工矿自动化

Development of innovation team construction and key technology research in coal mine intelligence

WANG Guofa, FU Jiaxing, MENG Lingyu

2022, 48(12): 1-15. doi: 10.13272/j.issn.1671-251x.18060

<Abstract>(665) <HTML> (91) <PDF>(132)

Abstract:
This paper analyzes the outstanding technical weaknesses in the intelligent construction of coal mines in China. This paper puts forward 12 innovative research and development directions, including intelligent system design, information infrastructure, intelligent geological support, intelligent mining and transportation, intelligent safety prediction and early warning, intelligent power supply and intelligent water affairs, intelligent washing, intelligent parks, intelligent open-pit coal mines, intelligent well construction, intelligent equipment and robots, and emergency rescue and supervision. 46 coal mine intelligent innovation teams have been established, forming the strongest team of coal mine intelligent innovation. The target collaboration, organization collaboration, knowledge collaboration and business collaboration models of the innovation chain and industry chain are established. Relying on 136 provincial experimental platforms, this study takes the lead in obtaining a number of innovative achievements in the important technical direction of the intelligent coal mine. This paper provides technical support for the construction of intelligent coal mine in our country. This study puts forward 9 key breakthrough directions to continuously promote the intelligent construction of coal mines and improve the technical equipment support capacity. It is suggested to continuously build the intelligent support technology system of coal mine, accelerate the construction of the intelligent technology standard system of coal mine and the construction benefit evaluation system, and accelerate the research and application of the intelligent "MineCloud" of coal mine. It is suggested to develop complete sets of intelligent mining process equipment for large mining height caving coal, strengthen the research and development of intelligent fast heading technology and equipment, and develop highly reliable and stable intelligent mine sensors. It is suggested to accelerate the research and development of key technologies of coal mine robot cluster, accelerate the construction of intelligent washing complete equipment and process package development, and strengthen the advanced and mature intelligent process and the stability and high reliability of core equipment in open-pit coal mines.

Binocular vision-based displacement detection method for anchor digging robot

MA Hongwei, CHAO Yong, XUE Xusheng, MAO Qinghua, WANG Chuanwei

2022, 48(12): 16-25. doi: 10.13272/j.issn.1671-251x.2022100066

<Abstract>(773) <HTML> (57) <PDF>(48)

Abstract:
The problem of low detection accuracy of driving displacement exists in the driving process of anchor digging robots. In order to solve the above problem, taking the supporting bolt as the positioning benchmark, by analyzing the distance relationship between the anchor digging robot and the supporting bolt, the positioning model of "anchor digging robot-supported anchor" is established. This paper proposes a binocular vision-based displacement detection method for anchor digging robots. Due to the complexity of the underground coal mine environment, the disparity map obtained by using the traditional Census transform algorithm has limitations. By analyzing the binocular vision ranging principle, an improved Census transform algorithm is proposed to obtain the disparity map of the anchor and the depth information of the anchor image. This paper presents a method of anchor feature recognition and positioning, and uses edge detection algorithm to extract the anchor contour in disparity map. The minimum circumscribed rectangle and the maximum circumscribed rectangle algorithm are used to frame the anchor outline and extract the pixel coordinates of anchor feature points. By analyzing coordinate conversion relationships, the pixel coordinates of feature points are converted to world coordinates. By using the least square method, the spatial coordinates of feature points are fitted into a straight line. The plane parallel to the roadway section is established through the straight line. The distance between the binocular camera and the plane is calculated, and then the distance between the anchor digging robot and the plane is obtained. A mobile robot platform is set up to carry out the displacement detection experiment of the anchor digging robot. The results show the following points. The improved Census transform algorithm reduces the mismatch rate from 19.85% to 11.52%, which is 41.96% lower than the traditional Census transform algorithm. The method of anchor feature point recognition and positioning can effectively extract the spatial coordinates of anchor feature points. The distance between the camera and the three parallel sections is 3 010.428, 2 215.910, 1 415.127 mm respectively through straight line fitting. In the robot positioning experiment, the real calculated displacement is compared with the theoretical displacement. The results show that the real calculated displacement curve coincides with the theoretical displacement curve basically. The error between the theoretical displacement and the calculated displacement is less than 20 mm. The autonomous, accurate and real-time displacement detection of the anchor digging robot can be realized.

Foreign object recognition of belt conveyor in coal mine based on improved YOLOv7

MAO Qinghua, LI Shikun, HU Xin, XUE Xusheng, YAO Lijie

2022, 48(12): 26-32. doi: 10.13272/j.issn.1671-251x.2022100011

<Abstract>(815) <HTML> (182) <PDF>(229)

Abstract:
The coal flow of the belt conveyor will be mixed with anchor rod, angle iron, wood, gangue, and lump coal. This will easily lead to the tearing of the conveyor belt, the blockage of the transition and even the breakage of the belt. It is difficult for the inspection robot of the belt conveyor to efficiently and accurately recognize foreign objects in the environment of uneven lighting and high-speed running of the belt conveyor. The model deployment is inconvenient. The YOLOv7 model has a high capability to extract target features, but its recognition speed is slow. In order to solve the above problems, a foreign object recognition method of belt conveyor in coal mine based on improved YOLOv7 is proposed. The method of adaptive histogram equalization with limited contrast is used to enhance the collected monitoring image of the belt conveyor to improve the clarity of object contour in the image. The YOLOv7 model is improved by introducing a simple and parameter-free attention module into the backbone extraction network. The improved model can improve the model's anti-interference capability against the complex background of the image and the capability to extract foreign object features. The depthwise separable convolution is introduced to replace the ordinary convolution in the backbone feature extraction network to improve the speed of foreign object recognition. TensorRT engine is used to convert the improved YOLOv7 model after training and deploy it on NVIDIA Jetson Xavier NX, realizing the acceleration of the model. The video of the belt conveyor with the resolution of 1 920 × 1 080 in the underground coal mine is recognized. The experimental results show that the recognition effect of improved YOLOv7 is better than YOLOv5L and YOLOv7. The recognition accuracy rate is 92.8%, and the recognition speed is 25.64 frames/s, meeting the requirements of accurate and efficient recognition of foreign objects in the belt conveyor.

Design of foreign object recognition and positioning system for sorting robot of coal mine belt conveyor

XUE Xusheng, YANG Xingyun, QI Guanghao, MA Hongwei, MAO Qinghua, SHANG Xinmang

2022, 48(12): 33-41. doi: 10.13272/j.issn.1671-251x.2022100024

<Abstract>(1247) <HTML> (79) <PDF>(78)

Abstract:
Machine vision has a certain theoretical basis in target detection and recognition for sorting robot of coal mine belt conveyor. But current target recognition of sorting robot of coal mine belt conveyor is mainly aimed at coal-gangue recognition. There are few kinds of research on the recognition of foreign object targets causing conveyor belt penetration and tearing, and also few kinds of research on the precise positioning of target foreign object. In order to solve the above problems, a foreign object recognition and positioning system based on machine vision for sorting robots of coal mine belt conveyor is designed. The system can recognize and position different types and shapes of foreign objects on the conveyor belt. The image information of the foreign objects on the conveyor belt in real-time is obtained by adopting binocular vision, and the image is preprocessed. Image information is enhanced based on the Canny operator. The gray stretching method is used to improve image edge information to highlight the edge features of foreign objects on coal mine belt conveyor. The morphological method is used to extract foreign object shape features, and establish foreign object image feature sample library. The image feature matching method is used to solve the existing area of foreign objects to realize the detection, classification and recognition of foreign objects. On the basis of the successful recognition of foreign object type, the region of interest (ROI) of the target foreign object is established based on the edge feature value of the target foreign object. The coordinate conversion relationship is built between the camera, conveyor belt and target foreign object. The fast multi-target centroid calculation method is used to obtain the centroid coordinate of the target foreign object, so as to realize the positioning of the target foreign object. The experimental result of the system prototype shows that the foreign object recognition rate of foreign object recognition and positioning system for sorting robot of coal mine belt conveyor is not affected by the size, material, color and other factors, the system can realize the image acquisition, process, feature extraction, recognition and positioning of the target foreign object of coal mine conveyor belt. The recognition rate is above 92.5%, and the average error of the target foreign object positioning is about 3%.

Research progress and key technologies of intelligent coal-gangue sorting robot

ZHANG Ye, MA Hongwei, WANG Peng, CAO Xiangang, WEI Xiaorong, ZHOU Wenjian

2022, 48(12): 42-48, 56. doi: 10.13272/j.issn.1671-251x.2022100048

<Abstract>(1262) <HTML> (74) <PDF>(111)

Abstract:
The gangue is wrapped by slurry in underground coal mine, which causes difficult coal-gangue recognition and sorting. The underground working space is narrow, so the equipment layout is difficult, and the diversion of coal-gangue is difficult. Therefore, developing a high-performance, highly reliable intelligent coal-gangue sorting robot is necessary. The paper analyzes the research status of coal-gangue recognition, robot trajectory plan and multi-dynamic-target multi-robot collaborative control technology of intelligent coal-gangue sorting robot. This paper points out that the coal-gangue sorting work environment is complex, and its weight and shape of coal-gangue are irregular and randomly distributed. Therefore, the three key technologies for intelligent coal-gangue sorting robot are recognition and grasping features extraction of coal-gangue in complex environment, stable and reliable grasping of coal-gangue in unstructured environment, and intelligent collaborative sorting of multi-target multi-robot. It is proposed that in order to realize the intelligent sorting of coal-gangue by the robot, further research should be carried out. The research includes the methods of coal-gangue recognition and sorting feature extraction suitable for underground, accurate positioning and synchronous tracking of dynamic targets, online trajectory planning of mechanical arms, and intelligent collaborative control of multiple mechanical arms. By soring out the above three key technologies, it can be concluded as follows. The construction and expansion of coal-gangue data set, recognition and grasping features extraction of coal-gangue are the key technologies to achieve efficient coal-gangue recognition. Precise tracking of dynamic coal-gangue, trajectory planning of synchronous tracking dynamic target of mechanical arm and fast and stable grasping of large quality targets are the key technologies to realize stable coal-gangue grasping by mechanical arms. Multi-task efficient allocation, anti-collision path planning and intelligent collaborative control are the key technologies to achieve efficient intelligent collaborative sorting of multiple mechanical arms. According to the common problems at present, this paper puts forward the solutions. In the aspect of recognition, the method of coal-gangue recognition and grasping feature extraction based on multi-mode deep learning is studied to realize fast coal-gangue recognition suitable for the underground. In the aspect of trajectory planning, the precise positioning and real-time tracking methods of dynamic coal-gangue are studied to realize the adaptive and stable grasping of dynamic coal-gangue by the robot. In the aspect of collaborative sorting, a multi-layer multiple mechanical arms collaborative control model is built to achieve efficient intelligent collaborative sorting of multiple mechanical arms in the complex environment.

Coal mine roadway environment-oriented LiDAR and IMU fusion positioning and mapping method

MA Aiqiang, YAO Wanqiang, LIN Xiaohu, ZHANG Liandui, ZHENG Junliang, WU Mouda, YANG Xin

2022, 48(12): 49-56. doi: 10.13272/j.issn.1671-251x.2022070007

<Abstract>(330) <HTML> (31) <PDF>(50)

Abstract:
The failure of autonomous navigation, positioning and mapping of the mobile robot is caused by the shotcrete surface and symmetrical roadway in coal mine. In order to solve this problem, a real-time positioning and mapping method based on LiDAR and IMU fusion is proposed for the roadway environment in the coal mine. Firstly, the original point cloud is segmented. The IMU pre-integration pose is used to remove the nonlinear motion distortion of the original point cloud. The line and surface feature extraction is carried out on the obtained point cloud. Secondly, the line and surface features of adjacent frames are matched. The initial pose value obtained by IMU pre-integration is fused in the hierarchical pose estimation process. The calculation iteration times are reduced, the matching precision of feature points is improved, and the pose of the current frame is solved. Finally, the local map factor, IMU factor and key frame factor are inserted into the factor graph to optimize and constrain the pose. The key frame is matched with the local map, and the map construction is realized through an octree structure. In order to verify the positioning performance and mapping effect of the proposed method, the experimental platforms of Autolabor, VLP-16 LiDAR and Ellipse-N IMU are built. The qualitative and quantitative comparison between the proposed method and LeGO-LOAM and LIO-SAM methods is carried out. The results show the following points. ① In the coal mine roadway environment, the average and median of the absolute positioning error in the three axes direction of the real-time positioning and mapping method based on LiDAR and IMU fusion are less than 32 cm. The position and attitude estimation precision in the X-axis is the highest, with a cumulative error of 1.65 m and a position deviation of 2.97 m. The overall mapping effect is good, and the mapping track does not drift. The point cloud map constructed has excellent performance in integrity and geometric structure authenticity. The map can directly reflect the actual situation of the roadway environment, and has good robustness. This is because hierarchical pose estimation is performed after point cloud matching. The multi-factor optimization can effectively reduce the global cumulative error, which plays an important role in improving track precision and map consistency. ② In the corridor environment, the three-axis error of the real-time positioning and mapping method based on LiDAR and IMU fusion for the coal mine roadway environment is less than 1.01 m. The average error is 5~15 cm, with small error range and high precision. The accumulated position deviation is only 1.67 m. Integrity and environment matching have good performance. This is because by adding keyframe factors and inserting factor graphs to optimize the related variables of the newly added nodes, the drift of pose estimation is reduced. The positioning and mapping precision is relatively high.

3D map construction of coal mine roadway mobile robot based on integrated factor graph optimization

ZOU Xiaoyu, HUANG Xinmiao, WANG Zhongbin, FANG Dongsheng, PAN Jie, SI Lei

2022, 48(12): 57-67, 92. doi: 10.13272/j.issn.1671-251x.2022100041

<Abstract>(357) <HTML> (198) <PDF>(45)

Abstract:
The working precision of mobile robots in coal mines seriously depends on the accuracy of simultaneous localization and mapping (SLAM) technology. There are some problems such as feature missing and poor lighting conditions in long and straight underground roadway. The problems lead to the failure of the laser odometer and visual odometer. The result limits the effective application of traditional SLAM method in coal mine roadway. At present, the research of the SLAM method mainly focuses on the multi-sensor fusion mapping method. There is a lack of research on the improvement of the mapping precision of the laser SLAM method. In order to solve the above problems, facing the mapping requirements of mobile robot in coal mine roadway, a 3D map construction method of coal mine roadway mobile robot based on integrated factor graph optimization is proposed. The method adopts the strategy of front-end construction and back-end optimization. The method designs a front-end point cloud registration module and a back-end construction method based on filtering and graph optimization. Therefore, the mapping result is more accurate and adaptable. The environmental degradation in coal mine long and straight roadway leads to the low registration precision of 3D laser point cloud. In order to solve the above problem, integrating iterative closest point (ICP) and normal-distributions transform (NDT) algorithms, taking into account the geometric characteristics and probability distribution characteristics of point clouds, an integrated front-end point cloud registration module is designed, which realizes the accurate registration of point clouds. Inview of the back-end optimization problem of 3D laser SLAM, the back-end construction method based on pose map and factor map optimization is studied. The factor map optimization model integrating ICP and NDT relative pose factors is constructed to accurately estimate the pose of the mobile robot. The performance of the proposed method of 3D map construction under different working conditions is verified by using the open dataset KITTI and the simulated roadway point cloud dataset. The experimental results on the open dataset KITTI show the following points. In terms of global consistency, this method has similar performance with the traditional A-LOAM method based on feature point matching and the LeGO-LOAM method based on plane segmentation and feature point extraction. It is superior to the other two methods in the local precision of mapping. The experimental results on the simulated roadway point cloud dataset show the following points. This method has significant advantages, through factor map optimization, a 3D map with high consistency can be obtained. The precision and robustness of 3D map construction of coal mine roadway are improved. The problems of the feature point missing and laser odometer failure in long straight underground roadway are solved.

LiDAR/IMU tightly-coupled SLAM method for coal mine mobile robot

LI Menggang, HU Eryi, ZHU Hua

2022, 48(12): 68-78. doi: 10.13272/j.issn.1671-251x.2022100061

<Abstract>(436) <HTML> (80) <PDF>(66)

Abstract:
SLAM (Simultaneous Localization and Mapping) of the underground robot is a hot research topic at present. But the research on improving the precision and robustness of laser SLAM in underground complicated conditions is still insufficient. The traditional laser SLAM method has the problems of rapidly increasing cumulative error, poor robustness of the rotation process and high error rate of feature correlation under complex underground environment. The existing laser inertial fusion location mapping tightly-coupled fusion mechanism still needs to further improve the adaptability to the complex environment in coal mines. In order to solve the above problems, a LiDAR (lidar)/IMU (inertial measurement unit) tightly-coupled SLAM (LI-SLAM) method for coal mine robot is proposed. Firstly, the IMU observation information is used to predict the point cloud motion state and make effective compensation to reduce the point cloud distortion caused by severe vibration, rapid rotation and other severe motion conditions. Secondly, the edge and plane features of the radar point cloud are extracted. The laser relative pose constraints are constructed based on point line and point surface scanning matching. In vector space and manifold space, the construction process of residual, Jacobian matrix and covariance matrix of constraints is derived analytically. Finally, the LiDAR/IMU tight coupling is completed based on the factor graph optimization method by constructing the radar relative pose constraint factor, IMU pre-integration constraint factor and loopback detection constraint factor. The localization and map construction of the mine mobile robot in the complex underground environment is realized. In order to verify the precision and robustness of the LI-SLAM method in the bumpy road and complex scenario, experiments are carried out in the field and underground garage environment based on the platform of wheeled mobile robot in the coal mine. The industrial experiments are carried out in Tashan Coal Mine of Jinneng Group. The results are compared with the current optimal LiDAR odometry and mapping (LOAM) method, lidar-inertial state estimator (LINS) method and lidar inertial odometry and mapping (LIO-mapping) method. The test results in field bumpy road show the following points. The map consistency of the LI-SLAM method and the LOAM method is the best, which is basically consistent with the real route. The LI-SLAM method has better adaptability to rotation, and the distance error is the minimum. The LIO-mapping method cannot run in real time. The method can obtain complete trajectory at 0.5 times. However, in the initial motion phase, there is a large degree of direction deviation, and the initialization process is easy to fail. Because LINS only uses the latest observation information, it drifts under complex terrain. The test results in underground garage environment show the following points. Compared with the LOAM method, LINS method and LIO-mapping method, the LI-SLAM method has higher modeling precision. The local refinement is higher, and the motion trajectory is smoother. The industrial test results in underground coal mines show the following points. The LI-SLAM method can operate stably and online in various terrain environments. The result meets the requirements of robustness and real-time. When the straight-line distance of the roadway where the coal mine mobile robot drives on is 273 m, 30 groups of distance results are analyzed, and the average error is less than 15 cm. It has high positioning and modeling precision. It basically meets the positioning and modeling precision requirements of coal mine mobile robots. It has better applicability for precise positioning and mapping of mobile robot in the complex environment of the coal mine.

Research on tightly combined positioning method of coal mine robot based on UWB and IMU

YU Lu, TANG Chaoli, HUANG Yourui, HAN Tao, XU Shanyong, FU Jiahao

2022, 48(12): 79-85. doi: 10.13272/j.issn.1671-251x.2022070058

<Abstract>(1152) <HTML> (63) <PDF>(54)

Abstract:
The underground coal mine environment is complex. The existing coal mine robot positioning methods have low positioning precision and low real-time performance caused by the non-line-of-sight (NLOS) error and other factors. In order to solve the above problems, a tightly combined positioning method of coal mine robot based on UWB (Ultra Wide Band) and IMU (Inertial Measurement Unit) is proposed. Firstly, the UWB module is used to measure distance between the coal mine robot and UWB base station. The least square support vector machine (LSSVM) model is trained by using real value and measured value of the distance between the coal mine robot and the UWB base station, and the modified LSSVM model is obtained. Secondly, the measured value measured by the UWB module during the positioning process of the coal mine robot is used as the input of the modified LSSVM model. The modified LSSVM model is used to correct the measured value of UWB, reduce the influence of NLOS error on positioning precision, and obtain more accurate distance information. Finally, the range information modified by modified LSSVM model is used as the measurement input of error-state Kalman filter (ESKF). The measurement equation is formed with the position information solved by inertial navigation. The ESKF is used to tightly combine the UWB ranging correction value with the range information calculated by the inertial navigation to complete the state update. The more precise position information of the coal mine robot is obtained, and the precise positioning of the coal mine robot is achieved. The simulation results under different layont schemes of UWB base stations show that using modified LSSVM model can make the UWB range information more accurate, and improve the positioning precision. In the static positioning experiment, when the four UWB base stations are symmetrically distributed at the same height, the root mean square error of the positioning is reduced from 0.146 4 m to 0.139 8 m. When the four UWB base stations are distributed symmetrically with unequal heights, the root mean square error decreases from 0.300 8 m to 0.200 6 m. When the four base stations are distributed irregularly, the root mean square error decreases from 0.317 5 m to 0.314 2 m. Therefore, in actual scenarios, the UWB base stations should be arranged symmetrically at the same height as possible. In the dynamic positioning experiment, the fusion positioning trajectory corrected by the modified LSSVM model is closer to the real trajectory of the coal mine robot than the fusion positioning trajectory before correction. The result verifies that the tightly combined positioning method can reduce the NLOS error and improve positioning precision.

Research on key technologies of coal mine roadway dust cleaning robot

LI Shijun, REN Huaiwei, ZHANG Desheng, MA Ziyan, ZHOU Jie, ZHAO Shuji, DU Ming

2022, 48(12): 86-92. doi: 10.13272/j.issn.1671-251x.2022100076

<Abstract>(659) <HTML> (57) <PDF>(43)

Abstract:
Roadway dust cleaning robot can effectively solve the problem of coal mine roadway dust accumulation. However, there is no relatively mature product to achieve "automatic dust accumulation monitoring-autonomous/semi-autonomous movement-adaptive dust cleaning operation". This paper analyzes the research status of three kinds of dust cleaning equipment developed at home and abroad, which are wheel-rail type roadway dust cleaning device, explosion-proof sprinkler and tunnel dust cleaning vehicle with chassis and hydraulic mechanical arm. It is pointed out that the wheel-rail type roadway dust cleaning device does not contain power system. The dust-cleaning effect of roadway wall dust is limited. The explosion-proof sprinkler has its own power, which can realize full section dust reduction of the trackless long roadway. However, the jet water surface is wide, and it is unable to deal with local areas with serious dust accumulation such as roadway walls and pipelines. Tunnel dust cleaning vehicle can solve the problem of dust accumulation in long-distance tunnels. However, manual driving and operation are still required, and it is impossible to realize tunnel dust accumulation monitoring and adaptive dust cleaning. Based on the above analysis, it is pointed out that in order to realize "automatic dust accumulation monitoring-autonomous/semi-autonomous movement-adaptive dust cleaning operation", the research should be carried out from the aspects of dust accumulation monitoring, structure design and control of dust cleaning device, and optimization strategy of dust cleaning mode. It is pointed out that the main technical problems faced by the above research include the common problems of coal mine robots such as explosion-proof safety design, underground precise positioning, and long-distance wireless communication. The problems also include the characteristic problems of roadway dust cleaning robot such as dust monitoring, adaptive dust cleaning, and vehicle arm coordinative operation. In view of the characteristic problem of the roadway dust cleaning robot, this paper puts forward the corresponding key technologies. ① It is suggested to research and develop a multi-sensor fusion monitoring technology based on the weighing method, laser method and image method to realize long-term monitoring of dust cleaning in coal mine roadway and dynamic evaluation of dust cleaning effect. ② It is suggested to develop dust cleaning structure based on explosion-proof mechanical arm and "wind-water-brush" linkage dust cleaning device to realize adaptive dust cleaning. ③ It is suggested to establish a unified workspace for vehicle chassis and mechanical arm. It is suggested to develop small deviation automatic compensation and flexible obstacle avoidance technology for roadway dust cleaning robot based on torque control. The vehicle arm coordination of the roadway dust cleaning robot in the dynamic scene is realized.

Research on obstacle avoidance control method of multi-rotor aircraft in coal mine

GUO Aijun, WANG Miaoyun, MA Hongwei, ZHANG Xuhui, XUE Xusheng, DU Yuyang, ZHANG Chao

2022, 48(12): 93-100. doi: 10.13272/j.issn.1671-251x.2022110020

<Abstract>(229) <HTML> (59) <PDF>(27)

Abstract:
Multi-rotor aircraft has a good application prospect in coal mine production inspection because of its advantages of simple structure, hovering and multi-directional flight. However, multi-rotor aircraft moves at a high speed, and the aircraft is easily influenced by various external factors during flying. It is difficult to establish a precise mathematical model. The design of a flight control algorithm is complicated. The existing synchronous positioning and map construction method based on laser radar is difficult to meet the real-time requirement of rapid flying of the multi-rotor aircraft. In view of the above problems, an obstacle avoidance control method of multi-rotor aircraft in coal mine by using remote virtual control technology is studied. The virtual remote control system of the multi-rotor aircraft in coal mine underground roadway is constructed. The virtual roadway model and the global navigation map are established in the virtual remote control system according to the initial information of the coal mine roadway. The known static obstacle information in the moving process of the aircraft is obtained, and the known static environment model is established. The task quantity of environment perception modeling in the moving process of the multi-rotor aircraft is reduced, and the operation efficiency of virtual remote control can be improved. In the inspection process, the multi-rotor aircraft detects dynamic obstacle information in the moving direction through sensing equipment carried by the multi-rotor aircraft. The remote control system reconstructs the dynamic obstacle information in an initial virtual roadway model in real-time according to obstacle data. The virtual environment state is updated in real-time to provide a reliable environment basis for local obstacle avoidance control of the aircraft. The remote control system uses the compound virtual force field (CVFF) obstacle avoidance control algorithm to plan the obstacle avoidance path by reading the positioning data and moving speed information of obstacles and aircraft. If the obstacle in front is detected to pose a great threat to the movement of the aircraft, the remote controller can implement remote intervention on the aircraft according to the planned obstacle avoidance path. The system realizes autonomous obstacle avoidance flight and human remote intervention control. In order to improve the perception efficiency and accuracy of aircraft to dynamic obstacles, a CVFF obstacle avoidance control algorithm is studied based on virtual force field (VFF) algorithm by introducing the influence of relative velocity between aircraft and obstacles and target points. The CVFF obstacle avoidance control algorithm is verified by simulation from two aspects of static and dynamic obstacle avoidance paths. The results show that under static conditions, compared with the VFF algorithm, the CVFF algorithm reduces the number of iterations and also shortens the trajectory length of the aircraft. Under dynamic conditions, the aircraft successfully avoids the two dynamic obstacles set in advance and successfully reaches the set target point. The effectiveness of the obstacle avoidance control method of multi-rotor aircraft using the CVFF algorithm is verified.

High-efficiency gas extraction technology of staged fracturing roof with sand of underground broken and soft coal seam

SUN Siqing, LI Wenbo

2022, 48(12): 101-107. doi: 10.13272/j.issn.1671-251x.18050

<Abstract>(205) <HTML> (71) <PDF>(13)

Abstract:
The gas extraction method of floor cross-layer drilling commonly used in gas control of broken and soft coal seam has problems such as large excavation quantity, long control period, short coal uncovering section of drilling, limited extraction control effect and so on. The gas extraction method of bedding short hole has problems such as poor drill-forming property, short extraction drilling hole, small extraction area and so on. This paper makes statistics on mechanical parameters and in-situ stress of coal seams and their roof and floor surrounding rocks in five typical broken and soft coal seam mining areas in Huaibei, Huainan, Jiaozuo, Jincheng and Yangquan. It is concluded that the elastic modulus of roof rock is 2.56-6.71 times of that of the broken and soft coal seam. The Poisson's ratio is 0.48-0.84 times of that of the coal seam. The analysis shows that roof rock of broken and soft coal seam is characterized by high elastic modulus and low Poisson's ratio. The roof is easier to be fractured than broken and soft coal seam. Referring to the idea of staged fracturing roof with sand of the horizontal borehole of surface coalbed methane, the idea of gas extraction by staged fracturing roof with sand of underground broken and soft coal seam is put forward. The directional long boreholes in stable strata of coal seam roof are constructed. The boreholes are generally less than 10 m away from the coal seam. The sand-carrying fracturing shall be carried out from the inside to the outside section by section in the boreholes. It will form a multi-stage fracture network in which the rock layer fully connects through directional long holes and the coal seam fully connects through fracture network in coal strata. The proppant is used to ensure that the fracturing network is in open state, so as to realize efficient gas extraction in a large area by the directional long hole in roof of broken and soft coal seam. The geological model of fracturing roof with sand in No.3 coal seam of a working face in Shanxi Xinjing Coal Industry Co., Ltd. is established. The numerical simulation of hydraulic fracturing coal seam and roof with sand is carried out by using FracproPT software. The result shows that the fracturing cracks in roof mainly extend to the coal seam in vertical direction. The length of fracturing cracks in roof in horizontal direction is 3.49 times of that of fracturing cracks in coal seam. This result shows that indirectly fracturing roof of broken and soft coal seam is better than directly fracturing the coal seam. Two 609 m directional long boreholes are drilled in roof of the No.3 coal seam in the working face to carry out gas extraction engineering application test of hydraulic staged fracturing with sand. The fracturing influence radius of the two boreholes is 20-38 m. The gas extraction pure amount of the fracturing holes is 1 025.11 m³/d and 2 810.60 m³/d respectively. The gas extraction pure amount of 100 m is 5.6-15.4 times of that of bedding un-fracturing boreholes in the same area. This study realizes high-efficiency gas extraction in a large area of broken and soft coal seam.

Coal mine external fire detection method based on edge intelligence

ZHAO Duan, LI Tao, DONG Yanqiang, WANG Zhiqiang, LIU Chun

2022, 48(12): 108-115. doi: 10.13272/j.issn.1671-251x.2022080046

<Abstract>(306) <HTML> (53) <PDF>(33)

Abstract:
The detection of external fire in coal mines and the reliable identification of initial fire are of great significance for improving the level of coal mine fire detection. It is also an important direction of intelligent mine construction in the future. In order to improve the speed, precision and real-time of coal mine external fire detection, a coal mine external fire detection method based on edge intelligence is proposed. The feature scale of the backbone network of the YOLOv5s model is improved. The model can fully learn the shallow features and improve the small target detection performance. At the same time, an adaptive attention module is added on the basis of the original feature pyramid network (FPN) to improve the detection precision of the model. There are problems of image detection error and missed detection caused by poor light conditions, more dust and camera shooting angle in the underground mine. In order to solve the above problems, the YOLOv5s-as model is constructed by using multi-sensor auxiliary detection and weighting fusion identification of video detection information and multi-sensor detection information through dynamic weighting algorithm. The YOLOv5s-as model is transplanted to the intelligent edge processor, and lightweight processing is carried out to realize the deployment of edge intelligent devices. The experimental results show that the reasoning time of the YOLOv5s-as model is slightly longer than that of the YOLOv5s-a model without sensor information fusion reasoning, but mean value of average precision when the intersection over union is 0.5 (mAP@0.5) is increased by 7.24%. Compared with the YOLOv5s model before transplantation, the mAP@0.5 of the YOLOv5s-as model transplanted to the intelligent edge processor and subjected to lightweight processing increased by 15.04%. For small target fire sources, SSD 300, SSD 512 and YOLOv5s models cannot identify them. The YOLOv5s-a and YOLOv5s-as models can detect small target fire sources with good adaptability. When using the edge processing method, the response period of YOLOv5s as model is 238 ms, which is 38.66% shorter than the centralized processing method.

Analysis of influencing factors of coal mine water inrush accidents based on DEMATEL-ISM-BN

HONG Weibin, SHENG Wu

2022, 48(12): 116-122. doi: 10.13272/j.issn.1671-251x.2022060079

<Abstract>(248) <HTML> (52) <PDF>(16)

Abstract:
The water inrush accident is the third largest coal mine accident after the gas accident and the roof accident. The analysis and exploration of the causes of the water inrush accident and the intrinsic relationship between the various factors can effectively realize the control and containment of the water inrush accident. The existing coal mine water inrush accident research mostly aims at a certain area or a certain aspect. There is a lack of in-depth research on the complex causal relationship among the influence factors and the influence degree of each factor on the accident. In order to solve this problem, decision making trial and evaluation laboratory (DEMATEL) and interpretative structural modeling method (ISM) are used to analyze the influencing factors of coal mine water inrush accidents. The multi-level hierarchical structure model is constructed, which is mapped into the Bayesian network (BN) model. The DEMATEL-ISM-BN model is obtained. Based on the data-driven theory, typical accident cases are studied. There are 18 influencing factors inducing coal mine water inrush accidents determined. Based on expert scoring results, DEMATEL analysis is carried out. The influence degree, influenced degree, cause degree and centrality of each factor are calculated. The reachability matrix of ISM is calculated according to the DEMATEL analysis results. The multi-level hierarchical structure model is constructed. The BN model is constructed based on the real case data of coal mine water inrush accidents. The causal chain analysis is carried out by using the fault diagnosis function of the BN model. The results of the DEMATEL analysis show that the main factors affecting the occurrence of coal mine water inrush accidents are the lack of understanding of water disasters and inadequate hydrogeological detection. The other factors include confusion of safety management and weak technical means. ISM analysis results show that the "three violations" behavior and water source threat are at the top of the multi-level hierarchical structure model of water inrush accidents. These are the direct factors inducing water inrush accidents. The BN analysis results show that the most likely cause chain is inadequate hydrogeological detection → water source threat → water inrush accidents. To effectively curb the occurrence of coal mine water inrush accidents, it is suggested to improve the staff awareness of water disasters, strictly carry out hydrogeological exploration, and fundamentally eliminate the illegal behavior of production personnel.

Research on safe power threshold of radio wave explosion-proof in coal mine

LIANG Weifeng, SUN Jiping, PENG Ming, PAN Tao, ZHANG Gaomin

2022, 48(12): 123-128, 163. doi: 10.13272/j.issn.1671-251x.18045

<Abstract>(1333) <HTML> (60) <PDF>(67)

Abstract:
In order to prevent gas explosion caused by radio waves emitted by wireless equipment in the coal mine, the power and energy of radio waves in coal mines should be limited. This paper introduces the safety power threshold of continuous radio wave explosion-proof specified in different standards. ① GB/T 3836.1-2021 Explosive atmospheres-Part 1: Equipment-General requirements and the international standard IEC 60079-0:2017 Explosive atmospheres-Part 0: Equipment-General requirements refer to the European standard CLC/TR 50427:2004 Assessment of inadvertent ignition of flammable atmospheres by radio-frequency radiation-Guide. When there is no slender structure object (such as a crane) that can be used as a receiving antenna in an explosive environment, the clause that the explosion-proof safety power threshold of continuous radio wave in Class I environment (representative gas is methane) is 8 W is omitted. It is indiscriminately stipulated that the safe power threshold of continuous radio wave explosion-proof in Class I environment is 6 W. ② The British Standard BS 6656:1991 Guide to prevention of inadvertent ignition of flammable atmospheres by radio-frequency radiation specifies that for continuous radio-wave operating frequencies greater than 30 MHz in a Class I environment, the safe power threshold for continuous radio-wave explosion-proof is 8 W, Whether there is a crane or other slender annular structure object. ③ The British Standard BS 6656:2002 Assessment of inadvertent ignition of flammable atmospheres by radio-frequency radiation - Guide and the European Standard CLC/TR 50427:2004 both specify a safety power threshold of 8 W for continuous radio-wave explosion-proof in Class I environments without slender annular structures such as cranes. The safe power threshold of continuous radio wave explosion-proof in Class I environment with slender annular structures such as cranes is 6 W. The characteristic of the underground environment and equipment in the coal mine are analyzed. Generally, there is no crane underground. The underground coal mine is a confined space, with a long roadway but a small roadway section. Cable, water pipe, rail, steel wire rope, overhead line, tape rack and other axial conductors laid along the roadway axis are thin and long, but will not form a ring antenna conducive to radio wave reception. Transverse conductors such as roadway I-beam support can form a ring antenna conducive to radio wave reception. However, the section of the I-steel conductor is large, which does not meet the characteristics of slender structure. The hydraulic support in the fully mechanized working face can form an annular structure. However, the hydraulic support jack divides it into multiple annular structures. The support conductor section is large, which does not meet the characteristics of slender structure. It is pointed out that before the explosion-proof safety power threshold of continuous radio wave in coal mine is implemented to 6 W, the mine wireless communication systems such as leakage, induction, through-the-ground and multi-base stations have been widely used in the coal mine. And there is no case of gas and coal dust explosion accident. Therefore, the threshold of explosion-proof safety power of radio wave in the coal mine is set as 6 W without distinction, which lacks of theoretical analysis and experimental verification. In particular, 5G, WiFi 6, UWB, ZigBee and other mining mobile communication systems and personnel and vehicle positioning system working frequency is higher. Therefore, the coal mine continuous radio wave explosion-proof safety power threshold should be 8 W.

Coordinated optimization method for IGBT peak voltage suppression of mine-used inverter

WANG Yue, SHI Han, RONG Xiang, JIANG Dezhi

2022, 48(12): 129-136, 143. doi: 10.13272/j.issn.1671-251x.17884

<Abstract>(159) <HTML> (40) <PDF>(23)

Abstract:
At present, the methods of optimizing busbar structure parameters, changing gate drive resistance and designing absorption circuit are commonly used to suppress the peak voltage of insulated gate bipolar transistor (IGBT) in mine-used inverter caused by stray inductance. But the existing research has not revealed the coordination and unification relationship between the methods and their coordination and optimization criteria. In order to solve this problem, taking BPJ5-630-1140 type mine-used four-quadrant inverter as the research object, based on the analysis of the influence of stray inductance on the electric-thermal performance of IGBT, a coordinated optimization method of IGBT peak voltage suppression is proposed. ① The method analyzes the influence of busbar structure parameters and grid drive resistance on IGBT peak voltage and power loss. The results show that the peak voltage and power loss of IGBT increase with the AC busbar length increase and the AC busbar width decrease. With the increase of gate drive resistance, IGBT peak voltage decreases and power loss increases. ② The diode clamped absorption circuit is designed, which is verified by experiments to reduce the peak voltage and power loss of IGBT. ③ Considering that the AC busbar width has no effect on the layout and heat dissipation performance of IGBT, the gate drive resistance and the AC busbar length are selected as decision variables. The BP neural network and elitist non-dominated sorting genetic algorithm (BP-NSGAⅡ) are used to achieve multi-objective optimization of IGBT peak voltage, the maximum IGBT temperature and the maximum temperature of radiator surface. The experimented results show that when the maximum temperature of radiator surface is 55-65 ℃ and the maximum IGBT temperature is 74-80 ℃, the minimum IGBT peak voltage is 1861 V. The corresponding grid drive resistance is 5 Ω, the AC busbar length is 300 mm, and the AC busbar width is 200 mm. The optimized IGBT peak voltage of BPJ5-630-1140 type inventer is 1 856 V, which is 35% lower than 2 856 V before optimization. The IGBT peak voltage is effectively suppressed, and the operation reliability of the mine-used inverter is improved.

Mining machine cutting load classification based on vibration signal

XU Zhipeng, LIU Zhenjian, ZHUANG Deyu, YIN Yuxi

2022, 48(12): 137-143. doi: 10.13272/j.issn.1671-251x.2022070078

<Abstract>(154) <HTML> (49) <PDF>(12)

Abstract:
There are some errors and lags in the way of judging the cutting load type of the mining machine manually. In order to solve the above problem, a classification method of mining machine cutting load based on wavelet packet decomposition and sparrow search algorithm optimized BP neural network (SSA-BPNN) is proposed. The method comprises two parts of signal feature extraction and mode classification. In the part of signal feature extraction, the collected vibration signal of the mining machine rocker arm is decomposed by wavelet packet to obtain the energy of each subband and the total energy of the signal. After normalization, feature vectors representing different load types are obtained. The principal component analysis is used to reduce the dimensions of the feature vector. In the mode classification part, SSA is used to optimize the initial weight and threshold of BPNN. The feature vector is used as the input of SSA-BPNN to realize the load classification and recognition. Taking the MG500/1170-AWD1 mining machine as an object, the magnetic acceleration sensor is attached to the shell of the rocker arm of the mining machine near the bracket side. The vibration signals of the mining machine drum under three working conditions of no-load, cutting bauxite and rock are collected and tested. The experimental results show that the vibration signals under different cutting loads have some differences in the energy of each sub-band. This result indicates that the energy features obtained by wavelet packet decomposition can be used as feature vectors to distinguish different load types. Compared with BPNN, SSA-BPNN has faster convergence speed and higher recognition accuracy, and the recognition accuracy of load classification is 95.3%.

Study on reasonable width of coal pillar under water-rock interaction

KONG Fanlong, LIU Jingdong, TIAN Lingtao, ZHANG Zhiqiang, WANG Dongdong, ZHENG Zhiqiang, XU Qiang

2022, 48(12): 144-150. doi: 10.13272/j.issn.1671-251x.2022060062

<Abstract>(141) <HTML> (41) <PDF>(11)

Abstract:
The water accumulation in goaf and coal rock interaction will weaken the strength of the coal pillar in the section and cause gradual destruction and failure of the coal pillar. The interaction of water and rock is the key factor that must be considered in the design of the reasonable width of the coal pillar. The uniaxial compression experiment and theoretical analysis are carried out based on the engineering background of the coal pillar design between mining area 31 and 33 of a mine in Xinjie mining area, Ordos, Inner Mongolia. The results show that water-rock interaction has a significant impact on the weakening of coal strength parameters. The width of the plastic zone at the side of the water accumulation in the section coal pillar expands with the increase of the weakening degree of the coal body strength. Based on the basic conditions for the stability of the section coal pillar, the reasonable theoretical width of the section coal pillar is 53.62 m. Using FLAC3D to simulate the process of water-rock interaction, the paper analyzes the stability characteristics of coal pillar with different widths. The results show that when the width of the coal pillar is small, the weakening effect of water accumulation in goaf has stronger destructive capability to the elastic core area with higher stress concentration. With the increase of coal pillar width, the stress concentration degree in the elastic core area decreases. The area where the vertical stress at the water accumulation side of the goaf is lower than that of the original rock increases. The stress concentration distribution on both sides of the coal pillar tends to be uniform, and the weakening effect of water accumulation in the goaf on the elastic core area is no longer significant. Based on the results of theoretical calculation and numerical simulation, the width of coal pillar is determined to be 70 m. The engineering application results show that the coal pillar with width of 70 m can effectively bear the roof pressure. The deformation of the roadway surrounding rock is small, the stress of the anchor cable is stable, and the safety production of the mine is guaranteed.

Intelligent control system design for water treatment device in coal mine

ZHAO Kangkang, LIU Bo

2022, 48(12): 151-157. doi: 10.13272/j.issn.1671-251x.2022050056

<Abstract>(253) <HTML> (63) <PDF>(38)

Abstract:
The water treatment device for emulsion preparation in fully mechanized mining face mostly adopts manual or hydraulic control mode for water preparation and filter element cleaning. It is unable to monitor water production data and device working status in real-time. It has low degree of automation, complex operation and heavy maintenance workload. By analyzing intelligent control requirements of the water treatment device in underground coal mine, a thermal redundancy scheme of double controllers is designed. The scheme has two controllers that are used to realize the water preparation and automatic cleaning functions of the water treatment device respectively. Based on the scheme, an intelligent control system for water treatment device in underground coal mine is designed. The KXH12B mine-used intrinsically safe controller is used as the core control unit to collect the data of water tank level sensor, pressure sensor, flow sensor and conductivity meter. The water treatment device operation status is monitored in real-time. The two controllers work in a master-slave mode. The master-slave controllers realize control functions of automatic water preparation and automatic cleaning respectively. When the main controller fails, its water preparation function can be automatically switched to the slave controller. This ensures that the water for emulsion preparation in coal mine is not affected. The test platform is built to test the system, and the results show that the system realizes control functions of automatic water preparation, automatic cleaning and data monitoring. The system also realizes the hot-switching function of master and slave controllers. The system has been applied to the water treatment device in a coal mine. It runs stably and reliably, and realizes unattended operation of the water treatment device in coal mine.

Design of low-power test platform for reversing characteristics of mine solenoid pilot valve

LI Junshi

2022, 48(12): 158-163. doi: 10.13272/j.issn.1671-251x.2022060072

<Abstract>(166) <HTML> (41) <PDF>(22)

Abstract:
The electromagnetic pilot valve is one of the core components for realizing unmanned mining in the fully mechanized working face. The current mine electromagnetic pilot valve test methods use emulsion pump as a liquid source. The power consumption is large. In addition, the system pressure regulation uses manual adjustment, which is inefficient. In order to solve the above problems, a low-power test platform for reversing characteristics of mine electromagnetic pilot valve is designed. The platform consists of two parts: hydraulic system and measurement and control system. The hydraulic system mainly includes electric proportional valve, gas-liquid booster pump, valve to be tested, and accumulator. The double-headed gas-liquid booster pump is used as the liquid source so as to reduce energy consumption. The electric proportional valve is used to adjust the inlet air pressure of the gas-liquid booster pump. The inlet pressure of the tested valve is adjusted to the required value to realize the automatic regulation of the system pressure. The measurement and control system consists of upper computer, acquisition card, program control power supply, electric proportional valve controller (PID controller), and various sensors. According to the requirements of sensor installation and precision, the laser displacement sensor is selected to test the displacement of the electromagnet ejector rod. The PID controller is used to adjust the pressure of the liquid inlet of the electromagnetic pilot valve. The platform can realize the real-time dynamic monitoring and data storage of various performance indexes of the electromagnetic pilot valve in the reversing process. The indexes include voltage, current, inlet and outlet pressure, ejector rod displacement, dynamic response time and real-time power consumption, etc. The maximum power is only 800 W, which improves the test safety and efficiency, greatly reduces the system energy consumption, and provides an efficient and reliable test and verification means for researchers.

2022 Vol. 48, No. 12

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