In underground backfilling technology, long-distance pipeline transportation of gangue slurry is prone to problems such as sedimentation and blockage. At present, point-based acquisition and local observation methods are mostly used to monitor the pipeline transportation state, which makes it difficult to achieve continuous coverage and accurate localization of anomalies along long-distance pipelines. To address this problem, Distributed Acoustic Sensing (DAS) based on phase-sensitive optical time-domain reflectometry was adopted to achieve full-field continuous monitoring of blockage conditions in gangue slurry transportation pipelines. A gangue slurry transportation pipeline blockage experimental platform was established to simulate normal transportation as well as blockage conditions of 20%, 40%, and 60%. DAS optical fiber was used to collect vibration signals along the pipeline. A dual-branch identification model was constructed using One-Dimensional Convolutional Neural Network (1DCNN), Long Short-Term Memory (LSTM), and Cross Attention (CA) mechanism. The energy and power spectral density of DAS vibration signals were used as features to classify pipeline blockage conditions. The experimental results showed that the model achieved accuracy, recall, and F1 score of 91.27%, 93.10%, and 92.28% for normal pipeline conditions, and 92.72%, 92.06%, and 92.39% for different blockage conditions, respectively. The performance was superior to that of DAS vibration signal identification models such as multi-scale convolution combined with a hidden Markov model. Based on the proposed method, a DAS monitoring and intelligent identification system for gangue slurry transportation pipeline blockage was developed, which realized visualization of vibration signal feature analysis and a B/S network service application for pipeline blockage identification.
To address the problems of low system reliability and high maintenance costs caused by reliance on mechanical position sensors in permanent magnet external rotor hoists, a sensorless control strategy integrating multibody dynamics modeling for permanent magnet external rotor hoists under time-varying load was proposed. First, a mathematical model of the permanent magnet external rotor hoist was established, and the pulsating high-frequency signal injection method was theoretically analyzed. Second, a phase-locked loop position observer was used to dynamically track the rotor position, and a sensorless control system was constructed. Then, a multibody dynamics model of the hoist was established using RecurDyn software, including the elastic deformation of the wire rope and the drum winding effect, to accurately characterize the time-varying characteristics of the system load. Finally, an electromechanical coupling co-simulation model based on Matlab/Simulink and RecurDyn was established, and the proposed sensorless control system was experimentally verified using a physical verification platform. The results showed that under the time-varying load torque condition of the hoist, the proposed sensorless control strategy controlled the speed tracking error within ±0.2 r/min and the rotor position observation error below 0.01 rad. It showed good rotor position tracking accuracy and speed tracking performance throughout the entire operating cycle of the permanent magnet external rotor hoist.
The spontaneous combustion process of coal is influenced by multiple factors.Existing studies mainly investigate coal spontaneous combustion characteristics from aspects such as coal metamorphic degree,oxygen concentration,air supply rate,air humidity,particle size,and moisture content rate.However,the sulfur content in coal is also one of the important factors affecting coal spontaneous combustion.To investigate the effects of sulfur content on the spontaneous combustion characteristics and oxidation kinetic parameters of anthracite,oxidation processes of five coal samples with different sulfur contents were tested using a programmed temperature-rise experiment system with a tube furnace.The effects of different sulfur contents on CO and CO2 gas release characteristics and characteristic temperatures of spontaneous combustion during the low-temperature oxidation process of coal were quantitatively analyzed.By calculating the oxygen consumption rates of coal samples with different sulfur contents under different temperature conditions,the variation pattern of the oxygen consumption rate with temperature was quantitatively examined.Based on chemical reaction kinetics,the apparent activation energies of coal samples under different sulfur contents were calculated,and the variation pattern of apparent activation energy with sulfur content was quantitatively analyzed.The results showed that when the sulfur content remained constant,the concentrations of CO and CO2 and the oxygen consumption rate increased exponentially with increasing temperature,while the apparent activation energy gradually decreased.With increasing sulfur content,the concentrations of CO and CO2 and the oxygen consumption rate at each temperature point first increased and then decreased,reaching maximum values at a sulfur content of 5.14％.In contrast,the characteristic temperature of spontaneous combustion and the apparent activation energy showed opposite trends and reached minimum values at a sulfur content of 5.14％,at which point the coal sample exhibited the strongest spontaneous combustion tendency.Therefore,the critical sulfur content affecting the spontaneous combustion tendency of anthracite was 5.14％.
CO released from the oxidation of residual coal in goaf areas causes CO exceedance in the return air corner of coal mines,and clarifying the critical thresholds for CO exceedance in the return air corner and establishing a graded early warning index system are of great significance for the early warning of coal spontaneous combustion in goaf areas.To achieve accurate early warning of coal spontaneous combustion disasters in coal mine goaf areas,a medium-rank bituminous coal was taken as an example,and the oxidation characteristics of coal and the generation patterns of indicator gases under ambient temperature and heating conditions were systematically analyzed by combining laboratory experiments and field observations.Experimental results indicated that coal exhibited a hysteresis phenomenon of O2 consumption and CO generation at ambient temperature,and the background CO volume fraction generated by ambient-temperature oxidation was determined to be 18×10-6.During the heating oxidation stage,the oxygen consumption rate and CO generation rate showed a significant increasing turning point with a sharp increase at 70 ℃,and the characteristic gas C2H4 began to appear at 100 ℃.Based on these results,the low-temperature oxidation process of coal was divided into three stages,namely slow oxidation(30-70 ℃),accelerated oxidation(70-100 ℃),and intense oxidation(＞100 ℃).By integrating experimental data with historical field monitoring data,a four-level early warning index system for coal spontaneous combustion was established with the CO concentration in the return air corner as the core indicator,which provided a theoretical basis and practical guidance for early identification and graded prevention and control of coal spontaneous combustion based on CO concentration in the return air corner.
Coal gangue exhibits complex morphology, rough surfaces, and significant size variations. In dynamic conveying scenarios, it is easily affected by factors such as reflection, occlusion, and motion asynchrony, which lead to breakage or displacement of laser line stripes, resulting in point cloud sampling loss and volume measurement errors. To address this problem, a point cloud volume measurement method for coal gangue based on an improved projection-based integration method was proposed. The Random Sample Consensus (RANSAC) algorithm was used to fit the main plane, and a spatial filtering criterion was applied to effectively remove the conveyor belt background and noise. Initial region growing segmentation was performed based on normal and curvature constraints, and a multi-factor clustering mechanism was introduced to eliminate over-segmentation interference, thereby achieving accurate instance segmentation of adhesive objects. Considering that the vertical scanning perspective of the line-laser camera and the irregular natural morphology of coal gangue caused severe self-occlusion in the bottom region, a bottom surface completion strategy integrating normal foot projection and uniform density filling was proposed, and a closed bottom contour was reconstructed using a two-dimensional concave hull or ellipse fitting. In the traditional projection-based integration process, concave hull boundaries were introduced to eliminate redundant empty grids. The median criterion was applied to remove height outliers, and a radial-sector parallel strategy was adopted to improve overall computational efficiency and noise robustness. The experimental results showed that the overall average relative error of coal gangue volume measurement was only 8.92%, and the qualification rate reached 95.89% under the maximum allowable error standard of 20%. In multi-orientation flipping tests of the same gangue, the average relative error of volume measurement was only 5.7%.
In response to the problems of slow detection speed and low automation control level in traditional PLC-based mine air door monitoring technologies,a mine air door monitoring system based on PLC and an improved YOLOv11 model was proposed,which embedded the improved YOLOv11 model into the conventional PLC-based air door monitoring system to realize real-time and accurate recognition of underground personnel and vehicles and intelligent linkage control of air door opening and closing.Taking YOLOv11 as the base model,an EAW-YOLO model was proposed.The exponential moving average(EMA)attention mechanism was integrated into the C3k2 module to form a C3k2-EMA module to enhance the model's feature extraction capability.Then,ADown convolution was introduced to retain key information while performing channel dimensionality reduction.Finally,the WIoU loss function was introduced to enhance the regression convergence speed of the model by dynamically adjusting the weighting of different anchor boxes based on their importance.Experimental results showed that:① compared with YOLOv11,the EAW-YOLO model improved accuracy by 1.6％and mAP@0.5 by 1.9％,reduced the number of model parameters by 19.2％,and increased inference speed by 9.7％to reach 86.7 frames/s.② Compared with YOLOv11,Faster-CNN,EfficientDet,and RT-DETR,the EAW-YOLO model improved accuracy by 1.6％,0.6％,2.0％,and 0.2％,respectively,improved mAP@0.5 by 1.9％,0.7％,1.6％,and 1.1％,respectively,reduced the number of parameters by 0.5×106,135.0×106,1.8×106,and 40.7×106,respectively,increased inference speed by 7.7,51.2,9.8,and 35.1 frames/s,respectively,and reduced model size by 0.4,102.9,11.1,and 80.9 MiB,respectively.③ For different vehicles with large targets at close range,the EAW-YOLO model showed higher detection accuracy.For different vehicles with small targets at long distance,the detection accuracy of the EAW-YOLO model was slightly improved.For small personnel targets at long distance with blurred edge features,the EAW-YOLO model showed a larger improvement in detection accuracy and effectively identified correct personnel targets.In scenes with occlusion and strong backlighting,the EAW-YOLO11 model achieved higher detection accuracy.To verify the feasibility of the mine air door monitoring system based on PLC and the improved YOLOv11 model,laboratory validation was conducted,and the results showed that when the camera captured a vehicle model,the recognition signal was transmitted to the PLC in real time,thereby accurately controlling the opening and closing actions of the air door device.
Existing image super-resolution reconstruction methods have difficulty coping with multi-source coupled degradations such as coal dust scattering and non-uniform blur in underground coal mine environments,and they are limited by local receptive fields,making it difficult to capture global structures,while excessive model complexity prevents lightweight deployment on underground edge devices.To address these issues,a blind super-resolution reconstruction method for underground coal mine images based on degradation kernel diffusion was proposed.In the degradation modeling stage,degradation kernel diffusion modeling was introduced,and the reverse sampling process of a diffusion probabilistic model was used to explicitly simulate the degradation kernel distribution in complex underground coal mine scenes,thereby correcting reconstruction artifacts caused by degradation estimation bias at the early stage.In the image reconstruction stage,a hybrid Transformer-CNN encoder and a dynamic invertible decoder were designed,in which a parallel dual-branch structure was used to complementarily extract local textures and global dependencies,and a dynamic proportional fusion mechanism was employed to achieve adaptive interaction between degradation features and image content,reducing the number of model parameters while ensuring lossless transmission of deep features.By combining L1 loss,Structural Similarity(SSIM)loss,and perceptual loss,a multi-metric joint loss function was constructed to enhance perceptual image quality while ensuring pixel-level accuracy.Experiments were conducted on the CMUID underground coal mine image dataset and public benchmark datasets.The results showed that,in terms of objective evaluation metrics,the proposed method achieved overall superior performance in peak signal-to-noise ratio and SSIM compared with competing methods while maintaining a lower parameter count.In terms of subjective visual quality,the proposed method effectively suppressed low-light noise,sharpened edge structures,and clearly restored texture details of conveyor belts and coal blocks in underground coal mine scenes.
Drums and idlers are core components that bear the main load of belt conveyors and continuously perform rotational motion, and their health condition directly determines the operational efficiency and reliability of the entire belt conveyor system in coal mines. Focusing on key rotating mechanical components such as drums and idlers of coal mine belt conveyors, this paper systematically explains the typical fault types that are prone to occur under harsh roadway working conditions in coal mines and the corresponding fault monitoring methods, and analyzes the monitoring principles and technical routes based on vibration and sound signals. From three core aspects including vibration and sound signal preprocessing, feature extraction, and fault identification for rotating components of coal mine belt conveyors, the research progress in fault diagnosis is compared and reviewed. Research on vibration and sound signal preprocessing shows a development trend toward adaptive optimization of fixed parameters and the integration of multiple methods. The feature extraction methods show a trend from traditional methods to adaptive learning and from single methods to the integration of multiple methods. The fault identification methods show a trend from traditional machine learning models with simple structures to deep learning models. The main challenges encountered in the field of health monitoring and fault diagnosis of underground coal mine belt conveyors are summarized, including poor preprocessing performance of vibration and sound signals in harsh environments, insufficient feature extraction capability of single-signal perception methods under complex working conditions, the scarcity of underground coal mine fault samples, and insufficient generalization ability of fault diagnosis models. Future research and application of fault diagnosis technology for underground coal mine belt conveyors should focus on intelligent adaptive preprocessing methods for harsh underground environments, the development of multi-source monitoring and information fusion technologies based on in-depth understanding of fault mechanisms, and the exploration of new intelligent fault identification methods incorporating small-sample learning and enhanced generalization.
To address the problems that existing deep learning-based belt conveyor coal quantity detection algorithms have a large number of parameters, are difficult to deploy on edge computing devices, and lack quantitative detection capability, a belt conveyor coal quantity detection method based on an improved DeepLabv3+ was proposed. MobileNetV2 was used as the backbone network of DeepLabv3+ for feature extraction, which improved computational speed while maintaining segmentation accuracy as much as possible. Considering the directional characteristics of the coal flow and conveyor belt, as well as the elongated strip-like structure of conveyor belt pixel edges, Strip Atrous Spatial Pyramid Pooling (SASPP) was adopted for enhancement, and the SASPP module was fused with a 1×1 convolution and a residual structure to obtain CA-SASPP, thereby enhancing deep feature extraction. The Convolutional Block Attention Module (CBAM) mechanism was incorporated to achieved weighted emphasis on key information in the feature maps. Experimental results showed that, while the mean segmentation accuracy decreased by only 0.36%, the improved DeepLabv3+ model reduced the number of parameters by 85.58% and increased the inference speed to 113 frames/s, which was 12 frames/s higher than that of the original method, achieving significant lightweight performance while maintaining segmentation accuracy comparable to that of the original model. Based on the semantic segmentation results, quantitative coal quantity detection was achieved by calculating the area ratio between the coal region and the conveyor belt region, which provided a theoretical basis for intelligent speed regulation of multi-stage belt conveyors. The improved DeepLabv3+ model was accelerated using TensorRT and deployed on the Jetson Orin Nano edge computing device. Real-time processing and analysis of coal flow images were achieved, reducing the computational burden on cloud servers and meeting the requirements for real-time performance and accuracy in on-site industrial environments.
To investigate the coupling mechanism between permeability evolution of coal around boreholes and the radial stress distribution and failure characteristics in hard low-permeability coal seams,and to reveal the radial gas seepage characteristics of coal around boreholes,the radial stress distribution characteristics and fracture development degree of coal around boreholes were analyzed.Axial stress was applied using a constant-speed constant-pressure pump until coal sample failure occurred,simulating the entire process from elastic deformation to failure.By combining steady-state and transient methods,experimental studies on the radial gas seepage characteristics of coal around boreholes in hard low-permeability coal seams were conducted,and the permeability evolution characteristics during the whole deformation and failure process of coal samples were obtained.The results showed that:① coal around boreholes radially formed a crushed zone,a plastic zone,and an elastic zone in sequence.In the crushed zone,the coal structure became unstable and a large number of interconnected fractures were generated.In the plastic zone,new fractures were generated,but fracture apertures were restricted under high stress.In the elastic zone,primary fractures underwent elastic closure.② The permeability of coal around boreholes along the radial direction of the borehole decreased rapidly and then increased slowly,showing an overall distinct V-shaped variation trend.③ When the confining pressure increased from 3 MPa to 4 MPa,the permeability of coal samples decreased significantly,and the permeability under a confining pressure of 4 MPa was only 4.57％of that under 3 MPa.④ Within each stress zone along the borehole radial direction,the crushed zone exhibited the highest permeability,the plastic zone showed a stress peak and the lowest permeability,and the stress concentration phenomenon in the elastic zone gradually disappeared while permeability gradually recovered to the original permeability of the coal seam.
The construction of autonomous transportation systems in open-pit mines is an effective measure to reduce on-site personnel, avoid or reduce accidents, and improve production efficiency and equipment utilization. Therefore, in response to the safe production requirements of autonomous transportation in open-pit mines, technical requirements were proposed to regulate such transportation. ① Requirements for autonomous trucks were proposed. The trucks should have autonomous driving capability and be able to complete operations according to instructions from the dispatching management platform. They should have environmental perception capability to detect objects that may affect vehicle operation and predict their trajectories. They should have integrated navigation and positioning capability. They should have obstacle avoidance capability and automatically avoid different types of obstacles. They should have the capability to communicate with other equipment. They should have functions for storing operating status and fault information of autonomous trucks. They should have safety monitoring and management functions. They should have remote driving capability to support one operator controlling multiple vehicles. They should have high-precision maps containing lane lines, traffic signs, guardrails, and other information to meet the requirements of perception, positioning, planning, decision-making, and control for autonomous driving. They should also be able to operate in harsh mining environments such as dust, rain, snow, fog, severe vibration and extreme temperatures. ② Functional requirements of the onboard control system were proposed, including operation task processing and execution, environmental perception, planning and decision-making, positioning and navigation, vehicle control, safe parking, emergency parking, Vehicle to Everything (V2X), safety monitoring, data storage, and Over-the-Air (OTA) upgrade. ③ Requirements for drive-by-wire systems were proposed, including drive, steering, braking, lifting, status monitoring, and driving warning systems. ④ Requirements for autonomous operating environments were proposed, including the operating environment, stripping areas, dump sites, haul roads, parking areas, and refueling area sites. ⑤ Inspection requirements were proposed for intelligent incremental components of autonomous trucks, instruments, lighting and electrical systems, steering systems, power systems, lifting systems, and vehicle body components.
To address the problems of weak fault features,scarce high-quality samples,and cross-condition distribution shifts in mining rolling bearings,which lead to insufficient generalization performance of traditional deep learning models,a Mine Rolling Bearing Fault Diagnosis Model Based on Low-Rank Multimodal Fusion and Adversarial Metrics(MTSFCL)is proposed.The superlet transform was used to construct dual-modal input data composed of time-series signals and time-frequency images,which enhanced the multidimensional representation of rolling bearing faults.A lightweight dual-branch feature extraction layer was designed.The temporal branch adopted a Bidirectional Gated Recurrent Unit(BiGRU)enhanced by the Efficient Channel Attention(ECA)mechanism,which captured long-term dependencies in time-series signals while effectively suppressing interference from redundant information.The spatial branch was built on an improved StarNet architecture.Multi-scale convolution and a selective kernel fusion mechanism were used to extract multi-scale fault features from time-frequency images.Element-wise multiplication was used to achieve high-dimensional spatial feature mapping without increasing network depth.A Low-Rank Multimodal Fusion(LMF)module was designed,in which low-rank factors projected temporal and spatial features into a common subspace,and nonlinear fusion was performed through element-wise multiplication,enabling deep interaction between dual-modal features with low computational cost.To improve model generalization performance,a domain adaptation module based on an adversarial metric was constructed by combining the Conditional Domain Adversarial Network(CDAN)with Local Maximum Mean Discrepancy(LMMD)as a metric constraint,thereby reducing marginal and conditional distribution differences between the source domain and the target domain.Experimental results showed that:① the number of parameters of MTSFCL was only 0.322 1 × 106,and the inference time for a single sample was 2.76 ms.② The average diagnostic accuracy under a single operating condition reached 99.94％.Under the small-sample condition with only five fault samples for each class,the average diagnostic accuracy reached 94.12％,which was significantly higher than that of high-parameter models such as ViT and VGG16.③ Under cross-condition scenarios,the average diagnostic accuracy reached 99.28％.Compared with the CDAN domain adaptation method without the LMMD metric constraint,the accuracy increased by 4.27％.High accuracy was also maintained under strong noise interference,demonstrating strong generalization performance and robustness.
Under complex underground operating conditions,mechanical noise generated by belt friction and coal flow impacts,airflow-induced disturbance noise,and coupled noise from multiple devices are superimposed,causing fault-related acoustic signatures of idlers to be easily masked by environmental noise.Meanwhile,the acquisition of abnormal idler samples is difficult and annotation costs are high,making traditional supervised learning-based idler abnormal condition detection methods hard to generalize effectively.To address these issues,an unsupervised idler abnormal condition detection method based on Multi-Granularity Attention Autoencoder(MG-AAE)was proposed,which used only normal-condition idler sounds for model training and required no fault labels.A multi-granularity composite acoustic feature composed of Mel spectrograms and Mel-Frequency Cepstral Coefficients(MFCCs)was constructed to jointly capture energy contours and fine-grained acoustic signatures.A Gaussian Difference Pyramid(GDP)and a Multi-Head Attention(MHA)mechanism were introduced into the encoder to perform multi-scale modeling and adaptive weighted fusion,thereby suppressing steady background noise and highlighting key fault-related frequency bands.A multi-dimensional reconstruction mean-square error was used as the anomaly criterion to achieve automatic identification of idler abnormal conditions.Experimental results showed that,when trained using only normal samples,the MG-AAE model demonstrated excellent performance in cross-device and real-world operating conditions.Evaluation on four typical device categories in the MIMII dataset showed that,under a strong noise condition of 0 dB,the average area under curve(AUC)and local AUC(pAUC).f the MG-AAE model reached 84.2％and 70.4％,respectively,representing improvements of 7.3％and 5.6％over the Autoencoder model.On real idler data,the AUC reached 95.47％,and the reconstruction error of abnormal samples was approximately 1.40 times that of normal samples.These results indicate that the proposed method has good cross-device generalization and a low false alarm rate,and provides effective technical support for abnormal condition detection of idlers in coal mine belt conveyor systems.
The coal-rock interface trajectory is multivariate time-series data,and complex correlations exist among different variables,which makes high-precision prediction challenging.To address this problem,this study proposed a coal-rock interface prediction model based on multichannel correlated complementary features,named SSIC-former,which integrated a Centralized Attention Mechanism(CAM),an Interactive Convolution Block(ICB),and a Sharpness-Aware Minimization(SAM)strategy.First,a sliding window method was used to construct continuous samples from the raw data.Then,an SSIC-former architecture for coal-rock identification was built to extract cross-channel correlation information and local features of the coal-rock interface,and reversible instance normalization was introduced to dynamically eliminate data nonstationarity.The CAM extracted correlated complementary features among multiple channels,while the ICB extracted local features at different scales and enabled dynamic cross-scale interaction,and their outputs were fused through residual connections to enhance feature representation.Finally,during the training stage,the SAM strategy was combined to prevent the model from falling into local optima,and the prediction results were output through a projection layer.Experimental results showed that:① An SSIC-former-based coal-rock interface prediction model achieved a mean absolute error of 6.37 mm,a mean absolute percentage error of 2.79％,a root mean square error of 8.08 mm,a mean square error of 0.07 mm2,and a coefficient of determination of 0.99,with an average inference time of 0.006 6 s per sample.Among Transformer-based models,it had the shortest inference time and met the low-latency requirements of real-time operation of shearers.(2)Compared with models based on LSTM,Crossformer,Nonstationary_Transformer,FPPformer,iTransformer,and PatchTST,the SSIC-former-based model outperformed the other models in the first five evaluation metrics mentioned above,indicating that the SSIC-former-based model had high prediction accuracy and strong generalization ability and provided more accurate results for coal-rock interface trajectory prediction.
Large coal block accumulation is one of the main causes of blockage at the head transfer point of the scraper conveyor in a fully mechanized mining face.Timely and accurate breaking of large coal blocks is crucial for ensuring smooth coal flow in the fully mechanized mining face.However,short-term occlusion and posture changes of large coal blocks lead to low detection accuracy,which further prevents the crushing robot from accurately breaking them.To address this problem,a tracking and detection model for large coal blocks on a scraper conveyor named DAMP-YOLO11n-BT based on YOLO11n was proposed.The DCSNet module was used to replace the backbone network of the original YOLO11n model,which reduced the floating-point operations of the model while maintaining detection accuracy.The AG-SPPF module was adopted to enhance the model's attention to the global background information of the coal flow region of the scraper conveyor and the local key information of coal blocks,and to improve its anti-interference capability under uneven illumination and other environmental conditions.Powerful-IoU(PIoU)was introduced to optimize bounding box regression through adaptive penalty and gradient adjustment,strengthen the focus on medium-quality anchor boxes,and enhance the detection capability for large coal blocks in dense coal block scenes.By integrating the DAMP-YOLO11n model with the ByteTrack algorithm,the DAMP-YOLO11n-BT model was proposed to realize the tracking and detection of large coal blocks.Experiments were conducted using a large coal block detection dataset of scraper conveyors collected on site.The results showed that:① the accuracy,mAP@0.5:0.95,and recall of the proposed DAMP-YOLO11n model were 86.3％,77.6％,and 85.5％,respectively,which were improved by 2.4％,2.4％,and 3.2％,respectively,compared with the original YOLO11n model.The number of parameters,floating-point operations,and model size were 1.95×106,4.8× 109,and 4.09 MiB,which were reduced by 24.4％,23.8％,and 23.6％,respectively,compared with the original YOLO11n model.The detection speed reached 351 frames/s,which met the real-time detection requirement.② The multiple object tracking accuracy,multiple object tracking precision,and ID F1 score of DAMP-YOLO11n-BT for large coal block tracking and detection were 76.6％,74.5％,and 75.2％,respectively,all of which were better than those of YOLO11n-BT.The proposed method solves the problems of missed detection and ID switching of occluded large coal blocks and meets the tracking requirements for precise operation of the crushing robot.
Existing robot obstacle avoidance methods mostly rely on a single sensor, which leads to large obstacle calibration errors and insufficient safety margins in complex unstructured roadway environments where dynamic obstacles appear randomly. To address these problems, a dynamic obstacle avoidance method for tracked robots based on multi-sensor perception and deep reinforcement learning was proposed for narrow unstructured coal mine roadways. The high-resolution imaging capability in the visible spectrum and the sensitivity to thermal radiation in the infrared band were used to perceive roadway environments with low illumination and high reflectivity. The Mean Shift algorithm was introduced to perform kernel density estimation on the occurrence probability of obstacles in the roadway, and the three-dimensional spatial coordinates of obstacles were calibrated to overcome the limited field of view and occlusion caused by the narrow roadways. The spherical envelope method was used to construct the safety potential field boundary corresponding to the three-dimensional spatial coordinates of obstacles as the constraint condition for the obstacle avoidance reward in deep reinforcement learning, and the robot obstacle avoidance behavior was optimized according to the reward to achieve dynamic obstacle avoidance. Experimental results showed that under conditions of high dust concentration, strong light, and weak light, the mean of cross-modal structural similarity between the visible light images and the infrared images of the perceived results was higher than 55%, enabling accurate perception of the roadway environment. The maximum error between the calibrated obstacle position and the actual position was only 0.4 m. During movement, the minimum distance between the robot using the proposed method and obstacles was greater than the safety threshold, and no collision occurred, indicating a sufficient safety margin for obstacle avoidance.
At present, health monitoring of mine hoisting systems faces challenges such as large system span and dispersed component distribution, which makes it difficult to achieve full coverage, continuous online monitoring, and cost-effective deployment for key components. Early degradation of key components often manifests as weak features. Under multi-source disturbances and strong noise backgrounds, weak fault information is easily masked, significantly increasing the difficulty of feature extraction. In addition, harsh service conditions and high reliability requirements further complicate monitoring tasks. This study introduces the structural composition and functions of mine hoisting systems, with a focus on analyzing the core requirements for health state monitoring of key components such as the drum, main shaft, steel wire rope, and bearings during long-term service. On this basis, two types of core sensing technologies used in current mine hoisting system health monitoring are described in detail. One type is fixed-point sensing technology based on multiple monitoring signals such as vibration, sound, vision, and temperature, and the acquisition methods, sensing principles, and applicable scenarios of each type of signal are explained. The other type is mobile sensing technology based on inspection using mobile robots and integrated inspection of hoisting conveyances, and the characteristics, operating processes, and application limitations of these technologies are analyzed. In addition, the application of health state assessment methods in the state monitoring of key components of mine hoisting systems is analyzed, and the principles, effectiveness, and characteristics of intelligent assessment methods based on signal processing, machine learning, and deep learning are discussed. The development status and technical characteristics of traditional mine hoisting system monitoring platforms and digital twin platforms are summarized. Based on the existing problems and challenges in current health monitoring technologies for mine hoisting systems, future development directions are proposed, including optimization of sensing under complex operating conditions, intelligent mobile sensing, efficient evaluation models, and integrated monitoring systems.
At present,research on fault diagnosis of mine belt conveyors mainly focuses on three aspects:single-signal detection,traditional algorithm modeling,and multi-feature fusion.Diagnostic methods based on single signals such as vibration and current are prone to problems including bias in feature extraction and insufficient reliability of diagnostic results.Some optimization algorithms suffer from low efficiency in parameter optimization and poor adaptability to multiple fault types.In addition,existing multi-feature fusion studies lack specificity and cannot achieve complementary validation among multidimensional signals.To address these problems,an intelligent fault diagnosis method for mine belt conveyors based on multi-source signal fusion and Bat Algorithm(BA)-optimized Sequential Minimal Optimization(SMO)parameters,namely BA-SMO,was proposed.A vibration-temperature-smoke multi-source signal collaborative acquisition system was constructed.Linear trend removal and an improved Kalman filtering method were used to perform signal denoising preprocessing.An improved Variational Mode Decomposition(VMD)algorithm incorporating an adaptive penalty factor and a redundant component elimination mechanism was proposed and combined with multiscale sample entropy to achieve accurate quantitative extraction of fault features.Based on the extracted multidimensional feature vectors,a BA-SMO model was constructed,in which the global optimization capability of BA was used to optimize the core parameters of SMO,thereby improving the classification accuracy and environmental adaptability of the model.The experimental results showed that:① the signal-to-noise ratio of the improved VMD algorithm reached 27 dB,and the Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)remained below 0.08.The algorithm showed significant advantages in signal decomposition accuracy,efficiency,and matching accuracy of fault feature frequency,and accurately separated the characteristic frequencies of multiple types of faults in mine belt conveyors.② The BA-SMO model achieved high recognition accuracy for various faults.The recognition accuracy for bearing inner race faults was close to 100％,and the recognition accuracy for idler slip faults was above 90％.③ Under low,medium,and high interference conditions,the average recognition accuracies of BA-SMO were 99.2％,97.6％,and 95.3％,respectively.The missed detection rate was below 5％,and the average recognition time was only 32.6 ms.Field application results showed that during a three-month field application,the proposed method successfully identified faults including bearing inner race pitting,idler slip,and rolling element wear.The diagnostic accuracy reached 97.8％,which improved the diagnostic accuracy by 25.3％compared with the traditional manual inspection method and effectively reduced the missed detection and misdiagnosis rates.
No-Reference Video Quality Assessment(NRVQA)is a key technique for evaluating the video quality of underground drilling sites in coal mines and enabling remote monitoring.Existing NRVQA methods are mostly designed for general ground scenes and are difficult to achieve satisfactory performance in underground drilling environments where composite image distortions are caused by coal dust and equipment vibration.To address this problem,an NRVQA method for underground drilling sites based on spatiotemporal domain dynamic aggregation was proposed.Video features of drilling site surveillance videos were extracted from two dimensions,namely spatial and motion.The spatial feature extraction branch was based on the Swin Transformer architecture and introduced a local perception enhancement module to strengthen the representation capability of texture and edge details under coal dust interference.The motion feature extraction branch embedded a DeformConv3D deformable convolution module into ResNet to accurately capture the dynamic characteristics of drilling rig motion trajectories and coal dust diffusion.A spatiotemporal dynamic aggregation module was designed to dynamically allocate the weights of spatial and motion features,enabling discriminative representation of different distortion types and degrees.The Coal-DB dataset was constructed and ablation experiments and comparative experiments were conducted.The results showed that the proposed method achieved Spearman rank correlation coefficient,Pearson linear correlation coefficient,Kendall rank correlation coefficient,and root mean square error values of 0.904 3,0.902 3,0.753 6,and 4.684 0,respectively,which were superior to the baseline model and mainstream video quality assessment methods such as VSFA and StableVQA.The predicted video quality scores of this method were closer to the subjective scores.
Mining wireless short-range communication technology has the characteristics of low latency,high reliability,and flexible networking,and is a key means to address problems encountered in the intelligent mine construction,such as large data transmission delay,complex information interaction processes,and limited equipment control accuracy.At present,research on mining wireless short-range communication mainly focuses on ZigBee,WiFi,and 5G.ZigBee is difficult to meet the requirements of routine applications,WiFi suffers from co-channel interference and large transmission delay,and 5G involves uncontrollable data transmission risks and high costs.To address these problems,this study proposed a mining wireless short-range communication solution with sidelink communication operating in licensed dedicated frequency bands as the core.Two types of architectures were constructed,including an independent networking architecture for mining sidelink communication and a hybrid networking architecture integrating mining sidelink communication and 5G for mining,forming a new full-scenario mining communication mode of"wide-area coverage plus local enhancement".To meet the requirements of time synchronization,resource efficiency improvement,and reliability assurance in the application of sidelink communication in coal mines,key technologies such as GNSS-free time synchronization,dynamic configuration of resource pools based on subframes and subchannels,physical-layer wireless signal transmission,and distributed resource allocation for terminals were investigated,which effectively improved the adaptability and reliability of the system in complex coal mine environments.Application scenarios of mining sidelink communication technology in intelligent equipment control,underground autonomous driving,and emergency command during disasters were also studied,which verified its practical value in promoting the integration of mining operations and communication systems.A test system simulating a GNSS-free underground coal mine environment was built in the laboratory,and the test results showed that the average system latency was less than 23 ms,which effectively met the requirements of critical mining scenarios for wireless short-range communication technology and provided a reliable wireless short-range communication solution for intelligent mine construction.
