工矿自动化

Development path of new generation intelligent coal mine under HCPS theory system

JIN Zhixin, WANG Hongwei, FU Xiang

2022, 48(10): 1-12. doi: 10.13272/j.issn.1671-251x.17988

<Abstract>(258) <HTML> (50) <PDF>(64)

Abstract:
Under the background of the current development of the intelligent high-quality coal mine industry, based on the technical mechanism of "human-cyber-physical system"(HCPS) of new generation intelligent manufacturing , the theoretical system and technical path of the new generation intelligent coal mine with human as the center are proposed. By introducing the evolution of modern coal mine mechanization, informatization and intelligent production mode and the technical development of human, cyber system and physical system, as well as the key scene technologies faced by the development process of the new generation intelligent coal mine, the HCPS technical system of the new generation intelligent coal mine is constructed. The interaction mechanism of human, coal mine cyber system and coal machine equipment physical system is expounded. The HCPS technology elements are given from the four dimensions of human, coal mine cyber system, coal machine equipment physical system and their integration. According to the human-centered development concept, the human-machine cooperation technology path under HCPS theoretical system of the new generation intelligent coal mine is proposed. The core technologies such as human-machine autonomous cooperative interaction mode under the goal of coal mine safety production, situation awareness of coal mine safety production based on man in the loop , coal mine system control sharing under human-machine trust and interaction mode, visualization application development of human-machine information interaction of coal mine task scene are emphatically expounded. It is pointed out that training and practice of the multi-disciplinary talent of mining, machinery, cyber, computer and management, and the innovation of coal mine management, safety system, production mode and personnel type of work are the two key points of the development of the new generation intelligent coal mine.

Hydraulic support digital twin joint modeling method

WANG Hongwei, WU Yadan, CHEN Long

2022, 48(10): 13-19. doi: 10.13272/j.issn.1671-251x.2022080010

<Abstract>(294) <HTML> (215) <PDF>(73)

Abstract:
The existing hydraulic support modeling method has the problems of single modeling mode and lack expression of internal actions of the model. It is difficult to realize deep knowledge mining of the digital twin model. The modeling of hydraulic support only studies the mechanical or hydraulic parts separately. It is difficult to master its overall dynamic characteristics. In order to solve the above problems, taking the shield hydraulic support ZY6800/08/18D as the research object, a hydraulic support digital twin joint modeling method is proposed. The three-dimensional solid models of the mechanical system and the hydraulic system of the hydraulic support are established by using the SolidWorks software. The three-dimensional solid model is imported into the MapleSim software by .sldasm file format. The kinematic pair is used for connecting the mechanical part, and the hydraulic element is used for connecting the hydraulic part. The twin models of the mechanical system and the hydraulic system of the hydraulic support are established. The twin models are combined to carry out data interaction and model optimization with the physical entity through a database. In order to make the model one to one mapping physical entity, the hydraulic support digital twin is established, including system layer, information layer and physical layer. The consistency experiment of virtual and real is carried out on the digital twin of the hydraulic support. Under the condition of inputting the same signal into the physical entity and the twin, the consistency of the angle change of the connecting rod between the physical entity and the twin is analyzed. The rationality and accuracy of the model are verified. The results show that the fitting degree of the angle of the connecting rod between the physical entity and the twin is 0.986, which is close to 1. The fitting degree is good, which indicates that the position and attitude information of the twin model driven by real data is basically consistent with the running result of the physical entity. The overall angle error of the connecting rod is from −0.198° to +0.185°, which meets the precision requirements within the precision range of the tilt sensor. The motion law of the digital twin model conforms to the actual motion state of the hydraulic support. The mutual mapping and mutual fusion between the physical entity and the digital twin are realized.

Manual regulation and control decision model of middle hydraulic support cluster automation in the intelligent working face

ZHANG Jintao, FU Xiang, WANG Ranfeng, WANG Hongwei

2022, 48(10): 20-25. doi: 10.13272/j.issn.1671-251x.17989

<Abstract>(255) <HTML> (49) <PDF>(42)

Abstract:
The intelligent working face has abnormal working conditions such as lost support, uneven straightness, and support skew after the automatic following of hydraulic support. Therefore, manual regulation and control are needed. At present, the research lacks the knowledge discovery of manual regulation and control working conditions after the hydraulic support automation in the production process of the intelligent working face. This is not conducive for workers to quickly judge the number of hydraulic support requiring manual regulation and control. In order to solve the above problems, based on the identification of the number of hydraulic support that is not up to the standard after the hydraulic support automation, a manual regulation and control decision model of middle hydraulic support cluster automation in the intelligent working face is put forward. Firstly, the historical data of the working face is analyzed. It is concluded that after the automatic following of the hydraulic support is finished, three characteristic values can be used as important characteristics for judging whether the hydraulic support carries out manual regulation and control after the automatic following of the hydraulic support. The characteristic values include the distance of the automatic following of the hydraulic support, the stroke variation of the pushing oil cylinder before and after the automatic following of the hydraulic support, and the absolute difference between the number of the hydraulic support at the position of the shearer and the number of the judged hydraulic support. According to the above conclusion, the structure of the manual control decision model after the hydraulic support cluster automation is proposed. The data acquisition module is used for providing the original data. The data preprocessing module prepares the original data by outlier processing, filtering, sorting and correlation analysis. The characteristic engineering module calculates and standardizes the above three characteristic values to provide a sample set for the classification model. After the classification model divides the sample set, the ID3 decision tree is used for classification. Finally, the number of hydraulic supports needing normal working conditions and the number of hydraulic supports nedeing manual control are output. The results of the model evaluation show that, compared with the traditional K-nearest neighbor (KNN), support vector machine (SVM), logical regression (LR) classification algorithms, the training set accuracy of the ID3 decision tree based classification model for the working conditions of hydraulic supports in the middle of the intelligent working face is 92.27%. The test set accuracy is 93.75%. The model can better distinguish the manual control hydraulic support number after automation.

Dynamic evaluation of support quality of hydraulic support in space-time region

JIA Sifeng, FU Xiang, WANG Ranfeng, WANG Hongwei, WANG Pengfei

2022, 48(10): 26-33, 81. doi: 10.13272/j.issn.1671-251x.17992

<Abstract>(247) <HTML> (146) <PDF>(35)

Abstract:
The support process of hydraulic support is a dynamic change process in time and space regions. At present, the evaluation of support quality of hydraulic support mostly focuses on the static characteristics of support. There is little research on the dynamic change of support column pressure. In order to solve the above problems, a dynamic evaluation model of support quality of hydraulic support in the space-time region based on improved LeNet-5 network is established by using the deep learning method. Firstly, the column pressure data of hydraulic support in working face is preprocessed (missing value filling, abnormal value processing, screening, sorting, etc.) to obtain more complete pressure data hydraulic support. Secondly, the preprocessed column pressure data of the hydraulic support is arranged according to time and space. The important characteristics are extracted such as initial setting force, circulating end-resistance, time-weighted resistance, and spatial distribution condition of resistance, which reflect the support condition of the hydraulic support in intelligent mining working face. The pressure time-sequence and the pressure space-sequence are combined into the total space-time pressure matrix in 2D space-time region. Thirdly, according to the support requirements of the working face, the support quality of the space-time region into seven types, namely, support quality preliminary deterioration, support quality continuous deterioration, support quality deep deterioration, support quality general maintenance, support quality preliminary optimization, support quality continuous optimization and support quality good maintenance. On the total space-time pressure matrix, the sliding window is used to intercept the sub-matrix with the given size at a certain interval. The sub-matrix is one-to-one corresponding to the seven support quality types in space-time regions to form samples and labels. Finally, the samples and the labels are input into the improved LeNet-5 network for training. The dynamic evaluation model of support quality of hydraulic support in space-time region is constructed, which evalues the support condition of hydraulic support in the region in real-time. The experimental results show that the model based on improved LeNet-5 network can be used to identify the dynamic effect of support quality in the working face, and provide the basis for the field operators to adjust the support state of the hydraulic support pertinently. The classification accuracy is 85.25%, which is 12% higher than that of the model based on LeNet-5 network. At the same time, the improved LeNet-5 network can converge to the optimal solution quickly in the training process, which accelerates the training speed of the network. The result verifies the advantages of the improved LeNet-5 network in evaluation of the support quality of hydraulic support in space-time region of intelligent working face.

Method for extracting froth velocity of coal slime flotation based on image feature matching

GUO Zhongtian, WANG Ranfeng, FU Xiang, WEI Kai, WANG Yulong

2022, 48(10): 34-39, 54. doi: 10.13272/j.issn.1671-251x.17991

<Abstract>(195) <HTML> (139) <PDF>(37)

Abstract:
The local static characteristics of coal slime flotation foam image are similar. The dynamic characteristics of flotation foam image are needed for judging some complex working conditions. The existing extraction method for the dynamic features of the froth velocity of coal slime flotation has the problems of insufficient accuracy, real-time performance and stability. In order to solve the above problems, a feature extraction method of froth velocity in coal slime flotation based on image feature matching is proposed. Firstly, the contrast limited adaptive histogram equalization (CLAHE) and block-matching and 3D filtering(BM3D) are used to preprocess the flotation froth image to improve the quality of the image and highlight the edge details of the image. Secondly, the accelerated-KAZE (AKAZE) algorithm of accelerated features in nonlinear scale space is used to detect the feature points of flotation froth features. Thirdly, on the basis of rough matching of froth image features by brute-force matching (BF), a grid-based motion statistics (GMS) algorithm is used to quickly and reliably distinguish correct and wrong feature matching. Finally, the method calculates the slime foam velocity according to the feature matching results. The foam velocity is taken as the measured value. The Kalman motion estimation method is used to iteratively modify the measured values to obtain more stable foam velocity characteristics of coal slime flotation. The experimental results show the following points. ① The AKAZE-GMS algorithm can solve the problem of feature point clustering well and keep more feature points as much as possible. This is because the preprocessed image is less affected by noise, has better contrast, and has more prominent edge features. ② Compared with SIFT (scale-invariant feature transform), SURF (speeded up robust features) and AKAZE, the AKAZE-GMS algorithm has a more uniform distribution of matching pairs, retains more correct matching pairs. The method achieves a matching accuracy of 99.99%. The running time is only 3.73 s. ③ The measured value of froth velocity directly calculated from the feature matching results fluctuates greatly. The velocity estimated value of the measured value corrected by Kalman motion estimation is more stable, which is more consistent with the real working condition.

A joint algorithm of multi-target detection and tracking for underground miners

ZHOU Mengran, LI Xuesong, ZHU Ziwei, HUANG Kaiwen

2022, 48(10): 40-47. doi: 10.13272/j.issn.1671-251x.2022060040

<Abstract>(361) <HTML> (55) <PDF>(46)

Abstract:
The existing multi-target tracking algorithms for underground miners has the problems of slow detection speed and low recognition precision. In order to solve the above problems, a joint algorithm of multi-target detection and tracking algorithm based on the improved YOLOv5s model and the improved Deep SORT algorithm is proposed. In the part of multi-target detection, the YOLOv5s-GAD model is obtained by improving YOLOv5s model. The GhostConv module and the depthwise separable convolution (DWConv) module are introduced to replace the BottleneckCSP module in the YOLOv5s model backbone network and path aggregation network respectively. Therefore, the feature extraction speed is improved. Considering the characteristics of dark underground light and many noisy images, the efficient channel attention neural network (ECA-Net) module is introduced into the minimum feature map to improve the model's overall precision. In the part of multi-target tracking, the omni-scale network (OSNet) is used to replace the shallow residual network in Deep SORT to carry out omni-directional feature learning. Therefore, pedestrian re-identification and target tracking precision are improved. The experimental result shows that on the custom dataset Miner21, the YOLOv5s-GAD model average preciscom (when the intersection of union ratio is 0.5) reaches 97.8%, and the frame rate reaches 140.2 frames/s. The multi-target detection effect is better than the commonly used Faster RCNN, YOLOv3 and YOLOv5s models. On the public miners dataset MOT17, the speed and accuracy of the multi-target detection and tracking joint algorithm are better than those of IOU17, Deep SORT and other common multi-target tracking algorithms. The proposed model has the least number of personnel identity conversions and the best miner re-recognition effect. The joint algorithm of multi-target detection and tracking for underground miners can detect and track underground miners in time, and the multi-target tracking effect is good.

Unsafe action recognition in underground coal mine based on cross-attention mechanism

RAO Tianrong, PAN Tao, XU Huijun

2022, 48(10): 48-54. doi: 10.13272/j.issn.1671-251x.17949

<Abstract>(546) <HTML> (69) <PDF>(104)

Abstract:
The real-time video monitoring and alarming of unsafe actions of coal mine personnel is an important means to improve the level of safety in production. The coal mine underground environment is complex, and the monitoring video quality is poor. The conventional action recognition method based on image features or human body key point features is limited in application in the underground coal mine. An action recognition model of multi-feature fusion based on cross-attention mechanism is proposed to recognize unsafe actions of coal mine personnel. For segment video images, the 3D ResNet101 model is adopted to extract image features. The openpose algorithm and ST-GCN (space-time graph convolutional network) are adopted to extract human body key point features. The cross-attention mechanism is used to fuse the image features and human key point features. The fused features are spliced respectively with the image features or human key point features processed by the self-attention mechanism to obtain the final action recognition features. The recognition features is processed by the fully connected layer and the normalized exponential function softmax to obtain action recognition result. Based on the public data sets HMDB51 and UCF101, and the self-built coal mine video dataset, the action recognition experiment is carried out. The results show that the cross-attention mechanism can make the action recognition model more effective in fusing image features and human key point features, and greatly improve the recognition accuracy. At present, SlowFast is the most widely used action recognition model. Compared with the SlowFast, the recognition accuracy of the action recognition model of multi-feature fusion based on cross-attention mechanism has been improved by 1.8% and 0.9% for HMDB51 and UCF101 datasets respectively. The recognition accuracy on the self-built dataset has increased by 6.7%. It is verified that the action recognition model of multi-feature fusion based on cross-attention mechanism is more suitable for the recognition of unsafe actions in the complex coal mine environment.

Multi-object detection of iron foreign bodies in scraper conveyor based on improved Mask R-CNN

SHI Lingkai, GENG Yide, WANG Hongwei, WANG Hongli

2022, 48(10): 55-61. doi: 10.13272/j.issn.1671-251x.2022080029

<Abstract>(300) <HTML> (54) <PDF>(54)

Abstract:
The scraper conveyor is the key transportation equipment in the coal mine. The iron foreign body entering the scraper conveyor will lead to wear and tear, chain breakage, and even cause serious accidents such as production stoppage and personal injury. The existing scraper conveyor foreign bodies identification method has the problems of poor adaptability to underground images and the incapability of distinguishing the types and quantities of foreign bodies. To solve the above problems, a multi-object detection method for iron foreign bodies in scraper conveyor based on improved mask region-convolutional neural network (Mask R-CNN) is proposed. The image enhancement algorithm based on the Laplace operator is used to preprocess the images collected under the environment of low illumination and high dust. The enhanced images are marked to make a data set. The ResNet-50 feature extractor of the Mask R-CNN model is used to obtain the image features of iron foreign bodies. The feature pyramid network is used for feature fusion to ensure both high-level semantic features (such as category, attribute, etc.) and low-level contour features (such as color, contour, texture, etc.), so as to improve the accuracy of small-scale iron foreign body identification. To solve the problem that the anchor point generated by the Mask R-CNN model does not correspond to the size of the iron foreign body to be detected, the Mask R-CNN model is improved. K-means Ⅱ clustering algorithm is used to replace the original anchor point generation scheme. The cluster center point is obtained by traversing the length and width information of the tag box in the data set, so as to achieve the multi-object detection of iron foreign bodies in the scraper conveyor. The experimental results show that the average detection time of the improved Mask R-CNN model is 0.732 s, which is shortened by 0.093 s and 0.002 s compared with Mask R-CNN and YOLOv5 respectively. The average precision is 91.7%, which is 11.4% and 2.9% higher than that of Mask R-CNN and YOLOv5 respectively.

Video real-time detection of bulk material accumulation on belt conveyor

TANG Jun, LI Jingzhao, SHI Qing, LIU Yang, SONG Shixian, REN Chengcheng

2022, 48(10): 62-68, 75. doi: 10.13272/j.issn.1671-251x.2022050078

<Abstract>(357) <HTML> (38) <PDF>(52)

Abstract:
The non-contact bulk material accumulation detection method has problems, such as slow detection speed, low detection precision in image fuzzy scene, large memory requirement of deep learning model. In order to solve the above problems, a video real-time detection method of bulk material accumulation on belt conveyor based on lightweight Mask-RCNN (mask region-based convolutional neural network) is proposed. Firstly, the collected image is preprocessed by the dark channel prior algorithm to reduce the image fogging phenomenon caused by dust in the transportation and loading process and improve the image edge features. The traditional Mask-RCNN backbone network ResNet can not meet the requirement of real-time detection of bulk material accumulation on an embedded platform. In order to solve this problem, the defogging preprocessed image is input into the backbone network based on MobileNetV2 + feature pyramid network (FPN) for feature extraction. The feature graph is generated. The backbone network is designed to be lightweight. The backbone network is deployed on the embedded platform to collect image data in real-time for instance segmentation. In order to find the edge of the segmented object more accurately, a method of adding edge loss in the mask branch of traditional Mask-RCNN is proposed. The mask is generated by using full convolutional network layer. The edge loss function is constructed by combining the Scharr operator. The segmentation image is obtained by fusing object classification, bounding box regression and semantic information. Finally, the bulk material accumulation detection is realized by judging whether the pixel value in the bulk material accumulation mask exceeds a preset threshold value. The experimental results show that the memory requirement of the proposed method is reduced to 1/5 of that of the model taking ResNet 101 as the backbone network. The average precision mean value after image defogging pre-processing is increased by 8%. The average detection time of one image is 0.56 s, the detection precision can reach 91.8%.

Drill pipe counting method based on improved MobileNetV2

ZHANG Dong, JIANG Yuanyuan

2022, 48(10): 69-75. doi: 10.13272/j.issn.1671-251x.2022060019

<Abstract>(246) <HTML> (55) <PDF>(42)

Abstract:
The existing drill pipe counting methods based on manual and instrument have the problems of low precision, time-consuming and labor-consuming. The existing drill pipe counting methods based on image processing are difficult to extract image features, the network model has high complexity and large amount of computation. In order to solve the above problems, a drill pipe counting method based on improved MobileNetV2 is proposed. The working state image of the drilling rig is collected through a camera. The collected image is preprocessed by adopting data enhancement. On the basis of MobileNetV2, the convolutional block attention module is added to enhance the thinning capability of features. The objective function is optimized to improve the recognition precision. The initial parameters are obtained through transfer learning. The improved MobileNetV2 is used as the working state recognition model of the drilling rig. The working state features of the drilling rig are extracted by the model. The confidence data are generated by recognizing the four working states of the drilling rig, including drill pipe installation, drill pipe driving, drill pipe unloading and shut down during the whole drilling process of the drill pipe. The confidence data are filtered through a sliding window. The number of drill pipes is accurately counted, and the drilling depth is determined. The experimental results show that the recognition accuracy of the improved MobileNetV2 model reaches 99.95%. Compared with the classical classification models ResNet50, Xception, InceptionV3, InceptionResNetV2 and MobileNetV2, the accuracy is improved by 1.35%, 1.28%, 1.43%, 0.85% and 1.25% respectively. The parameter is reduced by 38.9% compared with the MobileNetV2 model. The convergence speed of the model is faster and the comprehensive performance is better. The drill pipe counting method based on the improved MobileNetV2 is applied to the drill pipe counting of fully mechanized mining face of a coal mine. The average drill pipe statistical precision is 98.4%. The accurate counting of the drill pipes is realized. The feasibility and practicability of application of the method in the complex environment are verified.

Intelligent identification and positioning of steel belt anchor hole in coal mine roadway support

ZHANG Fujing, WANG Hongwei, WANG Haoran, LI Zhenglong, WANG Yuheng

2022, 48(10): 76-81. doi: 10.13272/j.issn.1671-251x.2022080070

<Abstract>(440) <HTML> (123) <PDF>(51)

Abstract:
When the steel belt auxiliary bolt is used in the coal mine underground heading roadway, if the positioning of the steel belt anchor hole is not accurate, the drill bit is easy to cause equipment damage when hitting the steel belt or anchor net. There are large potential safety hazards. In order to solve the above problems, an intelligent identification and positioning method of steel belt anchor hole in coal mine roadway support based on improved YOLOv5s model is proposed. ① The definition of the anchor hole image is increased by the super-resolution(SR). The high-frequency information of the anchor hole edge in the image is prevented from being lost due to image blurring. ② Because the anchor hole is small and the camera has a certain distance from the anchor hole, it is easy to lose the characteristic information of the small anchor hole in the convolutional neural network. This affects the detection effect of the anchor hole. The coordinate attention mechanism (CA) module is added to the Backbone network of YOLOv5s model. The network layers of the characteristic extraction network in the YOLOv5s network are increased. The coordinate information of the target object is integrated into the convolutional network. The characteristic information of the anchor hole small target can be effectively extracted, and the success rate of anchor hole detection is improved. ③ The YOLOv5s network embedded in the CA module is trained to the anchor hole dataset reconstructed by SR, and the improved YOLOv5s model, namely SR-CA-YOLOv5s model, is obtained. ④ The SR-CA-YOLOv5s model combined with the binocular camera is used to identify and locate the anchor hole in real-time. The experimental results show that compared with the YOLOv5s model, the mean average precision of the SR-CA-YOLOv5s model is 96.8%, which is 3.1% higher than the YOLOv5s model. The SR-CA-YOLOv5s model has better detection capability and avoids missing detection to a certain extent. Although the frames per second (FPS) of the SR-CA-YOLOv5s model is reduced by 18.5 frames/s, its FPS remains at 166.7 frames/s, which does not affect the real-time detection function of the model. The actual test results show that the SR-CA-YOLOv5s model can accurately detect the anchor hole and obtain the three-dimensional coordinate of the anchor hole relative to the camera under different lighting conditions. The coordinate error is within 6 mm, and the FPS meets the real-time requirements.

Research on key technologies of 3D laser scanning modeling in fully mechanized working face

RONG Yao, CAO Qiong, AN Xiaoyu, WEN Liang, ZHAO Yunfei

2022, 48(10): 82-87. doi: 10.13272/j.issn.1671-251x.2022060054

<Abstract>(328) <HTML> (47) <PDF>(65)

Abstract:
According to the boundary information of the coal wall and roof in the 3D laser scanning model of fully mechanized working face, the shearer can automatically adjust the cutting height of the drum to realize the coal precise mining. The existing technology realizes the automatic extraction of the roof line of coal cutting based on the laser point cloud of the working face. But the extraction results cannot be directly applied to the digital automatic coal cutting. In order to solve this problem, the overall scheme of 3D laser scanning modeling for fully mechanized working face is proposed. The key technologies such as boundary extraction of coal wall and roof, target ball detection, point cloud registration and coordinate transformation are studied. The near real-time acquisition of boundary information of coal wall and roof under 3D geological coordinate system is realized. The information can be directly sent to the shearer drum to provide data reference for the next cutting of the shearer. The scanning of the working surface is realized through an inspection robot to obtain the inspection point cloud. Based on the curvature characteristics of the boundary of the coal wall and roof, the string and normal vector method is used to extract the boundary of the coal wall and roof roughly. The angle information between the normal vector of data points and the normal vector of adjacent points is introduced. The obvious intersection points of non coal wall and roof are eliminated through the threshold. As the inspection point cloud and the extracted boundary information are both located in a local coordinate system, the head and tail point clouds and the inspection point cloud are registered through the detection and registration of the positioning target ball. The working face combined point cloud is obtained. According to the 3D geological coordinate and the local coordinate of the positioning target ball, the transformation relation between the coordinates is obtained. The combined point cloud is transformed into the 3D geological coordinate system through coordinate transformation. Therefore, the boundary information of the coal wall and the roof under the 3D geological coordinate system is obtained. The underground industrial test results show that the error of the boundary between the coal wall and roof extracted by 3D laser scanning technology in fully mechanized working face is less than 10 cm. The sampling points with error less than 4 cm account for 50%. The sampling points with error less than 8 cm account for 96.67%.

Design of variable frequency control system for local ventilator based on fuzzy theory

JIA Tianyi, XU Lijun, CHEN Zhifeng, TANG Jia

2022, 48(10): 88-96, 106. doi: 10.13272/j.issn.1671-251x.2022060087

<Abstract>(196) <HTML> (24) <PDF>(35)

Abstract:
The existing variable frequency control method for local ventilator lacks prediction of gas outburst variable. When a large amount of gas emission abnormally, there is a certain lag in regulation, which is easy to lead to gas accumulation. To solve this problem, a variable frequency control system for local ventilator based on fuzzy theory is designed. Fuzzy control is realized by using gas fuzzy controller and air volume fuzzy controller. The control quantity output by two fuzzy controllers is compared. The frequency conversion situation of ventilator is determined according to the larger value. When the two are equal, the fuzzy control of gas is dominant. The classification method based on gas emission is adopted. With the air volume corresponding to the farthest working point as the auxiliary, the ventilator frequency is divided into 4 levels. The gas volume fraction of the heading working face reaching 0.8% is set as the frequency-increasing condition. The gas volume fraction not more than 0.6% or 0.5% is set as the frequency-reducing condition. Moreover, the air supply quantity of the ventilator after frequency reduction is set as the air supply volume required to control the gas volume fraction of return airflow at 0.7% or 0.6% when the frequency reduction condition is achieved. When a large amount of abnormal gas emission, the ventilator is increased in frequency to reduce the gas concentration. At the same time, the air supply volume of the ventilator can meet the greater gas discharge demand. The ventilator can provide a certain buffer for adjustment, and overcome the shortcomings of frequency conversion control lag. The test results show that the gas volume fraction is 0.5% under the condition of frequency reduction. The air supply volume after frequency reduction is the air supply volume required to control the gas volume fraction of return air at 0.6% when the frequency reduction condition is achieved. The control effect is good under this condition. But the air supply volume of level I is slightly less than the minimum air supply volume required at the farthest heading distance. The new frequency level I^* between level I and level II can be set. The air supply volume can be increased by increasing the frequency of the ventilator to meet the minimum air supply volume requirement at the farthest heading distance.

Research on dynamic workflow construction method of coal mine gas control

ZHANG Shulin, YANG Jian, SHU Longyong

2022, 48(10): 97-106. doi: 10.13272/j.issn.1671-251x.17971

<Abstract>(175) <HTML> (31) <PDF>(39)

Abstract:
At present, the key links of coal mine gas control management still need manual supervision. The gas control measures can not achieve "reliable quality" and "process traceability". The backward gas control management mode results in overlapping functions, poor process and low degree of data sharing. In view of the above problems, based on workflow technology, from the perspective of global management, the dynamic workflow construction method of coal mine gas control is studied. Firstly, the workflow, constraint conditions and implementation process of gas control in the minging face and coal uncovering working face are analyzed. The links of gas control flow is divided into two types: test and measure. It is specifically divided into five types of work, including approval of technical documents and reports, drilling construction, sampling, gas parameter measurement, and gas extraction and parameter detection. Secondly, the method reconstructs the last four types of work and further divides the work into 25 basic work units. The method uses Petri Nets to combine basic work units to establish different cross-departmental gas control complex business workflow. Based on the gas control workflow chart, a representation method for the implementation progress of gas control in the working face is established. The method uses the strategy of combining initiative and automation to assign task of the workflow. The method uses description files to dynamically generate and configure workflow networks to meet the requirements of dynamic workflow modeling for gas control. Finally, based on the Flowable workflow engine, the dynamic workflow function of gas control is developed and applied. The results show that the construction of dynamic workflow can make the gas control business process. It is conducive to improving the efficiency of gas control collaborative execution, realizing the rapid flow, tracking and sharing of data. It is conducive to monitoring the overall operation and optimization of gas control work, improving the decision-making capability of coal mine gas control, and innovating the management mode of coal mine gas control.

Research on emergency management system of unmanned transportation system in open-pit coal mine

XUE Qiwen, DING Zhen, SUN Zhenming, LI Tengfei, YANG Jianjian

2022, 48(10): 107-115. doi: 10.13272/j.issn.1671-251x.17998

<Abstract>(905) <HTML> (78) <PDF>(52)

Abstract:
With the development of the intelligent mine, open-pit coal mine unmanned transportation system has gradually carried out experimental applications. But the application of new technologies brings new management challenges. The existing emergency management system can not meet the needs of the new status. In order to solve the above problems, the emergency management system of the unmanned transportation system in the open-pit coal mine is studied from four aspects: the analysis of the present situation, the design of the system framework, the system construction and the future development trend. Firstly, the status quo of emergency management of unmanned transportation system in the open-pit coal mine is analyzed. The new challenges that may be faced in emergency management are summarized. The challenges include the lack of experience in the application of new technologies, the lack of targeted emergency management content, and the lack of clarity in the event disposal process and content caused by the weak capability of information sharing and coordination. Secondly, the emergency of the unmanned transportation system in the open pit coal mine is classified (including transportation accident, meteorological disaster, communication accident, fire and others). According to the general requirements of safety and emergency management of open pit coal mine enterprises, the framework of the emergency management system of the unmanned transportation system in the open pit is designed. The framework covers the whole links of prevention, monitoring, disposal and recovery of the sudden crisis in the production process. Thirdly, the construction of the emergency management system of the unmanned transportation system in the open pit coal mine is analyzed in detail from four aspects of emergency preparation and drill, monitoring and early warning, emergency response and disposal management. Finally, based on the development background of intelligent mine, the future development trend of emergency management system in the open-pit coal mine is discussed, including the integration of dispatching management and emergency disposal, the intellectualization of emergency plan preparation, and the emphasis on information security. The research results provide a reference for further improving the emergency management theory of unmanned transportation system in open-pit coal mine and formulating effective emergency plan.

Pressure relief law and application of deep-buried high-stress bedding coal by hydraulic flushing

ZHANG Jianguo, ZHAI Cheng

2022, 48(10): 116-122, 141. doi: 10.13272/j.issn.1671-251x.17966

<Abstract>(118) <HTML> (32) <PDF>(20)

Abstract:
In order to solve the problem of the high risk of coal and gas outburst in deep coal working face, taking 12090 working face of Shoushan No.1 Coal Mine as the engineering background, the paper analyzes the deformation and stress variation law of coal body after hydraulic flushing in coal working face under deep-buried and high-stress environment by numerical simulation method. The conclusions are listed as follows. The coal body around the hydraulic flushing hole deforms towards the hole, which is conducive to the development and conduction of cracks in the coal body, thus improving the permeability of the coal body. The horizontal stress of the coal body in the flushing area is effectively reduced. The pressure relief areas formed by each punching hole are interconnected to form a pressure relief strip, which is conducive to gas migration and extraction. According to the numerical simulation results and the actual project, the hydraulic flushing project scheme of 12090 working face in Shoushan No. 1 Coal Mine is determined. The upper side drilling angle is 5-6°, and the lower side drilling angle is −5-−4°. The drilling spacing is 4 m, and the length of each punching hole is 1 m. The spacing of flushing holes in each borehole is 7 m, and no flushing operation is carried out within 30 m from the roadway side. The flushing water pressure is 5-6 MPa, and the flow rate is 120-160 L/min. The practice shows that after adopting this scheme, the number of holes completed per month reaches 40 and the completion rate reaches 80%. The gas extraction concentration in the flushing hole is high and the gas attenuation is slow. After 50 days of extraction, the gas volume fraction in the flushing hole is 40%-60%, which is 2-3 times of that in the ordinary hole. After 120 days of extraction, the gas volume fraction in the flushing hole is still 20%. Hydraulic flushing effectively improves the gas extraction effect and reduces the gas content in coal seams. The average gas volume fraction of return air flow decreases to below 0.5%. The average daily footage of the working face increases from 2.4 m to 3.2 m, which improves productivity.

Coal mine underground wireless transmission analysis method

SHAO Shuicai, GUO Xudong, PENG Ming, ZHANG Gaomin

2022, 48(10): 123-128. doi: 10.13272/j.issn.1671-251x.18038

<Abstract>(180) <HTML> (50) <PDF>(31)

Abstract:
At present, the design and plan of the mine mobile communication system and the personnel and vehicle positioning system mainly depend on experience and field test. There are some problems such as heavy workload, difficult optimization of the layout of the communication base station and positioning substation and the antenna setting, etc. In order to promote the application of underground wireless transmission analysis methods in the design and plan of mine mobile communication system, personnel and vehicle positioning system, as well as the layout of the communication base station and positioning substation and the antenna setting, the application scope, advantage and disadvantages of different underground wireless transmission analysis methods are analyzed. ① Parabolic equation method has the advantages of simple algorithm and small computing memory resources. But it is not suitable for analyzing the influence of roadway undulation, support, longitudinal conductor and transverse conductor on wireless transmission attenuation in mines. ② The finite-difference time-domain method has a wide range of applications. But it requires a larger amount of computing memory resources. When analyzing the influence of roadway bending, undulation, irregular section shape and other factors on the wireless transmission attenuation in mines, the error is large. ③ The finite element method is the most widely used. The tetrahedral mesh can be used. Compared with the hexahedral mesh used in the finite difference time domain method, it can fit irregularly structured roadways better. But it requires the largest computing memory resources. The existing high-grade server memory capacity is difficult to meet the demand. It is suitable for small section, short distance, and low-frequency coal mine underground wireless transmission analysis. ④ The ray tracing method has the advantages of simple algorithm and minimum computing memory resources. But the application range is small. The ray tracing method is only suitable for analyzing the influence of factors such as high-frequency wireless working frequency, section shape, surrounding rock medium, and roadway bending on the wireless transmission attenuation of the mine. The ray tracing method cannot analyze the influence of factors such as different positions of an antenna on a roadway section, roadway branches, roadway undulation, supports, longitudinal conductor and transverse conductor on the wireless transmission attenuation of the mine. When analyzing the influence of low frequency band wireless operating frequency on the wireless transmission attenuation of the mine, the error is large. ⑤ The statistical analysis method has the advantage of simplicity and ease of use, but it requires a large amount of measured data. The coal mine underground roadway has a plurality of types, complex environment, branches, bends, and undulation. It has large measurement workload and low efficiency. It is difficult to measure the wireless transmission attenuation data under the conditions of different roadways and supports in the coal mine underground. It is difficult to analyze the wireless working frequency, the different positions of the antenna on the roadway section, the area and the shape of the roadway section, the bend of the roadway, the branch of the roadway, the undulation of the roadway, the surrounding rock medium, the supports, the longitudinal conductor and transverse conductor on wireless transmission attenuation of the mine.oadway, the surrounding rock medium, the supports, the longitudinal conductors, and transverse conductor on wireless transmission attenuation of the mine.

Optimization of coal loading performance of shearer screw drum

LI Minghao, NIU Hao, FAN Jiayi, ZHAO Lijuan, QIAO Jie

2022, 48(10): 129-135. doi: 10.13272/j.issn.1671-251x.2022050041

<Abstract>(291) <HTML> (58) <PDF>(23)

Abstract:
The screw drum is the direct mechanism of the shearer cutting coal and rock. The optimization design of geometric parameters and cutting strategy of screw drum has an important impact on improving coal loading performance of drum. The existing optimization design schemas of the screw drum based on the finite element method and the two-dimensional discrete element method are mostly based on a single factor or part factors. The influence of multiple design variables on the coal loading performance of the screw drum is not comprehensively considered. It is difficult to obtain the optimal solution of the geometric parameters and kinematic parameters simultaneously. In order to solve this problem, based on the test results of the physical and mechanical properties of coal, the coupling model of the shearer's screw drum cutting coal wall is established by using discrete element analysis software EDEM. The numerical simulation of coal loading performance of the shearer's screw drum is carried out. The single-factor method is used to analyze the influence of the spiral angle, diameter, hub diameter, cutting depth, drum rotation rate and traction speed of the screw drum on the coal loading performance. The three factors and three levels orthogonal test of the screw drum is designed based on the results of discrete element analysis. Through range analysis, the influence of geometric parameters of drum diameter, drum hub diameter and spiral rise angle, and kinematic parameters of cutting depth, drum rotation rate and traction speed on the coal loading performance of the screw drum is reduced in turn. According to the orthogonal test results, the optimal geometric parameters of the screw drum are 13° spiral rise angle, 1300 mm drum diameter and 475 mm drum hub diameter. The optimal cutting strategy is that the cutting depth is 600 mm, the drum rotation rate is 58 r/min, and the traction speed is 8 m/min. Under the optimal parameters, the coal loading rate of the screw drum is 76.39%, which is 15.82% higher than before.

Research on the application of 5G characteristics in intelligent mine

LIU Xin, FU Yuan, LI Chenxin

2022, 48(10): 136-141. doi: 10.13272/j.issn.1671-251x.2022070032

<Abstract>(587) <HTML> (77) <PDF>(69)

Abstract:
At present, the 5G construction in intelligent mine mainly focuses on the macro technology development direction, test methods and specific application scenarios of mine 5G. There is a lack of comprehensive analysis of the characteristics of various 5G application scenarios of intelligent mine. In order to solve this problem, the types of 5G application scenarios of intelligent mines are summarized. The communication requirements of the main application scenarios are sorted out. It is pointed out that sensor information backhaul applications have wide coverage requirements. The video information collection and backhaul applications have uplink large bandwidth transmission requirements. The real-time control information interaction applications have downlink low-delay transmission requirements. The automatic driving information collection and backhaul applications need to meet the differentiated transmission requirements of uplink large bandwidth and downlink low delay. According to the environmental characteristics and technical requirements of 5G application in intelligent mines, the overall architecture of mine 5G network with core network + bearer network + access network is proposed. ① The core network makes user plane function (UPF) and multi-access edge computing (MEC) sinking into the mine area to realize independent networking and independent operation of mine 5G. ② The information security module in the smart transport network is used for data security audit monitoring and transmission control to achieve safe isolation of surface and underground data. The network slicing and quality of service (QoS) management module is used to divide and isolate channels for different services, as to realize channel isolation for the coexistence of multiple services and transmission performance guarantee. ③ The access network adopts the mode of base station controller + base station collector + base station + terminal to realize 5G signal partition and on-demand coverage. According to the above architecture, the key technical scheme of 5G for diversified application requirements of intelligent mine is proposed. ① The network slicing technology is used to divide the mine 5G network into sensor slices, video return slices, real-time control slices and remote control slices. Combined with QoS indexes of transmission services, the service data is mapped to different slice resources for transmission,so as to realize the on-demand distribution of 5G network transmission. ② The flexible air interface scheduling mechanism is used to meet the on-demand scheduling of wireless resources. The air interface resource scheduling mode of "resource request-service buffer report resource allocation-service buffer-data transmission resource allocation" is adopted for large-bandwidth services. The mode is used to ensure the uplink transmission bandwidth. The reserved dedicated air interface resources are adopted for low-delay services to ensure the low delay of downlink transmission. ③ When a single frequency band cannot meet the uplink transmission requirement, multiple continuous or discontinuous carriers are aggregated into a larger bandwidth through carrier aggregation technology, effectively supporting the large bandwidth transmission requirement of mine 5G.

Open-circuit fault diagnosis method for switching tube of mine NPC three-level inverter

LIANG Hong

2022, 48(10): 142-150. doi: 10.13272/j.issn.1671-251x.17974

<Abstract>(128) <HTML> (48) <PDF>(17)

Abstract:
The inverter of the motor drive system in the mine hoist and belt conveyor mostly adopts neutral point clamped (NPC) three-level inverter. This inverter has a large number of switching tubes and high running frequency. Switching the working state of the switching tubes at high frequency in a short time and in complex working environment are prone to open-circuit fault. The fault signal has non-stationary characteristics. The existing fault diagnosis method for switching tube of NPC three-level inverter has the problems of difficult fault feature extraction, large calculation amount, and low fault accuracy. In order to solve the above problems, an open-circuit fault diagnosis method for switching tube of mine NPC three-level inverter based on probabilistic neural network (PNN) is proposed. Firstly, the phase voltage signals of three-phase of inverter are collected by oscilloscope. The phase voltage signals are processed by denoising and normalization. Secondly, the three-phase voltage is converted into two-phase rotating (d-q) coordinate system voltage by Clark transform and Park transform. The d-axis voltage is decomposed into multiple intrinsic mode function (IMF) using empirical mode decomposition (EMD). For different open-circuit faults, the variance contribution rate of each IMF is calculated. The variance contribution rates of the second, third and eighth IMF differ greatly. The three IMF represent different open-circuit faults. The mean, mean square and variance of the second, third and eighth IMF are calculated as the open-circuit fault feature vector of the inverter switching tube. Finally, the feature vector is input into the PNN for training and classification. The open-circuit fault diagnosis of the NPC three-level invert switching tube is realized. The experimental results show that the fault diagnosis method based on PNN has higher fault diagnosis accuracy than the fault diagnosis method based on CNN and SVM, and the average fault diagnosis accuracy reaches 97.75%.

2022 Vol. 48, No. 10

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