vibration signals from the spindle device of the mine hoist, it is a challenge to overcome the difficulty of fault feature extraction and accurately identify the fault of rotor-bearing system. In response to this problem, this paper proposes a new approach based on variational mode decomposition (VMD), SVM, and statistical characteristics such as variance contribution rate (VCR), energy entropy (EE), and permutation entropy (PE). Comparisons have gone to evaluate the performance of rolling bearing defect by using EMD (Empirical Mode Decomposition), MEEMD (Modified Ensemble EMD), BP (Back Propagation) network, single or multiple statistical characteristics, and different motor loads. The experiment was carried out on the mechanical failure simulator of the main shaft device of the hoist, which verified the reliability and effectiveness of the method. The results show that the diagnosis method is suitable for feature extraction of bearing fault signals, with the highest diagnosis accuracy. It can provide a good practical reference for the fault diagnosis of mechanical equipment of the hoist spindle device and has certain practical value.
The performance of the rolling bearing of a spindle device is directly related to the safety and reliability of the operation of a mine hoist. To extract bearing vibration signal features effectively for fault diagnosis, a feature extraction method based on the parameter optimization of a variational mode decomposition (VMD) method and permutation entropy (PE) is proposed. In addition, a support vector machine (SVM) classifier is used to identify bearing fault types. An analogue signal is used to test the effect of noise and sampling frequency on VMD performance. Focused on the problem of the VMD method needing to determine the number of mode components K and a penalty factor α during the signal decomposition process, a genetic algorithm is used to optimize the parameter combination [K,α] with the minimum sample entropy as the indicator. By using mean squared error (MSE) and correlation coefficient, an evaluation indicator is constructed to determine the decomposition effects of the optimized VMD, centre frequency, empirical mode decomposition (EMD) and ensemble EMD (EEMD) methods. The normalized PE of the five mode components is used as an eigenvalue, which is used as the input parameter of the SVM. Two different experimental datasets are used to verify the effectiveness of the proposed method. The results show that the proposed method has better diagnostic accuracy than EMD, EEMD and a BP neural network in the case of limited samples and unknown sample inputs. It can provide a good reference for the diagnosis of a rolling bearing and has practical application value.
The effect of underground coal mining on groundwater, ranging from minimal to severe depending on the mined-out panel size, is primarily associated with the change in ground hydraulic permeability. This paper presents a novel panel design method, taking consideration of reducing water loss during the mining operation, which is based on evaluating and ranking the impact of panel size on the hydraulic permeability of weakly cemented strata. The permeability test results of weakly cemented rock samples collected in the Yili No.4 Coal Mine in Xinjiang, China strongly indicates that, in contrast to common rock, their post-peak permeability during the total stress-strain process is lower than the initial permeability due to high porosity and the presence of clay minerals. A numerical modeling based on strain-permeability functions reveals that the post-mining permeability distribution in the weakly cemented overlying strata could be subdivided into three zones: the permeability reduction zone, the permeability restoring zone, and the permeability high-increment zone. The impact significance of different size factors on the post-mining permeability of overlying strata can be ranked in decreasing order as follows: mining height, panel width, and panel length, the quantification of which was based on the variance analysis of such indices as maximum pore pressure and maximum flow velocity. Based on the above findings, the optimal size of panel 21103 in the Yili No.4 Coal Mine was determined and validated by water level field observations.
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