Automatic segmentation of coal crack in CT images is of great significance for the establishment of digital cores. In addition, segmentation in this field remains challenging due to some properties of coal crack CT images: high noise, small targets, unbalanced positive and negative samples, and complex, diverse backgrounds. In this paper, a segmentation method of coal crack CT images is proposed and a dataset of coal crack CT images is established. Based on the semantic segmentation model DeepLabV3+ of deep learning, the OS of the backbone has been modified to 8, and the ASPP module rate has also been modified. A new loss function is defined by combining CE loss and Dice loss. This deep learning method avoids the problem of manually setting thresholds in traditional threshold segmentation and can automatically and intelligently extract cracks. Besides, the proposed model has 0.1%, 1.2%, 2.9%, and 0.5% increase in Acc, mAcc, MioU, and FWIoU compared with other techniques and has 0.1%, 0.8%, 2%, and 0.4% increase compared with the original DeepLabV3+ on the dataset of coal CT images. The obtained results denote that the proposed segmentation method outperforms existing crack detection techniques and have practical application value in safety engineering.

To address the challenge of reduced prediction accuracy caused by capacity regeneration during the use of lithium-ion batteries, this study proposes an RUL (remaining useful life) prediction method based on mode decomposition and an enhanced Informer-LSTM hybrid model. The capacity is selected as the health indicator, and the CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) algorithm is employed to decompose the capacity sequence into high-frequency and low-frequency components. The high-frequency components are further decomposed and predicted using the Informer model, while the low-frequency components are predicted with an LSTM (long short-term memory) network. Pearson correlation coefficients between each component and the original sequence are calculated to determine fusion weights. The final RUL prediction is obtained through weighted integration of the individual predictions. Experimental validation on publicly available NASA and CALCE (Center for Advanced Life Cycle Engineering) battery datasets demonstrates that the proposed method achieves an average fitting accuracy of approximately 99%, with MAE (mean absolute error) below 0.02. Additionally, both MAPE (mean absolute percentage error) and RMSE (root-mean-square error) remain at low levels, indicating improvements in prediction precision.

To address the issues of high model complexity and weak noise resistance in convolutional neural networks for bearing fault diagnosis, this paper proposes a novel lightweight multi-angle feature fusion convolutional neural network (LMAFCNN). First, the original signal was preprocessed using a wide-kernel convolutional layer to achieve data dimensionality reduction and feature channel expansion. Second, a lightweight multi-angle feature fusion module was designed as the core feature extraction unit. The main branch fused multidimensional features through pointwise convolution and large-kernel channel-wise expansion convolution, whereas the auxiliary branch introduced an efficient channel attention (ECA) mechanism to achieve channel-adaptive weighting. Feature enhancement was achieved through the addition of branches. Finally, global average pooling and fully connected layers were used to complete end-to-end fault diagnosis. The experimental results showed that the proposed method achieved an accuracy of 99.5% on the Paderborn University (PU) artificial damage dataset, with a computational complexity of only 14.8 million floating-point operations (MFLOPs) and 55.2 K parameters. Compared with existing mainstream methods, the proposed method significantly reduces model complexity while maintaining high accuracy, demonstrating excellent diagnostic performance and application potential.

Permeability is a critical parameter governing the gas flow behavior of the coalbed methane (CBM) reservoir during the exploration and exploitation of CBM, as well as the geological storage of CO₂ in the coalbeds. It is strongly associated with the multi-scale fractures developed in coal. Based on the distribution characteristics of micro-fractures, a multi-scale permeability model for coal reservoirs was established by introducing the permeability tensor, which comprehensively considers adsorption-induced coal swelling, pore pressure, effective stress, and micro-fractures. Further, the dynamic evolution law and mechanism of multi-scale permeability of coal reservoirs under different adsorption pressures were discussed. The results indicate that the increase in effective stress on the coal caused by adsorption-induced swelling essentially leads to a decrease in the equivalent multi-scale permeability of coal. Two key indicators, namely equilibrium pressure and rebound pressure, were defined to quantitatively characterize the evolution law of the equivalent multi-scale permeability during gas adsorption or desorption processes. The effective stress generated by the CO₂ adsorption-induced swelling effect in the low-rank coal is 1.47 times that in the middle-rank coal and 2.51 times that in the high-rank coal. Additionally, the effective stress generated by the CO₂ adsorption-induced swelling effect in the low-rank coal is 5.15 times that generated by N₂, while this level is 4.32 times higher than that in the middle-rank coal. Therefore, compared with the low- and middle-rank coal, the high-rank coal exhibits a smaller decrease in multi-scale permeability due to its weaker adsorption-induced swelling effect. During N₂ adsorption, the pore pressure effect dominates over the adsorption-induced swelling effect, resulting in a decrease in the effective stress on the coal with increasing gas pressure. Consequently, the equivalent multi-scale permeability of coal will increase much more significantly with an increase in injected N₂ pressure than with an increase in CO₂ pressure. By accounting for the differences between the effects of adsorption-induced swelling and pore compression on the equivalent multi-scale permeability of coal reservoir, the injectivity of CO₂ can be improved by mixing it with N₂.

Coal fracture segmentation in CT images is critical for coal structure analysis, coalbed methane extraction, and mine safety, but it is challenged by complex fracture features and limited computing resources for mine-site deployment. Basic UNet exhibits redundancy, sensitivity to image noise, and high overfitting risk. This study proposes LightECA-UNet, integrating depthwise separable convolution (DSC), efficient channel attention (ECA), and adaptive channel pruning. Experiments show LightECA-UNet achieves 1.6% higher mean Intersection over Union (mIoU) and 2.5% higher fracture IoU than currently popular models. Compared to lightweight counterparts, it reduces computational load by 87.1% and parameter count by 86.9%, enabling deployment on mine-used edge equipment while maintaining segmentation accuracy.

In order to realize the efficient exploitation of coalbed methane (CBM) in the southern Qinshui basin in China, raw coal specimens with original fractures of the No.3 coal reservoir in the Sihe mine were selected to conduct compression tests with different confining pressures. During this loading process, the synchronous and staged real-time CT scanning tests were carried out by a high-precision X-ray scanner. The mechanical properties, the failure mode, and the evolution of fractures of coal samples due to the increased effective stress were analyzed from the microscopic point of view. The result showed that the original fractures in the coal specimens of the Sihe coal mine are relatively developed. Four mechanical stagescompaction, elastic, yield, and plastic failurewere consistently identified; image-based segmentation allowed real-time, precise quantification of crack initiation and growth. Within the tested confining pressure range, further confirmation through real experiments that compressive strength, peak strain, and elastic modulus rose with confinement, while residual strength was preserved by frictional resistance along fracture surfaces. New fractures tend to start cracking near the original fractures. Crack networks were more intricate under lower confinement (uniaxial tensile failure) and simplified yet more distributed under higher confinement (triaxial shear failure), indicating a transition from brittle to semibrittle/semiductile behavior. Quantitative CT metricsgray value, fracture volume, and fracture porosityproved reliable indicators of damage progression. Porosity is below 1% at the compaction and elasticity stage, twice the size of the original fractures at the initial damage stage. The integrated experimental–visualization workflow elucidates fracture propagation mechanisms in anthracite and offers theoretical guidance for CBM stimulation and ground-control strategies in the Qinshui Basin.