Abstract:Simultaneous Localization and Mapping(SLAM)is a key technology for achieving autonomous navigation of mining robots.However,due to sparse and highly repetitive environmental features in underground coal mines,significant cumulative localization errors and prolonged relocalization times occur.To address this problem,a multi-sensor mapping and localization method for underground coal mines based on semantic landmarks was proposed.The method integrated observation information from visual odometry,inertial odometry,and LiDAR odometry to construct a tightly coupled multi-sensor fusion odometry system,thereby improving localization robustness in feature-deficient environments.Semantic landmarks suitable for underground environments were defined.By establishing a mapping relationship between roadway structural features and landmark encoding information,a fused semantic landmark map containing spatial geometric features and customized semantic labels was constructed to solve low relocalization efficiency and feature mismatches caused by the high repetitiveness of roadway features.Semantic landmarks were used to correct cumulative odometry errors in real time to achieve dynamic pose correction of the robot.Experiments were conducted using a dust suppression robot platform in surface tunnel environments and in underground industrial tests.The results showed that the average mapping error in the ground tunnel was 0.020 m,the maximum static localization error was 0.035 m,the maximum absolute pose error in dynamic localization was 0.153 m,and the average relocalization time was 3.3 s.In underground roadways,a global map covering 2 400 m was constructed,with an average error of 0.038 m per 100 m,and autonomous navigation of the robot was achieved.

Abstract: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.

Abstract: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.

Abstract:Existing studies on intelligent mine ventilation still show deficiencies in accurate perception of key parameters,coupled processing of multi-source information,and coordinated regulation,and have not yet formed an integrated technical system,which makes it difficult to meet the demand for refined and intelligent management of mine ventilation systems.To address these problems,an integrated system architecture of"perception-transmission-analysis-decision-application"was adopted,and an intelligent mine ventilation system was de-signed.The system adopted a wind velocity monitoring method based on the ultrasonic time difference principle and a wind pressure monitoring method based on piezoresistive Micro-Electro-Mechanical Systems(MEMS),achieving high-precision online perception of ventilation parameters.Through structural optimization of air doors and air windows,variable frequency drive of power equipment,and multi-parameter perception and intelligent control technologies,remote coordinated control of ventilation facilities and collaborative regulation of power equipment were realized.Combined with multi-source information fusion,ventilation network calculation,Radial Basis Function(RBF)neural-network-based identification,and fuzzy inference methods,abnormal diagnosis of the mine ventilation network was conducted,enabling identification of ventilation anomalies,determination of their locations,and assessment of the affected range of disasters.Backpropagation(BP)neural network and Particle Swarm Optimization(PSO)algorithms were used,together with a dynamic weight optimization mechanism,dynamic prediction of required air volume and global optimal regulation of the ventilation network were achieved.Field application results showed that the maximum relative error of air volume calculation was 8.38％,and the average relative error of wind resistance calculation was 2.11％,indicating that the system accurately reflected the operating state of the mine ventilation network,and automatically performed emergency ventilation control and guided personnel evacuation based on disaster information,effectively improving the intelligent management level and intrinsic safety capability of the mine ventilation system.

Abstract: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.

Abstract: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.

Abstract: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.

Abstract: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.

Abstract: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.

Abstract:The complex non-stationary mechanical noise in fully mechanized mining faces severely interferes with underground dispatch communication.Existing speech separation methods based on the Time-Domain Audio Separation Network(TasNet)architecture(encoder-mask network-decoder)tend to generate target speech masks that retain residual noise and interfering speech components.In addition,noise suppression may damage target speech features,resulting in reduced speech separation accuracy.To address this problem,a speech separation method for fully mechanized mining faces based on a mask feature cross pre-decoding network was proposed.The mask feature cross pre-decoding network was integrated after the mask network of TasNet and mainly consisted of a mask feature extraction module and a feature cross pre-decoding module.The mask feature extraction module learned noise-related features in different target speech masks through concatenation operations and a convolutional gating module,generated noise-related complementary weights,and used these weights to perform complementary weighting on the target speech masks to achieve noise filtering.The feature cross pre-decoding module performed cross-complementary fusion of features from different target speech masks,mined correlation information among the target speech masks,and then used a convolutional gating module and a residual enhancement module to purify and compensate the masks,avoiding weak speech from being masked and protecting target speech that may be damaged during the noise suppression.Experimental results showed that,compared with mainstream TasNet-based speech separation methods such as Convolutional Time-Domain Audio Separation Network(Conv-TasNet),Dual-Path Recurrent Neural Network(DPRNN),Dual-Path Transformer Network(DPTNet),and Globally Attentive Locally Recurrent Network(GALR),the proposed method improved the Scale-Invariant Signal-to-Noise Ratio Improvement(SI-SNRi)by 3.52,1.74,1.40,and 2.09 dB,and improved the Signal-to-Distortion Ratio Improvement(SDRi)by 3.21,1.45,1.14,and 1.80 dB,respectively,and had fewer parameters.The proposed method can be deployed on embedded chips with built-in Neural Network Processing Units(NPUs).The module is compact and requires low computational cost,meeting the engineering application requirements for miniaturization and low power consumption of underground voice terminals.

Abstract: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.

Abstract: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.

Abstract:Seismic wave CT inversion technology is an important method for rock burst hazard prediction,as well as routine rock burst prevention monitoring and effectiveness evaluation.However,in practical applications,active CT inversion has high implementation costs,cannot achieve continuous real-time monitoring,and is limited in large-scale detection,making it difficult to dynamically track stress changes.Passive CT inversion suffers from large errors in source location and low resolution,and the quality of tomographic imaging is constrained by the frequency and energy of natural microseismic events.To address this problem,a joint inversion strategy based on active-passive dual-source CT suitable for predicting rock burst hazards in coal pillars of deep roadways is proposed.First,active CT detection was implemented by arranging artificial seismic sources and receiver arrays,and a high-precision three-dimensional initial velocity model was obtained using the travel-time tomography method.Based on the active detection,the passive seismic sources recorded by the microseismic monitoring system during the subsequent time period,along with their ray travel-time information,were combined to form the joint inversion dataset.The high-precision three-dimensional initial velocity model obtained from active CT inversion was used as the initial model for the joint inversion.Travel-time tomography was performed again.The rock burst risk zones in the coal pillars were predicted according to the inversion results.An engineering application was carried out at the working face 7305 of Zhaolou Coal Mine,and the inversion results of active CT,passive CT,and active-passive dual-source joint CT were comparatively analyzed.The results showed that the dual-source CT joint inversion strategy effectively complemented the coverage blind zones of a single method and improved the identification accuracy of high-stress zones.Verification results based on microseismic events showed that more than 80％of the seismic sources were located within the high-velocity zones identified by the dual-source CT inversion,demonstrating the feasibility and accuracy of this strategy.

Abstract: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％.

Abstract:The operating environment of coal mine belt conveyors is complex,and multi-source interference causes severe noise contamination in vibration signals,making fault features indistinct.Fault diagnosis based solely on mechanism analysis using signal decomposition and frequency-domain feature reconstruction makes it difficult to achieve accurate and stable identification.Fault diagnosis of coal mine belt conveyors highly depends on the stability of large-scale data transmission between underground and surface systems,which makes it difficult to realize real-time and high-precision fault diagnosis at the edge side.To address these problems,an edge-side fault diagnosis and lightweight modelling approach for coal mine belt conveyors was proposed.A signal preprocessing method combining Ensemble Empirical Mode Decomposition(EEMD)and Denoising Autoencoder(DAE)was adopted to achieve deep denoising of vibration signals,and feature reconstruction of the denoised signals was conducted based on correlation coefficients.A joint fault diagnosis method combining mechanism analysis and a data-driven model was established,and a convolutional neural network was used to perform local feature extraction and deep feature mining,which effectively compensated for the insufficient adaptability of a single fault diagnosis method.To meet the deployment requirements of edge-side fault diagnosis models in underground environments,convolutional channels and fully connected layer neurons with less importance were pruned to effectively remove redundant structures in the model.The experimental results showed that the joint fault diagnosis method combining mechanism analysis and a data-driven model achieved an average accuracy of 98.54％.After lightweighting,the number of parameters,computational cost,model size,and memory usage were reduced by 24.5％,22.7％,22.8％,and 24.4％,respectively,compared with those before lightweighting,while the decreases in average accuracy,recall,and Fl score were all less than 1％,achieving a balance between diagnostic accuracy and computational resource consumption and improving the feasibility of deploying the fault diagnosis model in underground edge environments.

Abstract: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.

Abstract:In fully mechanized working face in thin coal seams,fixed rail-type inspection robots are difficult to adapt to curvature of working face track and floor undulations,cable-suspended inspection robots are difficult to adapt to environments with large roof undulations,while ground-moving inspection robots have low obstacle-crossing speed and insufficient flexibility.To address these problems,a quadruped inspection robot walking mechanism suitable for the complex environment of a fully mechanized working face in thin coal seam was proposed.The walking mechanism was driven by an explosion-proof servo motor and enabled switching between high-and low-leg-lifting modes through a linkage-leg mechanism and a gear shifting mechanism,thereby balancing walking efficiency and obstacle-crossing capability.Taking the installation position of the crankshaft as the design variable and the leg lifting height of the linkage-leg mechanism and the driving torque as optimization objectives,a multi-objective optimization model was established and solved using the NSGA-Ⅱ algorithm,and the Pareto optimal solution set under both high-and low-leg-lifting modes were obtained,which significantly improved the obstacle-crossing capability of the walking mechanism while considering driving requirements and force transmission performance.A robot simulation scenario for a fully mechanized working face in thin coal seam was established in ADAMS and dynamic simulations under four operating conditions,including flat-ground walking,slope walking,flat-ground obstacle crossing,and slope obstacle crossing,were carried out.The results showed that the maximum walking speed of the robot reached 15.8 m/min,the robot walked stably on a 10° slope,and it crossed obstacles with a height of 100 mm.Under the four operating conditions,the robot posture remained controllable without instability,meeting the requirements for motion performance and stability for follow-up inspection in fully mechanized working faces in thin coal seam.

Abstract:Coal-gangue identification is a key technology for reducing the gangue content in raw coal during fully mechanized top-coal caving mining.Affected by the high dust concentration,low visibility,and limited operating space in underground working environments,existing identification methods based on a single feature such as images,multispectral and ray signals,sound,or vibration acceleration have difficulty achieving precise coal-gangue identification.To address this problem,vibration and sound signals were used as joint identification features,and a vibration-acoustic coupling model of coal-gangue impacting the tail beam of a hydraulic support was established using COMSOL Multiphysics software.The dynamic behavior and sound pressure frequency spectrum characteristics of coal-gangue impacting the tail beam under different gangue shapes and incidence angles were investigated,and the distribution patterns of vibration acceleration and sound pressure signals were obtained.The results showed that the stress,vibration acceleration amplitude,and sound pressure signal characteristics generated by gangue impacting the tail beam were all greater than those generated by coal.When spherical,cubic,and cylindrical coal-gangue particles impacted the tail beam,the maximum von Mises stress,vibration acceleration amplitude,and main frequency of the sound pressure decreased successively.As the incidence angle of coal-gangue particles increased,the contact force,peak vibration acceleration,and spectral centroid of the sound pressure signal during impact with the tail beam all decreased,and the response attenuation rate during gangue impact was higher than that during coal impact.These findings provide a theoretical basis for constructing a coal-gangue identification strategy based on the fusion of multi-feature vibration-sound pressure signals.

Abstract: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.

Abstract:In underground small-current grounding power supply systems,the decomposition performance of the single-phase grounding fault line selection method based on Variational Mode Decomposition(VMD)depends heavily on the selection of parameters such as the penalty factor and the number of decomposition modes,which are difficult to set uniformly for different signals.To address this problem,a fault line selection method for mine small-current grounding based on optimized VMD and Random Forest(RF)was proposed.The Crested Porcupine Optimizer(CPO)was used to adaptively optimize the key parameters of VMD,including the penalty factor and the number of decomposition modes.A simulation model of underground power supply lines was established on the PSCAD/EMTDC platform.Zero-sequence current data under different fault conditions were obtained by changing the grounding resistance,initial fault phase angle,fault line,and fault location.The optimized VMD was applied to decompose the fault zero-sequence current signals.The modal components of each line were extracted,and their sample entropy was calculated to construct multidimensional feature vectors that reflected the complexity and nonlinear characteristics of the signals.The feature vectors were then input into the RF classifier for training and identification to achieve accurate determination of the fault line.The simulation results showed that the accuracy of the RF classifier was 98.3％,which was higher than that of Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM)network,and Extreme Learning Machine(ELM).The experimental results showed that the proposed method achieved a fault identification accuracy of 97.5％,unaffected by factors such as transition resistance,initial phase angle,and fault location,demonstrating high accuracy and applicability.