工矿自动化

Research on perception method of coal mine gas and coal dust explosion based on explosion sound recognition

SUN Jiping, YU Xingchen, WANG Yunquan, LI Xiaowei

2023, 49(3): 1-5, 114. doi: 10.13272/j.issn.1671-251x.18077

<Abstract>(262) <HTML> (51) <PDF>(54)

Abstract:
The characteristics of coal mine gas and coal dust explosion are analyzed. The gas concentration changes suddenly. The ambient temperature rises rapidly. The air pressure rises suddenly. It produces fireballs and smoke. It produces strong infrared and ultraviolet radiation. It generates explosion shock waves and flame waves. It produces explosive sound. The coal mine gas and coal dust explosion perception based on explosion sound has the following advantages. ① Explosion shock waves and flame waves attenuate quickly and travel close distances. Sound waves attenuate slowly and travel over long distances. The mine sound pickup equipment far away from the explosion source can be used for the perception of coal mine gas and coal dust explosion. ② Compared with the coal mine gas and coal dust explosion perception method based on gas concentration and temperature sensors, the proposed method has the advantage of fast response. ③ Compared with the coal mine gas and coal dust explosion perception method based on video images, the proposed method has the advantages of not being affected by dust, light, shelter, etc. ④ Mine sound pickup equipment has low cost and is easy to install. ⑤ The sound travels over a long distance and it is less affected by roadways and branches. ⑥ The sound processing speed is fast. The gas and coal dust explosion sound can be quickly recognized from various sound signals in a short time. A perception method of coal mine gas and coal dust explosion based on explosion sound recognition is proposed. The sound signals in the monitoring area are collected using microphone array pickups. After preprocessing such as normalization, framing, and adding category labels, the sound signal features are extracted. The features are input into a statistical classifier for training. The sound recognition model for coal mine gas and coal dust explosion is established. The sound signal of the monitoring area is collected in real-time. The extracted sound signal features are input into the trained coal mine gas and coal dust explosion sound recognition model. Whether it is the coal mine gas and coal dust explosion sound can be determined. If so, an alarm will be given.

Research on mine 5G-advanced communication evolution technology

LI Chenxin

2023, 49(3): 6-12. doi: 10.13272/j.issn.1671-251x.2022090070

<Abstract>(887) <HTML> (80) <PDF>(87)

Abstract:
The mine 5G is the foundation of intelligent mine construction. In order to meet the diversified application requirements of intelligent mine, it is necessary to promote the realization of mine 5G-advanced communication evolution system through 5G-advanced communication evolution technology. The current status of mine 5G technology research and system deployment is reviewed. The mine 5G has preliminarily realized audio and video call, HD video analysis, equipment remote control and other applications. However, there are some problems such as single terminal form, heavy uplink load, separate construction of 5G-advanced communication system and positioning system, etc. This paper analyzes the applicability of 5G-advanced communication evolution technology of 3GPP Release 17 in mine 5G-advanced communication. It is pointed out that RedCap lightweight terminal technology, NR direct connection communication technology and NR positioning technology will become the key technologies of mine 5G communication evolution. The key technologies of mine 5G-advanced communication evolution are studied. RedCap lightweight terminal technology can meet the research and development requirements of low-power consumption and low-cost multimode terminals of mine wireless sensors, single-channel wireless transmission mine video monitoring equipment, intelligent miner lamps and intelligent wearable devices. NR direct connection communication technology is applied to mine automatic driving vehicle-mounted terminal, roadway side equipment and emergency communication relay equipment. It can meet the requirements of low delay and high reliable transmission of mine automatic driving and emergency communication. NR positioning technology is applied to mine communication and positioning integration to meet the technical requirements of mine communication and positioning system fusion. This paper puts forward the architecture of mine 5G-advanced communication evolution system. The study provides the direction for building 5G fully connected mine, supporting multi-link wireless access and realizing the integration of communication and perception.

Deformation analysis and support optimization of adit surrounding rock under overburden load disturbance

CHAI Jing, LIU Hongrui, ZHANG Dingding, LIU Yongliang, HAN Zhicheng, TIAN Zhicheng, ZHANG Ruixin

2023, 49(3): 13-22. doi: 10.13272/j.issn.1671-251x.2022090020

<Abstract>(289) <HTML> (76) <PDF>(16)

Abstract:
The traditional convergence instrument, 3D laser scanning and other monitoring technologies for the deformation of surrounding rock in the mine roadway can not meet the comprehensive monitoring requirements of complex projects. The technologies have low real-time and automatic monitoring degree, and do not have the capability of long-distance, high-precision and large-area monitoring. The existing optical fiber sensing technology only monitors the single parameter of the surrounding rock in the roadway. It can not comprehensively analyze the stability of the surrounding rock in the roadway. Taking the main adit of a coal mine as the engineering background, the stability of surrounding rock before and after the filling above the adit is studied by numerical simulation. The results show that the filling engineering causes the bearing pressure of surrounding rock on both sides of the adit to rise with asymmetric distribution. The maximum subsidence of the top plate increases from 8.3 mm before filling to 22.1 mm. The maximum floor heave increases from 4.0 mm to 8.5 mm. The maximum increase of the displacement of the two sides is 16.2 mm. The deformation of the surrounding rock corresponds strongly to the bearing pressure, which increases with the thickness of the filling above the adit. The fiber Bragg grating (FBG) sensor is used to construct the adit surrounding rock deformation monitoring system. The FBG sensor is set at the adit section to monitor the opening of the adit arch crown crack, the deformation of the roof, floor and both sides, and the stress and strain of the section. The local deformation of the surrounding rock is analyzed through the real-time spectrum. The results show that the adit roof is obviously under pressure under the influence of the disturbance of the overburden load under the existing condition of stone masonry arch support. The maximum subsidence of the roof is about 30 mm, forming a crack about 2 mm wide. The monitoring results are consistent with the numerical simulation and field observation results. The result verifies the effectiveness of the FBG-based adit surrounding rock stability monitoring method. According to the monitoring results, the reinforcement support scheme of bolt+T-shaped steel plate is proposed for the weak part of the adit support. The support effect is verified by numerical simulation. The results show that after the optimized support scheme, the maximum subsidence of the adit roof under the disturbance of overburden load is 11.3 mm. The maximum displacement of the two sides is 12.04 mm, and the average reduction of the surrounding rock deformation is 48.8%. The scheme improves the stability of the surrounding rock.

Research on conveyor belt deviation detection method

WANG Kai, ZENG Xiangjin, LI Xin, ZHANG Rui, XU Cheng

2023, 49(3): 23-30, 52. doi: 10.13272/j.issn.1671-251x.2022050064

<Abstract>(473) <HTML> (82) <PDF>(53)

Abstract:
The machine vision-based conveyor belt deviation detection methods detect conveyor belt edge features. The features contain false edges. The existing research is difficult to identify false edges and has poor adaptability to multiple scenes. To solve this problem, the region of interest (ROI) is extracted from the conveyor belt monitoring image and normalized. The Canny algorithm with a larger threshold range is used to extract edge feature points to improve the scene adaptability of the algorithm. Morphological filtering methods are used to deal with some impurities and false edges. For images where the Canny algorithm cannot detect effective edges, gamma transform and gradient filtering in the 45° and 135° directions are performed on the extracted ROI to enhance edge features. The feature point extraction and morphological filtering based on the Canny algorithm are carried out. The pixel value relationship of edge points, neighborhood features, compactness features, as well as the length, relative position, and slope of edge lines are taken as constraints. The line filtering and sorting algorithm based on the idea of divide and conquer search is used to filter and fit the extracted edge feature points to obtain a real-time edge of the conveyor belt. The pixel value of the real-time edge is subtracted from the pixel value of the edge when no deviation occurs, and the pixel value of the current deviation is obtained. The test results show that for the conveyor belt monitoring images under various scenes, the detection error of the conveyor belt deviation detection method based on the Canny algorithm and line filtering and sorting is less than three pixel values. The detection time of 100 images is 6.9451 s. The CPU occupancy of the edge computer processing four video images is 132%, which meets the accuracy and real-time requirements of on-site conveyor belt edge detection.

Research and application of video fog concentration detection and real-time fog removal method in underground coal mine

GUO Zhijie, NAN Bingfei, WANG Kai

2023, 49(3): 31-38. doi: 10.13272/j.issn.1671-251x.2022080068

<Abstract>(309) <HTML> (64) <PDF>(49)

Abstract:
The dynamic change of scene fog concentration caused by spraying dust removal operation in the production state of underground coal mine working face leads to the blurred visual video image. This seriously affects the visual remote intervention coal mining control and operation in underground coal mine. Aiming at the above problems, a method of video fog concentration detection and real-time fog removal underground in coal mine working face is proposed. Firstly, the difference between the brightness value and the saturation value of the fog-containing video image is calculated based on the color attenuation prior to realize the fog concentration detection. The fog-containing image and the non-fog image are further identified. Secondly, the color attenuation prior and the scene change probability model are used for correcting the video time continuous cost function. The transmittance error between adjacent frames of the video is reduced. The flicker influence of the fog removed video image is reduced. Finally, the video fog concentration detection and real-time fog removal method in the coal mine working face and the Kim method are respectively used to process the foggy video of the coal mine underground scene. The experimental results show the following points. ① The fog concentration distribution in the image scene can be accurately calculated by the fog concentration detection method. The maximum connected region of the extracted fog concentration accounts for 38.693% of the total image pixels. The fog-containing images are those greater than 20% of the fog concentration threshold. According to the recognition result of the fog-containing image, the non-fog image is automatically ignored. The fog-containing image is selectively fog removed. ② The video fog concentration detection and real-time fog removal method in coal mine working face is used to remove the fog of the production video of different areas (a support area and a coal wall area) and different fog concentrations (a medium fog concentration and a higher fog concentration). The contrast of the video image is obviously enhanced after fog removal, and the visual effect is brighter and clearer. ③ The mean square error curve of the real-time fog removal method is lower than that of Kim method. This indicates that the mean square error value of the adjacent frames of the video is reduced after the continuous scene video is fog removed. The flicker phenomenon of the real-time fog removal result video is effectively suppressed. The contrast cost function and the color information loss cost function are used to estimate the transmittance of the fog-containing image. The ideal fog removal effect is obtained when the scene changes. ④ The mean value of mean square error between the adjacent frames of the fog removal result of the real-time fog removal method is reduced by 4.26 compared with the Kim method. The similarity between the adjacent frames is improved. The image flicker phenomenon between the adjacent frames is further suppressed. In the aspect of running time, the processing time of each frame of the real-time fog removal method is increased by 2 ms compared with the Kim method. However, the processing time of each frame is less than 40 ms, which meets the real-time requirement.

Aggregation enhanced coal-gangue video recognition model based on long and short-term storage

YANG Jun

2023, 49(3): 39-44, 62. doi: 10.13272/j.issn.1671-251x.18058

<Abstract>(154) <HTML> (51) <PDF>(15)

Abstract:
Some key targets will be missed when using coal-gangue image recognition technology to recognize coal-gangue. Compared with the image target recognition model, the video target recognition model is closer to the requirements of the coal-gangue recognition and separation scene. The coal-gangue features in the video data can be extracted more widely and deeply. However, the influence of frame repetition, frame similarity and contingency of key frame on the model performance is not considered in the current coal-gangue video target recognition technology. In order to solve the above problems, this paper proposes an aggregation enhanced coal-gangue video recognition model based on long and short-term storage (LSS) model. Firstly, the key frames and non-key frames are used to screen the massive information. Multi-frame aggregation is carried out on the video frame sequence of the coal-gangue. The feature information of the key frame and the adjacent frame is aggregated through temporal relation networks (TRN), and a long-term video frame and a short-term video frame are established. The calculation amount of the model is reduced while the key feature information is not lost. Secondly, the feature weights among the long-term video frames, the short-term video frames and the keyframes are reallocated by using an attention mechanism that integrates semantic similarity weights, learnable weights and region of interest (ROI) similarity weights. Finally, the LSS module is designed to store the effective features of long-term video frames and short-term video frames. The module fuses them in the key frame recognition to enhance the characterization capability of the key frame features, so as to realize coal-gangue recognition. The model is tested based on the coal-gangue video data set in Zaoquan Coal Preparation Plant. The results show that in comparison with the memory enhanced global-local aggregation (MEGA) network, the flow-guided feature aggregation for video object detection (FGFA), the relation distillation networks (RDN) and deep feature flow for video recognition (DFF) model for video recognition, the mean average precision of the aggregation enhanced coal-gangue video recognition model based on LSS is 77.12 % and better than that of other models. The recognition precision of the modes is negatively correlated with the moving speed of the target in the video. The recognition precision of the model in this paper is 83.82% for the slow-moving target detection, and the performance is the best.

Behavior recognition method for underground personnel based on fusion network

ZHANG Lei, RAN Lingbo, DAI Wanwan, ZHU Yonghong, SHI Xinguo

2023, 49(3): 45-52. doi: 10.13272/j.issn.1671-251x.2022120015

<Abstract>(214) <HTML> (32) <PDF>(57)

Abstract:
Underground personnel behavior recognition is an important measure to ensure safe production in coal mines. The existing research on behavior recognition of underground personnel lacks research and analysis on the perception mechanism, and the feature extraction method is simple. In order to solve the above problems, a behavior recognition method for underground personnel based on fusion networks is proposed. The method mainly includes three parts: data preprocessing, feature construction, and recognition network construction. Data preprocessing: the collected channel status information (CSI) data is processed through CSI quotient models, subcarrier denoising, and discrete wavelet denoising to reduce the impact of environmental noise and equipment noise. Feature construction: the processed data is transformed into images using the Gramian angular summation/difference fields (GASF/GADF) to preserve the spatial and temporal features of the data. Recognition network construction: according to the features of personnel actions, a fusion network composed of a gate recurrent unit (GRU) based encoding and decoding network and a multiscale convolutional neural network (CNN) is proposed. GRU is used to preserve the correlation between pre and post data. The weight allocation strategy of the attention mechanism is used to effectively extract key features to improve the accuracy of behavior recognition. The experimental results show that the average recognition accuracy of this method for eight movements, namely walking, taking off a hat, throwing things, sitting, smoking, waving, running, and sleeping, is 97.37%. The recognition accuracy for sleeping and sitting is the highest, and the most prone to misjudgment are walking and running. Using accuracy, precision, recall, and F₁ score as evaluation indicators, it is concluded that the performance of the fusion network is superior to CNN and GRU. The accuracy of personnel behavior recognition is higher than the HAR system, WiWave system and Wi-Sense system. The average recognition accuracy of walking and taking off a hat at normal speed is 95.6%, which is higher than 93.6% for fast motion and 92.7% for slow motion. When the distance between transceiver devices is 2 m and 2.5 m, the recognition accuracy is higher.

Locality-sensitive hashing K-means algorithm for large-scale datasets

WEI Feng, MA Long

2023, 49(3): 53-62. doi: 10.13272/j.issn.1671-251x.2022080018

<Abstract>(291) <HTML> (61) <PDF>(14)

Abstract:
Efficient processing strategy for large datasets is a key support for coal mine intelligent constructions, such as the intelligent construction of coal mine safety monitoring and mining. To address the problem of insufficient clustering efficiency and accuracy of the K-means algorithm for large datasets, a highly efficient K-means clustering algorithm based on locality-sensitive hashing (LSH) is proposed. Based on LSH, the sampling process is optimized, and a data grouping algorithm LSH-G is proposed. The large dataset is divided into subgroups and the noisy points in the dataset are removed effectively. Based on LSH-G, the subgroup division process in the density biased sampling (DBS) algorithm is optimized. And a data group sampling algorithm, LSH-GD, is proposed. The sample set can more accurately reflect the distribution law of the original dataset. On this basis, the K-means algorithm is used to cluster the generated sample set, achieving efficient mining of effective data from large datasets with low time complexity. The experimental results show that the optimal cascade combination consists of 10 AND operations and 8 OR operations, resulting in the smallest sum of squares due to error of class center (SSEC). On the artificial dataset, compared with the K-means algorithm based on multi-layer simple random sampling (M-SRS), the K-means algorithm based on DBS, and the K-means algorithm based on grid density biased sampling (G-DBS), the K-means algorithm based on LSH-GD achieves an average improvement of 56.63%, 54.59%, and 25.34% respectively in clustering accuracy. The proposed algorithm achieves an average improvement of 27.26%, 16.81%, and 7.07% in clustering efficiency respectively. On the UCI standard dataset, the K-means clustering algorithm based on LSH-GD obtains optimal SSEC and CPU time consumption (CPU-C).

Automatic picking method of microseismic first arrival time based on improved support vector machine

LI Tieniu, HU Binxin, LI Huakun, GENG Wencheng, HAO Pengcheng, JI Xubo, SUN Zengrong, ZHU Feng, ZHANG Hua, YANG Chengquan

2023, 49(3): 63-69. doi: 10.13272/j.issn.1671-251x.2022050081

<Abstract>(286) <HTML> (87) <PDF>(24)

Abstract:
The microseismic first arrival time picking is an important prerequisite for the high-precision positioning of the microseismic source. The traditional manual picking method is inefficient. The automatic picking method is difficult to pick the arrival time of the first wave accurately under the condition of low signal-to-noise ratio. In order to solve the above problems, an automatic picking method of microseismic first arrival time based on improved support vector machine (SVM) is proposed. Firstly, the method carries out normalization processing, linear correction and proper clipping on original microseismic data. The method marks different categories of the data by taking the amplitude, the energy and the energy ratio of adjacent moments of the microseismic data as features. Secondly, the method adopts a particle swarm optimization (PSO) algorithm and a grid search method to optimize the penalty parameters and the kernel function parameters of the SVM. The method carries out large-range fast positioning on the parameters by using the PSO algorithm to obtain a preliminary optimal solution. Then the method re-constructs a parameter search interval by taking the solution as an initial position, sets a small-step grid search method to carry out fine searching on the parameters to obtain the optimal parameters. The method substitutes the optimal parameters into the SVM model to train, and obtains the improved SVM model. Finally, the microseismic data are classified and identified according to the improved SVM model. The time corresponding to the first sampling point of the microseismic wave is defined as the arrival time of the first wave. The microseismic monitoring data from a mine shaft is used for the experiment. The results show that the accuracy of the method for picking the microseismic first arrival time is 96.5%, and the average picking error is 3.8 ms. Under the condition of low signal-to-noise ratio, the microseismic first arrival time can still be picked accurately. The picking precision is higher than the short term average/long term average (STA/LTA) method commonly used in automatic picking methods.

Path planning of drilling arm of hydraulic bolt drilling rig

ZHAO Xinyue, ZHAI Bowen, QIAO Hongbing, LI Yuze, WANG Dongjie

2023, 49(3): 70-76. doi: 10.13272/j.issn.1671-251x.2022060055

<Abstract>(258) <HTML> (86) <PDF>(24)

Abstract:
When the hydraulic bolt drilling rig is working, it is necessary to accurately control the orientation of the drilling bit in the working space and the angle and distance between the drilling bit and the roadway wall. It has very high requirements for the adjustment capability of the drilling arm. At present, there is little research on automatic positioning and autonomous path planning of hydraulic bolt drilling rig. In order to solve the above problems, a path planning method for the drilling arm of hydraulic bolt drilling rig is proposed. Based on the working parameters of the CMM2-36 mine hydraulic bolt drilling rig and the structure of the drilling arm, the 3D model of the drilling arm is built and simulated on the Matlab platform. The continuous path planning scheme is adopted. The joint angle planning method of the drilling arm based on the cubic polynomial interpolation method cannot guarantee the acceleration of the drilling arm at the beginning and end positions to be 0. In order to solve the above problems, the fifth polynomial interpolation method is adopted to plan the joint angle of the drilling arm. Taking the roadway roof as an example, 32 drilling positioning points are set on the roof. Three path planning schemes are designed and compared. It is concluded that the "工"-shaped path has the shortest distance and the most reasonable trajectory. The D-H coordinate system is constructed based on kinematics theory. The forward and inverse kinematics of the drilling arm is solved. The theoretical maximum workspace of the drilling arm of the hydraulic bolt drilling rig is solved by the Monte Carlo method. Therefore, the drilling arm will not collide with the roadway and the safety of the work is ensured. The simulation results show that on the premise of meeting the requirements of roadway support, the end drill frame of the drilling arm of the hydraulic bolt drilling rig can realize automatic positioning and independent path planning. And the drilljing arm will not collide with the roadway, which can ensure work safety.

Positioning control method for drilling arm of bolt drilling rig

LI Liheng, SONG Jiancheng, TIAN Muqin, WANG Xiangyuan

2023, 49(3): 77-84, 123. doi: 10.13272/j.issn.1671-251x.2022070052

<Abstract>(177) <HTML> (55) <PDF>(13)

Abstract:
Algebraic and geometric methods are commonly used to realize drilling arm positioning control of bolt drilling rig. However, there are some problems such as low efficiency, no solution, multiple solutions, or poor universality. Using particle swarm optimization (POS) algorithm for positioning control of the drilling arm has the advantages of simple programming, strong search performance and good fault tolerance. But it is easy to fall into the local optimal solution. At present, the drilling arm positioning control based on improved PSO algorithm has low overall optimization efficiency and long optimization time. In order to solve the above problems, a chaotic crossover elite mutation opposition-based PSO (CEMOPSO) algorithm is designed by introducing chaos initialization, crossover operation, mutation operation and extreme value perturbation based on elite opposition-based PSO (EOPOS) algorithm. The method uses standard test functions to test PSO algorithm, EOPSO algorithm, CEOPSO algorithm and CEMOPSO algorithm. The results show that CEMOPSO has the best stability, precision and convergence speed. The motion model of the drilling arm of the bolt drilling rig is established. The CEMOPSO algorithm is used to control the drilling arm positioning. The simulation of the control performance is carried out in Matlab. The results show that under the same iteration times and error precision constraints, the position error and posture error of the drilling arm have a very fast convergence rate from the initial iteration when using the CEMOPSO algorithm. The position error and posture error are smaller than those of the other three algorithms. The error curve is smooth, and the maximum position error is 0.005 m and the maximum posture error is 0.005 rad. When the position error is 1 mm and the posture error is 0.01 rad, the average iteration number of the CEMOPSO algorithm is 343. When the position error is 0.1 mm and the posture error is 0.001 rad, the average iteration number is 473. Under the same positioning precision, the convergence speed and stability of the CEMOPSO algorithm are better than those of the other three algorithms. The results meet the requirements of engineering application. The higher the accuracy of the solution, the better it is.

Research on digital mine all-optical network based on 5G C-RAN technology

SHEN Xue, LIU Jichao, LI Longfei

2023, 49(3): 85-92, 99. doi: 10.13272/j.issn.1671-251x.18065

<Abstract>(313) <HTML> (53) <PDF>(20)

Abstract:
One of the primary issues in building a digital mine is to build a high-quality information network with low latency, large bandwidth, and high reliability. Traditional wireless communication technologies such as WiFi and 4G have been unable to meet the new demand of the digital transformation of mines. Starting from the demand for communication networks in digital mines, the necessity and deployment difficulties of 5G technology in underground mines are studied. It is pointed out that the underground 5G networking scheme based on centralized-radio access network (C-RAN) can effectively reduce the deployment requirements and difficulties of 5G networks in underground mines. However, two issues, the high consumption of optical fiber resources and the difficulty in managing dumb resource failures, must be addressed. A digital mine all-optical network system based on 5G C-RAN technology is proposed. The system architecture is introduced from three levels: C-RAN access network, high-speed all-optical network, and intelligent control platform. Key technologies such as semi-active optical network architecture, low-cost wavelength division multiplexing (WDM) high-speed transmission, and intelligent control platform are studied. The system uses direct inspection WDM technology to save optical fiber resources, which can reduce the number of optical fibers used by 91.67%. At the same time, based on semi-active architecture and topping operation administration and maintenance (OAM) technology, it achieves low-cost control and flexible deployment of optical fiber networks. It solves the tight optical fiber resources and optical fiber network management challenges in underground roadways. The experimental results show that the transmission optical power of 12 WDM optical modules with different wavelengths is 3.5 dBm to 5.2 dBm. The reception sensitivity is −16.9 dBm to −19.0 dBm, and the link budget capacity can reach over 21 dB, meeting application requirements. The extinction ratio ranges from 4.7 dB to 5.1 dB, and the eye pattern margin is greater than 17.5%, indicating high signal quality. At a low temperature of − 40 ℃ and a high temperature of 85 ℃, the WDM optical module has some performance degradation in both transmission optical power and reception sensitivity. But it can still meet the transmission requirements of 10 km. Field application results show that the transmission optical power of 12 WDM optical modules with different wavelengths is 3.7 dBm to 5.6 dBm, and the reception sensitivity is −17.9 dBm to −16.3 dBm. The link budget capability of the worst channel is still above 20.2 dB, meeting application requirements.

Simulation analysis of the influence of gangue layer morphology on the cutting characteristics of the roadheader bolter

LIANG Xu, GUO Jiahao, CHANG Maomao, QU Xingjia, ZHANG Li

2023, 49(3): 93-99. doi: 10.13272/j.issn.1671-251x.2022090062

<Abstract>(175) <HTML> (48) <PDF>(19)

Abstract:
In the actual excavation process of the roadway, besides the coal seam, there are various types of gangue layers on the working face. The existence of these gangue layers will affect the cutting efficiency of the roadheader bolter. However, most current studies analyze the cutting characteristics of the drum with the background of a fully-coal working face or consider a relatively simple morphology of the gangue layer. To solve the above problems, taking the MB670-1 roadheader bolter as the research object, a 3D model of the roadheader bolter is created using Pro/E software. The model is input into RecurDyn software and the corresponding motion pair is added. The model is then input into EDEM software to establish an EDEM-RecurDyn coupling simulation model. The influence of three types of gangue layers, horizontal gangue layers, inclined gangue layers, and semi-gangue layers, on the cutting characteristics of the roadheader bolter is simulated and analyzed from three aspects: drum cutting performance, drum displacement and drum vibration. The results show the following points. ① Compared with the full coal seam, under the conditions of gangue layers, the drum cutting resistance, load fluctuation coefficient, and cutting specific energy consumption all increase. They increase most significantly under the condition of inclined rock layers. The average cutting resistance increases by 35.61%. The load fluctuation coefficients along the X-axis (along excavation direction of the roadheader bolter), Y-axis (perpendicular to the roadway bottom direction), and Z-axis (parallel to the drum axis direction) increase by 26.79%, 25.39%, and 61.28% respectively. The cutting specific energy consumption increases by 37.21%. ② The existence of gangue layers causes a decrease in the displacement of the drum. Compared with the full-coal seam, the displacement of the drum is reduced by 53, 89, 14 mm in the horizontal gangue layer, inclined gangue layer, and gangue layer respectively. ③ The vibration amplitude generated by the drum when cutting a working face containing gangue is much greater than when cutting a working face containing full-coal seams. ④ The influence of the morphology of the gangue layer on the cutting characteristics of the roadheader bolter is in the order of inclined gangue layer ＞ horizontal gangue layer ＞ semi-gangue layer.

Intelligent detection model of flotation tailings ash based on CNN-BP

HAN Yu, WANG Lanhao, LIU Qinshan, GUI Xiahui

2023, 49(3): 100-106. doi: 10.13272/j.issn.1671-251x.2022100019

<Abstract>(796) <HTML> (64) <PDF>(25)

Abstract:
The tailings ash is an important production index of flotation systems. It not only reflects the current operating conditions of flotation system and clean coal recovery, but also has important significance for intelligent flotation control. The existing image-based detection method of flotation tailings ash has the problems of incomplete feature extraction and insufficient model precision. In order to solve the above problems, an intelligent detection method of flotation tailings ash based on convolutional neural network (CNN) - back propagation (BP) is proposed. An intelligent detection model of flotation tailings ash is constructed by combining CNN preliminary prediction and BP neural network compensation prediction. The pulp image feature data is extracted through CNN to preliminarily predict the tailings ash. The image gray feature data and color feature data are used as input to the BP compensation model. The difference between the preliminary prediction value and the actual value is used as output. Finally, the preliminary prediction value and the compensation prediction value are added to obtain the flotation tailings ash. The experimental results show that when the rotor of the magnetic stirrer is small, the rotation speed is 500 r/min, and the light intensity is 12 750 Lux, the pulp is fully stirred, and the image quality is the best. Compared with the CNN model and extreme learning machine (ELM) model, the CNN-BP model has the highest prediction precision, the smallest error fluctuation range. The prediction error is within the range of −2% to +2%. The root mean square error (RMSE) of the CNN-BP model is 0.7705, the determination coefficient is 0.9974, and the mean absolute error (MAE) is 0.5572%. This indicates that its high precision, good effect and strong generalization can meet the requirements of on-site production testing.

Setting strategy of instantaneous current quick-breaking protection for mine power grid

YU Qun, CUI Guoliang, GU Feng, LIU Tao

2023, 49(3): 107-114. doi: 10.13272/j.issn.1671-251x.2022080092

<Abstract>(948) <HTML> (64) <PDF>(26)

Abstract:
The mine power grid operating environment is poor. When a short circuit fault occurs in the underground power supply system, the use of complex anti-skip trip protection device will reduce the reliability of the entire power grid and increase the cost. The mine power grid needs to ensure that the current quick-break protection can act instantaneously to remove the short circuit fault, especially at the line outlet. The current mine power grid protection scheme of the anti-skip trip can not take into account the protection quick-action, power supply reliability and equipment economy. In order to solve the above problems, according to the minimum principle that short-circuit accident in the mine power grid can not skip to the ground, a setting strategy of instantaneous current quick-breaking protection for mine power grid based on the overall optimal principle is proposed. The relationship between the short-circuit current value and the location of short-circuit point and the distribution characteristics of short-circuit current at the beginning and end of the line are analyzed. The shortcomings of the traditional single-setting method are compared and studied. Three indexes are defined, which are minimum-maximum system impedance ratio, maximum system impedance and line impedance ratio, and adjacent line impedance ratio. The indexes are used to represent characteristic relational expressions of different short-circuit current distribution scenarios and conditions for each setting method to meet the requirements. The setting methods applicable to instantaneous current quick-breaking protection under different short-circuit current distribution scenarios are determined. The corresponding optimal setting strategy flow is proposed. Taking a typical mine power supply line as an example, according to the proposed setting strategy, the setting calculation of the protection switch at all levels is carried out. The results show that the setting strategy is used to set five protection switches with the risk of the skip trip in the mine power grid model. The protection range of four protection switches can be controlled within the two-level line. The times of skip trip accidents are reduced. The probability of underground short-circuit faults extending to the surface of the mine power grid is reduced.

Fusion fault line selection method of small current grounding fault based on VMD

SUN Kongming, LI Yudun, FAN Rongqi, XUE Yongduan, PANG Qingle, XU Xianze

2023, 49(3): 115-123. doi: 10.13272/j.issn.1671-251x.2022080082

<Abstract>(242) <HTML> (60) <PDF>(17)

Abstract:
At present, the fault line selection method of coal mine distribution network has the problem of fault line selection failure when the relevant fault features are not obvious. The fault line selection method based on single modal component and single fault feature has low accuracy. In order to solve the above problems, a method of small current grounding fusion fault line selection based on variational mode decomposition (VMD) is proposed. VMD is used to decompose the fault zero-sequence current of each outgoing line in the bus into multiple modal components. The layer number of the VMD is determined according to the fault features of the modal components. The modal components with obvious fault features are selected as effective modal components for fault line selection. The transient energy and the waveform similarity of the effective modal component of the zero sequence current of each outgoing line fault are calculated respectively. According to the proportion of transient energy and the proportion of waveform similarity of effective modal components of each outgoing line, a fault line selection criterion based on transient energy and a fault line selection criterion based on waveform similarity are constructed. The two fault line selection criteria are fused to form a fault fusion line selection algorithm based on VMD. A coal mine distribution network model is built by using the electromagnetic transient simulation software ATP/EMTP. The proposed fusion fault line selection method is verified under single-phase-to-ground fault scenarios with different ground fault resistances, fault initial phase angles and fault locations. The results show that when various single-phase ground faults occur in the distribution network, the fusion line selection method of small-current grounding fault based on VMD is not affected by the fault location. The fault line selection accuracy is respectively improved by 17% and 50% compared with the energy method and the correlation clustering method. The fusion line selection method is not affected by the fault type, and can be applied to the small current grounding fault line selection.

Automation software design and application for fully mechanized working face

LI Zhongzhong, LIU Qing, LIU Junfeng, FENG Yinhui

2023, 49(3): 124-130. doi: 10.13272/j.issn.1671-251x.2022080078

<Abstract>(829) <HTML> (64) <PDF>(29)

Abstract:
In view of the demand for efficient mining of fully mechanized working face, the lack of automation software products in the mining process, the lack of industry pertinence of general industrial configuration software, the variety of equipment interface protocols and the lack of adaptability to business scenes, a design scheme of automation software for fully mechanized working face is proposed. The automation software of fully mechanized working face comprises a three-layer structure of an underground server, a ground server and a ground client. The underground server is the foundation of the whole architecture, which is composed of a driver layer, a database module, a model logic layer and a data visualization layer. The driver layer is responsible for accessing all kinds of equipment and communication protocols adapted to the working face, and realizing real-time two-way communication with each piece of equipment. The database module comprises a real-time database and a historical database. The real-time database provides real-time read-write service for the driver layer, and the historical database provides data recording service for the driver layer. The data model of the fully mechanized working face is constructed according to the business scene of coal mining. The logic layer of the model is used to solve the problem of lack of industry pertinence and adaptability of software. The model logic layer realizes real-time uploading of equipment data and real-time issuing of control instructions through interaction with the equipment layer. The layer provides data drive for data visualization. The layer completes collaborative control and data analysis functions of various equipment through loading control analysis components. The data visualization layer integrates a variety of data display technologies to facilitate the multi-dimensional display of data. The practical application results show the following points. ① In the aspect of auxiliary production, after the application of the automation software, the continuous online real-time monitoring of working condition information and equipment state of the working face can be realized. The remote centralized control of the working face equipment is supported. The number of underground operators who need to be on duty near each piece of equipment is reduced to two persons in the monitoring center for remote centralized monitoring. This effectively reduces the number of operators. ② In the advanced application of automation, after the application of the automation software, the data of each system is classified and fused. The automatic collaborative control function of multiple types of equipment is realized. The automation level of the fully mechanized working face is improved.

Synchronous upgrade method for the wireless terminal of underground personnel precise positioning system

ZHANG Lifeng, BAO Jianjun, JIN Yeyong

2023, 49(3): 131-136. doi: 10.13272/j.issn.1671-251x.18043

<Abstract>(154) <HTML> (57) <PDF>(28)

Abstract:
When the underground personnel positioning system's firmware is upgraded using the wireless in-application programming (IAP) mode, the mark card can not be used normally, the upgrade efficiency is low and the security is not high. In order to solve the above problems, a synchronous upgrade method for the wireless terminal of the underground personnel precise positioning system is proposed. The architecture of underground personnel precise positioning system and the process of wireless terminal synchronous upgrade method are pointed out. The security design and high concurrency technology implementation of the wireless terminal synchronous upgrade are mainly introduced. Security design: in order to improve the security of air data transmission, advanced encyption standard (AES) encryptor is used to encrypt the data transmission between the card reader and the identification card. In order to ensure the normal operation of the identification card after the wireless upgrade, the message digest algorithm MD5 is introduced to perform integrity verification when the identification card completes receiving and is about to switch. High concurrency technology: the ultra wide band (UWB) wireless communication network with full coverage in the mine is used to extend and compatible the wireless upgrade protocol. The distributed multi-node synchronous upgrade of all underground identification cards is realized. The problem of data switching between nodes in motion is solved by the way of breakpoint continuous transmission. Based on the slotted ALOHA algorithm, a dynamic slot allocation mechanism is proposed to ensure the capacity and response efficiency of the personnel precise positioning system. The test results show that the method can be compatible with the underground personnel precise positioning system. The method can realize the wireless upgrade of the embedded firmware of all wireless terminal positioning identification cards without affecting the system performance indicators. The upgrade success rate is 100%, and the upgrade process is efficient, reliable and safe.

A joint positioning method of PDOA and TOF in coal mines based on UWB

GUO Aijun

2023, 49(3): 137-141. doi: 10.13272/j.issn.1671-251x.18078

<Abstract>(773) <HTML> (56) <PDF>(43)

Abstract:
The precise positioning of personnel and vehicles in coal mines is an important guarantee for safe and efficient production in coal mines. Currently, ultra-wideband (UWB) wireless communication technology is mainly used for the precise positioning of personnel and vehicles in coal mines. The positioning method that only uses the time of flight (TOF) requires two positioning substations or antennas for joint ranging and direction. It has problems such as large antenna spacing, inconvenience in installation and maintenance, and large positioning errors. In order to solve the above problems, a joint positioning method based on UWB phase difference of arrival (PDOA) and TOF is proposed for one-dimensional positioning scenarios in coal mines. This method measures the distance between the positioning card and the positioning substation through TOF, and judges the direction of the positioning card through PDOA. The method locates the positioning card based on the measured distance and direction between the positioning card and the positioning substation. This method determines the angle of arrival (AOA) of the positioning card based on the phase difference between the radio signals transmitted from the positioning card and the two antennas of the positioning substation. It does not require a large antenna spacing to determine the direction of the positioning card. It shortens the distance between the two antennas of the positioning substation. It integrates the two antennas to facilitate installation and maintenance, improving positioning precision. The underground testing results of coal mines show that the positioning precision of this method is within 15 cm. Within the test distance range of 200 m, the positioning precision is not affected by the distance. The TOF ranging value is stable within a range of ± 10 cm relative to its mean value, with good stability.

Intelligent technology and engineering practice of backfilling mining in Xingdong Mine

ZHANG Jianzhong, WANG Yunbo, YANG Junhui, YANG Yinchao, WU Honglin, CHEN Feng, ZHANG Jian

2023, 49(3): 142-148. doi: 10.13272/j.issn.1671-251x.2022080020

<Abstract>(855) <HTML> (82) <PDF>(44)

Abstract:
Intelligent backfilling mining meets the industry development trend and need of green and intelligent mining in coal mines. But there is currently a lack of systematic research and practical engineering applications on key technologies of intelligent backfilling mining. In order to solve this problem, with the core of intelligent upgrading of solid backfilling mining faces, the primary goal of intelligent control of backfilling rate, and the goal of building intelligent backfilling mines, the key technologies of intelligent backfilling mining are systematically studied. The technologies include intelligent pretreatment and delivery control technology for coal-based solid waste, the underground intelligent jigging separation technology combined with real-time monitoring and control of ore deposits, intelligent control of waste discharge speed, and mixed separation control, the multi-source coal-based solid waste collection, storage and transportation technology based on real-time monitoring and precise feedback control of multi-source coal-based solid waste, and the intelligent solid backfilling and mining technology which integrates intelligent backfilling monitoring, intelligent flow control, backfilling hydraulic support, and electrohydraulic control of the working face. Through the linkage and coordination of various key intelligent backfilling mining technologies, Xingdong Mine has formed an intelligent backfilling mine with coal-based solid waste pretreatment, underground coal gangue separation, multi-source coal-based solid waste storage and transportation, and solid backfilling mining as the core. The results of engineering practice show that the capacity of intelligent backfilling working faces has been greatly improved after the application of intelligent backfilling mining technology. The working faces' output increases from 36000 t per month to 72000 t per month. The number of personnel per shift is reduced by 8-10. The time of one cycle operation is reduced by about 2.5 h, and the efficiency increases by about 50%. The problem of coal-based solid waste discharge has been comprehensively solved.

2023 Vol. 49, No. 3

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