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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H3: Fossil".

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 23872

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Special Issue Editors

Prof. Dr. Jiusheng Bao

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Guest Editor

School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: mine equipment and its intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qiang Zhang

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Guest Editor

College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Interests: mine equipment and its intelligence

Prof. Dr. Xuewen Wang

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Guest Editor

College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Interests: mine equipment and its intelligence

Dr. Jianjian Yang

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Guest Editor

School of Mechanical Electronic & Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Interests: mine equipment and its intelligence

Special Issue Information

Dear Colleagues,

Coal remains one of the most important energy resources in the world. With the continuous development of science and technology, coal will inevitably be developed by intelligent and even unmanned mining technologies in the future. As the largest producer and consumer of coal around the world, China has begun to vigorously develop intelligent coal mining technology in recent years, and has made outstanding progress in many fields, generating important contributions to promoting the safety and efficiency of coal development and realizing the great goal of "carbon peaking and carbon neutrality". However, due to the harsh underground environment and complex geological conditions of coal mines, the development of intelligent coal mining technology is still difficult and insufficient.

This Special Issue aims to summarize the scientific achievements and progress, and to discuss the newest research hotspots and difficulties around the world—especially in China—in the field of Intelligent Coal Mining Technology. Topics of interest for publication include, but are not limited to:

Intelligent underground coal mining technology;
Intelligent coal transportation technology;
Intelligent mine auxiliary transportation technology;
Intelligent roadway excavation and supporting technology;
Unmanned vehicles of underground and open-pit coal mines;
Underground positioning and navigation technology;
Intelligent coal mine robot technology;
Other intelligent technologies used in coal mines.

Prof. Dr. Jiusheng Bao
Prof. Dr. Qiang Zhang
Prof. Dr. Xuewen Wang
Dr. Jianjian Yang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

intelligent coal mine mining
underground coal mining
coal mine transportation
roadway excavation and supporting
unmanned mine mining vehicles
coal mine robot

Published Papers (14 papers)

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Research

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15 pages, 3835 KiB

Open AccessArticle

Intelligent Identification Method of Shearer Drums Based on Improved YOLOv5s with Dark Channel-Guided Filtering Defogging

by Qinghua Mao, Menghan Wang, Xin Hu, Xusheng Xue and Jiao Zhai

Energies 2023, 16(10), 4190; https://doi.org/10.3390/en16104190 - 19 May 2023

Cited by 4 | Viewed by 884

Abstract

In a fully mechanized mining face, there is interference between the hydraulic support face guard and the shearer drum. The two collisions seriously affect coal mine production and personnel safety. The identification of a shearer drum can be affected by fog generated when the shearer drum cuts forward. It is hydraulic support face guard recovery, not the timely block shearer drum, that will also affect the recognition of the shearer drum. Aiming at the above problems, a shearer drum identification method based on improved YOLOv5s with dark channel-guided filtering defogging is proposed. Aiming at the problem of fog interference affecting recognition, the defogging method for dark channel guided filtering is proposed. The optimal value of the scene transmittance function is calculated using guided filtering to achieve a reasonable defogging effect. The Coordinate Attention (CA) mechanism is adopted to improve the backbone network of the YOLOv5s algorithm. The shearer drum features extracted by the C3 module are reallocated by the attention mechanism to the weights of each space and channel. The information propagation of a shearer drum’s features is enhanced by such improvements. Thus, the detection of shearer drum targets in complex backgrounds is improved. S Intersection over Union (SIoU) is used as a loss function to improve the speed and accuracy of the shearer drum. To verify the effectiveness of the improved algorithm, multiple and improved target detection algorithms are compared. The algorithm is deployed at Huangling II mine. The experimental results present that the improved algorithm is superior to most target detection algorithms. In the absence of object obstruction, the improved algorithm achieved 89.3% recognition accuracy and a detection speed of 48.8 frame/s for the shearer drum in the Huangling II mine. The improved YOLOv5s algorithm provides a basis for identifying interference states between the hydraulic support face guard and shearer drum. Full article

(This article belongs to the Special Issue Intelligent Coal Mining Technology)

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18 pages, 4419 KiB

Open AccessArticle

Coal Gangue Classification Based on the Feature Extraction of the Volume Visual Perception ExM-SVM

by Murad S. Alfarzaeai, Eryi Hu, Wang Peng, Niu Qiang and Maged M. A. Alkainaeai

Energies 2023, 16(4), 2064; https://doi.org/10.3390/en16042064 - 20 Feb 2023

Cited by 1 | Viewed by 1425

Abstract

Computer-vision-based separation methods for coal gangue face challenges due to the harsh environmental conditions in the mines, leading to the reduction of separation accuracy. So, rather than purely depending on the image features to distinguish the coal gangue, it is meaningful to utilize fixed coal characteristics like density. This study achieves the classification of coal and gangue based on their mass, volume, and weight. A dataset of volume, weight and 3_side images is collected. By using 3_side images of coal gangue, the visual perception value of the volume is extracted (

E x M

) to represent the volume of the object. A Support Vector Machine (SVM) classifier receives (

E x M

) and the weight to perform the coal gangue classification. The proposed system eliminates computer vision problems like light intensity, dust, and heterogeneous coal sources. The proposed model was tested with a collected dataset and achieved high recognition accuracy (KNN 100%, Linear SVM 100%, RBF SVM 100%, Gaussian Process 100%, Decision Tree 98%, Random Forest 100%, MLP 100%, AdaBosst 100%, Naive Bayes 98%, and QDA 99%). A cross-validation test has been done to verify the generalization ability. The results also demonstrate high classification accuracy (KNN 96%, Linear SVM 100%, RBF SVM 96%, Gaussian Process 96%, Decision Tree 99%, Random Forest 99%, MLP 100%, AdaBosst 99%, Naive Bayes 99%, and QDA 99%). The results show the high ability of the proposed technique

E x M

-SVM in coal gangue classification tasks. Full article

(This article belongs to the Special Issue Intelligent Coal Mining Technology)

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16 pages, 4422 KiB

Open AccessArticle

Coal Mine Belt Conveyor Foreign Objects Recognition Method of Improved YOLOv5 Algorithm with Defogging and Deblurring

by Qinghua Mao, Shikun Li, Xin Hu and Xusheng Xue

Energies 2022, 15(24), 9504; https://doi.org/10.3390/en15249504 - 14 Dec 2022

Cited by 5 | Viewed by 1765

Abstract

The belt conveyor is the main equipment for underground coal transportation. Its coal flow is mixed with large coal, gangue, anchor rods, wooden strips, and other foreign objects, which easily causes failure of the conveyor belt, such as scratching, tearing, and even broken belts. Aiming at the problem that it was difficult to accurately identify the foreign objects of underground belt conveyors due to the influence of fog, high-speed operation, and obscuration, the coal mine belt conveyor foreign object recognition method of improved YOLOv5 algorithm with defogging and deblurring was proposed. In order to improve the clarity of the monitoring video of the belt conveyor, the dark channel priori defogging algorithm is applied to reduce the impact of fog on the clarity of the monitoring video, and the image is sharpened by user-defined convolution method to reduce the blurring effect on the image in high-speed operation condition. In order to improve the precision of foreign object identification, the convolution block attention module is used to improve the feature expression ability of the foreign object in the complex background. Through adaptive spatial feature fusion, the multi-layer feature information of the foreign object image is more fully fused so as to achieve the goal of accurate recognition of foreign objects. In order to verify the recognition effect of the improved YOLOv5 algorithm, a comparative test is conducted with self-built data set and a public data set. The results show that the performance of the improved YOLOv5 algorithm is better than SSD, YOLOv3, and YOLOv5. The belt conveyor monitoring video of resolution for 1920 × 1080 in Huangling Coal Mine is used for identification verification, the recognition accuracy can reach 95.09%, and the recognition frame rate is 56.50 FPS. The improved YOLOv5 algorithm can provide a reference for the accurate recognition of targets in a complex underground environment. Full article

(This article belongs to the Special Issue Intelligent Coal Mining Technology)

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15 pages, 6773 KiB

Open AccessArticle

ECViST: Mine Intelligent Monitoring Based on Edge Computing and Vision Swin Transformer-YOLOv5

by Fan Zhang, Jiawei Tian, Jianhao Wang, Guanyou Liu and Ying Liu

Energies 2022, 15(23), 9015; https://doi.org/10.3390/en15239015 - 29 Nov 2022

Cited by 3 | Viewed by 2241

Abstract

Mine video surveillance has a key role in ensuring the production safety of intelligent mining. However, existing mine intelligent monitoring technology mainly processes the video data in the cloud, which has problems, such as network congestion, large memory consumption, and untimely response to regional emergencies. In this paper, we address these limitations by utilizing the edge-cloud collaborative optimization framework. First, we obtained a coarse model using the edge-cloud collaborative architecture and updated this to realize the continuous improvement of the detection model. Second, we further proposed a target detection model based on the Vision Swin Transformer-YOLOv5(ViST-YOLOv5) algorithm and improved the model for edge device deployment. The experimental results showed that the object detection model based on ViST-YOLOv5, with a model size of only 27.057 MB, improved the average detection accuracy is by 25% compared to the state-of-the-art model, which makes it suitable for edge-end deployment in mining workface. For the actual mine surveillance video, the edge-cloud collaborative architecture can achieve better performance and robustness in typical application scenarios, such as weak lighting and occlusion, which verifies the feasibility of the designed architecture. Full article

(This article belongs to the Special Issue Intelligent Coal Mining Technology)

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17 pages, 3322 KiB

Open AccessArticle

Research on Location Estimation for Coal Tunnel Vehicle Based on Ultra-Wide Band Equipment

by Xiaoming Yuan, Yueqi Bi, Mingrui Hao, Qiang Ji, Zhigeng Liu and Jiusheng Bao

Energies 2022, 15(22), 8524; https://doi.org/10.3390/en15228524 - 15 Nov 2022

Cited by 5 | Viewed by 1226

Abstract

Because the road surfaces of the underground roadways in coal mines are slippery, uneven, with dust and water mist, and the noise and light illumination effects are significant, global positioning system (GPS) signals cannot be received, which seriously affects the ability of the odometer, optical camera and ultrasonic camera to collect data. Therefore, the underground positioning of coal mines is a difficult issue that restricts the intellectualization of underground transportation, especially for automatic robots and automatic driving vehicles. Ultra-wide band (UWB) positioning technology has low power consumption, high performance and good positioning effects in non-visual environments. It is widely used in coal mine underground equipment positioning and information transmission. In view of the above problems, this research uses the WLR-5A mining unmanned wheeled chassis experimental platform; uses two UWB receivers to infer the position and yaw information of the vehicle in the underground roadway through the method of differential mapping; and tests the vehicle on the double shift line and quarter turn line in the GAZEBO simulation environment and on the ground simulation roadway to simulate the vehicle meeting conditions and quarter turning conditions in the underground roadway. The positioning ability of the method in these two cases is tested. The simulation and test results show that the vehicle position and attitude information deduced by two UWB receivers through the differential mapping method can basically meet the requirements of underground environments when the vehicle is traveling at low speeds. Full article

(This article belongs to the Special Issue Intelligent Coal Mining Technology)

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14 pages, 3319 KiB

Open AccessArticle

Research on Positioning Method of Coal Mine Mining Equipment Based on Monocular Vision

by Rui Yu, Xinqiu Fang, Chengjun Hu, Xiuyu Yang, Xuhui Zhang, Chao Zhang, Wenjuan Yang, Qinghua Mao and Jicheng Wan

Energies 2022, 15(21), 8068; https://doi.org/10.3390/en15218068 - 30 Oct 2022

Cited by 2 | Viewed by 1189

Abstract

In view of the insufficient characteristics and depth acquisition difficulties encountered in the process of uniocular vision measurement, the posture measurement scheme of tunneling equipment based on uniocular vision was proposed in this study. The positioning process of coal mine tunneling equipment based on monocular vision was proposed to extract the environmental features and match the features, and the RANSAC algorithm was used to eliminate the pair of mismatching points. This was done to solve the optimized matching pair and realize the pose estimation of the camera. The pose solution model based on the triangulation depth calculation method was proposed, and the PNP solution method was adopted based on the three-dimensional spatial point coordinates so as to improve the visual measurement accuracy and stability and lay the foundation for the 3D reconstruction of the roadway. This was done to simulate the downhole environment to build an experimental verification platform for monocular visual positioning. The experimental results showed that the position measurement accuracy of the uniocular visual roadheader positioning method within 60 mm and 1.3° could realize the accurate registration of the point cloud in the global coordinate system. The time required for the whole monocular visual positioning was only 179 ms, so it had good real-time performance. Full article

(This article belongs to the Special Issue Intelligent Coal Mining Technology)

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17 pages, 3652 KiB

Open AccessArticle

Multi-Objective Intelligent Decision and Linkage Control Algorithm for Mine Ventilation

by Junqiao Li, Yucheng Li, Wei Zhang, Jinyang Dong and Yunan Cui

Energies 2022, 15(21), 7980; https://doi.org/10.3390/en15217980 - 27 Oct 2022

Cited by 1 | Viewed by 1273

Abstract

A novel bare-bones particle swarm optimization (BBPSO) algorithm is proposed to realize intelligent mine ventilation decision-making and overcome the problems of low precision, low speed, and difficulty in converging on an optimal global solution. The proposed method determines the decision objective function based on the minimal power consumption and maximal air demand. Three penalty terms, namely, dynamic ventilation condition, the supplied air volume at the location where the air is required, and roadway wind speed, are established. The particle construction method of “wind resistance” instead of “wind resistance & air volume” is proposed to reduce the calculation dimension effectively. Three optimization strategies, namely the contraction factor, optimal initial value, and elastic mirror image, are proposed to avoid premature convergence of the algorithm. The application flow of intelligent decision-making in the field and the parallel computing architecture are also discussed. Five methods are used to solve the problems. The results reveal that the improved parallel BBPSO algorithm (BBPSO-Para-Improved) outperforms other algorithms in terms of convergence efficiency, convergence time, and global optimization performance and meets the requirements of large ventilation systems for achieving economic and safety targets. Full article

(This article belongs to the Special Issue Intelligent Coal Mining Technology)

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16 pages, 4947 KiB

Open AccessArticle

Self-Derived Wavelet Compression and Self Matching Reconstruction Algorithm for Environmental Data in Complex Space of Coal Mine Roadway

by Xusheng Xue, Chuanwei Wang, Hongwei Ma, Qinghua Mao, Xiangang Cao, Xuhui Zhang and Guangming Zhang

Energies 2022, 15(20), 7505; https://doi.org/10.3390/en15207505 - 12 Oct 2022

Cited by 3 | Viewed by 1230

Abstract

A crucial assurance for coal mine safety production, prevention and control, and rescue, which is the fundamental tenet of implementing intelligent coal mining, is the safety, stability, and quick transmission of coal mine roadways. However, because of the complex structure of the roadway environment, such as limited and variable space and numerous pieces of equipment, the wireless communication network is affected by the environment, the data transmission channel characteristics are complex and variable, and the existing data transmission methods are weak in adapting to the changing channel. These factors result in poor stability of the transmission of coal mine roadway environment detection data in the wireless communicative network. As a result, this article investigates the wireless communication systems’ real-time transmission in the intricate environmental setting of a coal mine. Based on the application of multiscale wavelet theory in data compression and reconstruction, an adaptive multiscale wavelet compression model based on the wireless data transmission channel is proposed, with an improved Huffman data compression coding algorithm derived from the multiscale wavelet, so that the environmental data meet the wireless communication channel transmission capability. The proposed algorithm boosts the compression ratio and adaptability of environmental data. A self-matching wavelet reconstruction algorithm is developed to achieve real-time and accurate data reconstruction following self-driven wavelet decomposition. The compression and reconstruction experiment performed during real-time wireless transmission of gas concentration data reveals that the original signal’s compression ratio reaches 74% with minor error and high fidelity. The algorithm provides the theoretical foundation for compression and reconstruction in complex coal mine environments for accurate, stable, and real-time data transmission. It is critical for ensuring reliable data transmission in safe production, prevention and control, rescue, and other operations, and it provides theoretical and technical support for intelligent coal mining. Full article

(This article belongs to the Special Issue Intelligent Coal Mining Technology)

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17 pages, 43345 KiB

Open AccessArticle

Numerical Studies on the Flow of Coal Water Slurries with a Yield Stress in Channel Bends

by Yang Liu, Qige Yao, Feng Gao and Yanan Gao

Energies 2022, 15(19), 7006; https://doi.org/10.3390/en15197006 - 24 Sep 2022

Cited by 1 | Viewed by 1130

Abstract

Improving the efficiency of transport of coal water slurries (CWSs) and determining pipe wear both necessitate accurate predictions of flow characteristics in pipelines with complex geometries. At the bends of the channels, the flow is significantly influenced by the bend curvature, flow rate, and the rheological properties of the slurries that are viscoplastic. Herein, we numerically simulated the flow of CWS in curved channels with different curvature ratios, at different flow rates, and using different rheological models, respectively. The results showed that, due to the yield stress on the cross-stream slices, the velocity profiles showed an unyielded plug. The plug deflects outwards in most circumstances, except at the bend core in the highly curved channel, and, at the same time, at the lower conveying rate, which is due to the fact that the larger inner-wall-pointed pressure gradient has to be balanced by large velocities at the inner bend and, hence, the centrifugal effects are weakened at the lower conveying rate. Interestingly, the larger curvature, together with a higher conveying rate, induces a kidney-shaped velocity field at the bend exit, with two separated up and down velocity maximum zones, due to the larger wall shear stresses at the top and bottom than occur in the other cases. The bend brings in a secondary flow consisting of the following: an inward transverse flow at the bend entrance; two Dean swirls in symmetry in the vertical direction at the slices of the bend core and bend exit; and decayed swirls near the outlet. As the curvature ratio increases, the location of the strongest swirls switches from the bend core to the bend exit, since the flow in the highly curved channel requires a longer distance to fully develop the vortices. Decrease in the yield stress and decrease in the consistency index induce a shrinkage of the plug and enhance the streamwise flow and, thus, decrease the cross-stream secondary flow, especially in the channel with the larger curvature. Full article

(This article belongs to the Special Issue Intelligent Coal Mining Technology)

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17 pages, 2826 KiB

Open AccessArticle

Mine Intelligent Receiver: MIMO-OFDM Intelligent Receiver for Mine Information Recovery

by Anyi Wang, Zhiyuan Feng, Xuhong Li and Yong Pan

Energies 2022, 15(18), 6550; https://doi.org/10.3390/en15186550 - 7 Sep 2022

Cited by 1 | Viewed by 1281

Abstract

With the advancement of an intellectual and numerical society, the coal mining industry has also begun to change to intelligence. As an important aspect of intelligent coal mine construction, coal mine communication has put forward more stringent standards for communication quality. For the complex communication environment in mines, the transmission of communication signals is always damaged by various noises and interferences, resulting in serious distortion of the communication signals received at the receiving end. Therefore, the use of traditional receivers for information recovery has the problem of high bit error rate (BER), which cannot meet the standard of intelligent coal mine construction. Based on this, the aim of this research is to combine convolutional neural networks (CNN) and multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) communication systems to design an intelligent receiver model for complex mine communication systems. At the receiver side, CNNs are used to take the place of all the information processing processes. First, features are extracted from the received IQ signal by the convolutional neural network, and then the original information bit is recovered using a multi-label classifier to finally realize end-to-end information restoration. The experimental results show that the intelligent receiver model designed in this research has more accurate information recovery capability in the complex mine channel environment compared with the traditional receiver. In addition, they also verify that the intelligent receiver can still recover information effectively when the traditional receiver cannot recover information properly in the case of partial loss of received data. Full article

(This article belongs to the Special Issue Intelligent Coal Mining Technology)

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17 pages, 12178 KiB

Open AccessArticle

Research on Underground Coal Mine Map Construction Method Based on LeGO-LOAM Improved Algorithm

by Guanghui Xue, Ruixue Li, Shuang Liu and Jinbo Wei

Energies 2022, 15(17), 6256; https://doi.org/10.3390/en15176256 - 27 Aug 2022

Cited by 11 | Viewed by 2245

Abstract

The application of intelligent equipment and technologies such as robots and unmanned vehicles is an important part of the construction of intelligent mines, and has become China’s national coal energy development strategy and the consensus of the coal industry. Environment perception and instant positioning is one of the key technologies destined to realize unmanned and autonomous navigation in underground coal mines, and simultaneous location and mapping (SLAM) is an effective method of deploying this key technology. The underground space of a coal mine is long and narrow, the environment is complex and changeable, the structure is complex and irregular, and the lighting is poor. This is a typical unstructured environment, which poses a great challenge to SLAM. This paper summarizes the current research status of underground coal mine map construction based on visual SLAM and Lidar SLAM, and analyzes the defects of the LeGO-LOAM algorithm, such as loopback detection errors or omissions. We use SegMatch to improve the loopback detection module of LeGO-LOAM, use the iterative closest point (ICP) algorithm to optimize the global map, then propose an improved SLAM algorithm, namely LeGO-LOAM-SM, and describe its principle and implementation. The performance of the LeGO-LOAM-SM was also tested using the KITTI dataset 00 sequence and SLAM experimental data collected in two coal mine underground simulation scenarios, and the performance indexes such as the map construction effect, trajectory overlap and length deviation, absolute trajectory error (ATE), and relative pose error (RPE) were analyzed. The results show that the map constructed by LeGO-LOAM-SM is clearer, has a better loopback effect, the estimated trajectory is smoother and more accurate, and the translation and rotation accuracy is improved by approximately 5%. This can construct more accurate point cloud map and low drift position estimation, which verifies the effectiveness and accuracy of the improved algorithm. Finally, to satisfy the navigation requirements, the construction method of a two-dimensional occupancy grid map was studied, and the underground coal mine simulation environment test was carried out. The results show that the constructed raster map can effectively filter out outlier noise such as dynamic obstacles, has a mapping accuracy of 0.01 m, and the required storage space compared with the point cloud map is reduced by three orders of magnitude. The research results enrich the SLAM algorithm and implementation in unstructured environments such as underground coal mines, and help to solve the problems of environment perception, real-time positioning, and the navigation of coal mine robots and unmanned vehicles. Full article

(This article belongs to the Special Issue Intelligent Coal Mining Technology)

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14 pages, 3549 KiB

Open AccessArticle

Research on Autonomous Cutting Method of Cantilever Roadheader

by Ziyue Xu, Minfu Liang, Xinqiu Fang, Gang Wu, Ningning Chen and Yang Song

Energies 2022, 15(17), 6190; https://doi.org/10.3390/en15176190 - 25 Aug 2022

Cited by 4 | Viewed by 1391

Abstract

Roadway excavation is the leading project in coal mining, and the cantilever roadheader is the main equipment in roadway excavation. Autonomous cutting by cantilever roadheaders is the key to realize safe, efficient and intelligent tunneling for underground roadways. In this paper, the working device of a cantilever roadheader was simplified into a series of translation or rotation joints, and the spatial pose model and spatial pose coordinate system of the roadheader were established. Using the homogeneous transformation matrix and the robot-related theory, the space pose transformation matrix of the roadheader and the space pose equation of the cutting head of the roadheader were derived. The forward kinematics and inverse kinematics of the cutting head were solved by using the D-H parameter method and an inverse transformation method. The location coordinates of the inflection point of the cutting process path for a rectangular roadway were determined, and cutting path planning and control were carried out based on the inflection point coordinates. Finally, MATLAB software was used to simulate the limit cutting area of the cutting head and the cutting process path. The simulation results showed that the limit cutting section had a bulging waist shape, the boundary around the roadway was flat, and the roadway cutting error was controlled within 1mm, which verified the reliability and effectiveness of the autonomous cutting theory of the roadheader. It lays a mathematical model and theoretical foundation for the realization of “autonomous operation of unmanned tunneling equipment”. Full article

(This article belongs to the Special Issue Intelligent Coal Mining Technology)

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16 pages, 4664 KiB

Open AccessArticle

PCViT: A Pre-Convolutional ViT Coal Gangue Identification Method

by Jianjian Yang, Boshen Chang, Yuzeng Zhang, Yucheng Zhang and Wenjie Luo

Energies 2022, 15(12), 4189; https://doi.org/10.3390/en15124189 - 7 Jun 2022

Cited by 10 | Viewed by 1593

Abstract

For the study of coal and gangue identification using near-infrared reflection spectroscopy, samples of anthracite coal and gangue with similar appearances were collected, and different dust concentrations (200 ug/m³, 500 ug/m³ and 800 ug/m³), detection distances (1.2 m, 1.5 m and 1.8 m) and mixing gangue rates (one-third coal, two-thirds coal, full coal) were collected in the laboratory by the reflection spectroscopy acquisition device and the gangue reflection spectral data. The spectral data were pre-processed using three methods, first-order differentiation, second-order differentiation and standard normal variable transformation, in order to enhance the absorption characteristics of the reflectance spectra and to eliminate the effects of changes in the experimental environment. The PCViT gangue identification model is established, and the disadvantages of the violent patch embedding of the ViT model are improved by using the stepwise convolution operation to extract features. Then, the interdependence of the features of the hyperspectral data is modeled by the self-attention module, and the learned features are optimized adaptively. The results of gangue recognition under nine working conditions show that the proposed recognition model can significantly improve the recognition accuracy, and this study can provide a reference value for gangue recognition using the near-infrared reflection spectra of gangue. Full article

(This article belongs to the Special Issue Intelligent Coal Mining Technology)

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Review

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26 pages, 4892 KiB

Open AccessReview

Research Status and Development Trend of Unmanned Driving Technology in Coal Mine Transportation

by Maosen Wang, Jiusheng Bao, Xiaoming Yuan, Yan Yin and Shah Khalid

Energies 2022, 15(23), 9133; https://doi.org/10.3390/en15239133 - 2 Dec 2022

Cited by 5 | Viewed by 2567

Abstract

Unmanned driving technology has always been one focus of mine smart transportation, which is a crucial component of smart mines. However, the descriptions of both the intelligent transportation system and its industrial application are not comprehensive. In order to have an all-encompassing and comprehensive understanding of both the intelligent coal mine transportation system and its industrial application with the intelligent system, this paper summarizes and analyzes the current research status of unmanned driving technology in mines and the industrial application of current mining transportation vehicles. It begins by outlining the current research state of unmanned driving technology in mines and then assesses the advancement of unmanned driving technology. The unmanned transportation system in mines is also introduced, together with its components for perception, location, path planning, vehicle control, and multi-vehicle scheduling. In addition, each component is described in combination with the current artificial intelligent unmanned technology individually. Then, some typical categories of intelligent industrial vehicles are introduced for learning about the conditions of their actual application. There are almost four hundred coal mines that have researched unmanned driving technology, and some companies have applied the unmanned technology to realize transportation with an efficiency enhancement of 70~80%. Finally, currently existing challenges and future research are analyzed and proposed. This review may provide more comprehensive knowledge of the intelligent coal mine, accelerating the development of intelligent technology and helping to build a new management and control model. Full article

(This article belongs to the Special Issue Intelligent Coal Mining Technology)

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