2020 Vol. 46, No. 4

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Research on concept and system architecture of smart mining workface in coal mine
GE Shirong
2020, 46(4): 1-9. doi: 10.13272/j.issn.1671-251x.2020030070
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Definition of smart mining workface was put forward as a production system that could independently operate coal mining process without manual interaction. It was pointed out that the smart mining workface depended on intelligent machine, autonomous sensing and controlling and independent operation, which focused on the purpose of unmanned mining. Five intelligent elements of the smart mining workface were introduced, which included the autonomous perception, autonomous decision-making, autonomous control, autonomous collaboration and autonomous interaction. It was suggested that the smart mining workface could be assessed as primary level, intermediate level and advanced level according to different levels of the intelligent elements. In this meaning, the development of smart mining workface would be a gradual process, which closely connected to the building up of the automation, digitalization and internet technology for mining workface. It was predicted that China's coal mines would come to primary smart level in 2025. Some coal mines with better condition would reach up intermediate smart level by 2035 with the equipment capable for autonomous perception and control. The unmanned coal mining was expected to be realized in 2045. The evaluation index for smart mining workface was introduced, and the cobweb map and analytic hierarchy methods were described for evaluating the level of smart mining workface. In order to construct the smart mining workface system, a coupling model was put forward including three intelligent control loops from the coal mining process, equipment operation and coal flow. The model and control loops were beneficial to improving the adaptive performance of coal seam cutting, roof supporting and coal flow transportation. Finally, the smart mining workface architecture with functional, technical and organization dimensions was proposed, which could provide the integration across machines and platforms to form a collaborative and co-managed intelligent mining ecology.
Research on coal and gas outburst collaborative prediction technology and its applicatio
XU Xuezhan
2020, 46(4): 10-16. doi: 10.13272/j.issn.1671-251x.2019080073
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In view of problems that existing coal and gas outburst prediction technology is difficult to achieve omnidirectional continuous prediction in the field of time and space,has low utilization rate of anti-outburst prediction results and lag in the release of prediction information, a set of multi-factor, omnidirectional, time and space continue coal and gas outburst collaborative prediction technology system for modern high-yield and efficient mines was proposed. According to space-time dimension of coal and gas outburst, coal and gas outburst risk prediction is divided into regional outburst risk prediction based on space dimension and local outburst risk prediction based on time dimension. Through in-depth mining and analysis of regional and local outburst prediction data such as geological structure, coal seam depth, coal quality, soft coal distribution, drill cuttings index, initial gas velocity, etc., regional and local outburst risk prediction results are obtained and effectively integrated in accordance with certain rules to achieve continuous monitoring and early warning in time and space dimension. Supplementary anti-outburst information integrated management and control platform and WTC-1 gas outburst data acquisition device are developed to provide software and hardware support for the realization of the coal and gas outburst collaborative prediction technology. Field application results show that prediction accuracy of the technology exceeds 90%, single approval time of anti-outburst prediction result is shortened to 22.5% of the original time, and the anti-outburst information comprehensive management and control platform has captured the danger of coal and gas outburst many times in advance, which effectively improves the mine anti-outburst information utilization efficiency.
Detection method of coal quantity and deviation of belt conveyor based on image recognitio
HAN Tao, HUANG Yourui, ZHANG Lizhi, XU Shanyong, XU Jiachang, BAO Shishui
2020, 46(4): 17-22. doi: 10.13272/j.issn.1671-251x.2019080055
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Traditional convolutional neural network（CNN） is a single-task network. In order to realize simultaneous detection of coal quantity and deviation of belt conveyor, two CNNs are used to detect coal quantity and deviation respectively, resulting in large network volume, many parameters, large computation and long operation time, which seriously affect detection performance. In order to reduce complexity of network structure, a detection method of coal quantity and deviation of belt conveyor based on multi-task convolutional neural network (MT-CNN) was proposed, which could make two tasks of coal quantity detection and deviation detection to share the same network underlying structure and parameters. On the basis of VGGNet model, MT-CNN is constructed by increasing scale of convolution kernel and pooling kernel, reducing the number of channels in full connection layer, and changing structure of output layer. After preprocessing the acquired conveyor belt images, such as graying, median filtering and extracting region of interest, the training dataset and test dataset are acquired, and the MT-CNN is trained. The trained MT-CNN is used to identify and classify the conveyor belt images, so as to realize accurate and fast detection of coal quantity and deviation. The experimental results show that detection accuracy of the trained MT-CNN in the test dataset is 97.3%, and average processing time of each image is about 23.1 ms. The effectiveness of the method is verified by field operation.
Strata gas enrichment rules under double mining influence of underlying protected seam
ZHOU Yinbo, HUANG Jilei, WANG Siqi, ZHAO Zhou, XU Jingxin, CHEN Liang
2020, 46(4): 23-27. doi: 10.13272/j.issn.1671-251x.2019080004
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Abstract:
During mining process of underlying protected seam, strata are damaged twice under double mining influence, whose caving characteristics are different from that of ingle seam mining, and gas movement and enrichment status would be influenced. Taking underlying protected seam mining of Qingdong Coal Mine as research background, strata caving characteristics and gas enrichment laws under double mining influence are researched by use of similar simulation experiment and theoretical analysis. The results show that under the double mining influence of underlying protected seam, the strata in stope do large scale movement in a short time, and the secondary mining influence areas are formed periodically. Gas stably enriching in stope flows into the secondary mining influence areas, where gas enriches in a short time with periodical change. According to the research results, high level boreholes are carried out in No.828 working face of underlying protected seam in Qingdong Coal Mine for gas drainage. The secondary mining influence areas and gas enrichment laws are verified by through analyzing gas drainage concentration, and good gas control effect is achieved.
A discrimination method of mine water inrush source
JIANG Zihao, HU Youbiao, JU Qiding, ZHOU Lu, ZHANG Shuying
2020, 46(4): 28-33. doi: 10.13272/j.issn.1671-251x.2019070087
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For problems of existing discrimination methods of water inrush source based on hydrochemical characteristics such as complex process, difficulty to distinguish single water inrush point, neglecting the correlation among hydrochemical characteristics, large calculation amount and so on, a discrimination method of mine water inrush source based on Bayes-extension discrimination method was proposed combining with Bayes discrimination method and extension discrimination method. Twenty-six groups of water samples from different aquifers and four groups of water samples to be judged in Pa'er Coal Mine are obtained, the content of SO2-4, Cl-, HCO-3, K++Na+, Mg2+, Ca2+ in the water samples is taken as hydrochemical characteristic indexes, and thus matter-element models of the water samples are established. The correlation degrees between the water samples to be judged and the known water samples are obtained by use of extension discriminant method. The miscalculation loss of Bayes discriminant method and density function of the water samples to be judged with the known water samples are combined to get Bayes-extension solutions. The type of water inrush sample is discriminated according to the minimum value of Bayes-extension solutions. Piper trilinear diagram, extension discriminant method and Bayes-extension discriminant method are adopted separately to discriminate the type of mine water inrush samples. The results show that Piper trilinear diagram is difficult to accurately discriminate a certain type of water samples, extension discriminant method has misjudgment, while Bayes-extension discriminant method can accurately discriminate the type of water inrush sample.
Research on free radical variation law in spontaneous combustion and oxidation process of coal gangue
WANG Sidong, LIU Yingzhong, XU Chao
2020, 46(4): 34-37. doi: 10.13272/j.issn.1671-251x.2019120025
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In order to research spontaneous combustion and oxidation characteristics of coal gangue from micro level, free radical variation law in spontaneous combustion and oxidation process of coal gangue were analyzed by using experimental system for free radical determination of coal gangue. The results show that with the increase of temperature, free radical concentration increases slowly and then increases rapidly, g factor decreases slowly and then increases rapidly and then decreases, and CO production quantity increases slowly and then increases rapidly. The higher the sulfur content is, the lower the critical temperature for coal gangue from slow oxidation to rapid oxidation and the temperature required for rapid growth of g factor is, and the greater the CO production quantity is. It can be seen that sulfur content promotes spontaneous combustion of coal gangue, so desulfurization treatment should be carried out before accumulation of coal gangue.
Research on a wireless positioning algorithm for underground personnel
LIU Xia, LI Guoliang, ZHANG Lingfeng, WANG Yu, SUN Hu, HUANG Qineng, DING Qiong
2020, 46(4): 38-45. doi: 10.13272/j.issn.1671-251x.2019110023
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For problems that traditional underground fingerprint positioning algorithm needs to collect a large number of fingerprint data and positioning accuracy is not high, a wireless positioning algorithm for underground personnel based on differential evolution and artificial fish swarm algorithm optimization least square support vector machine (DEAFSA-LSSVM) was proposed. Firstly, the underground experimental area is divided into several small areas, and the fingerprint database is established by Kriging interpolation algorithm. Secondly, the hybrid intelligent algorithm of differential evolution and artificial fish swarm is used to optimize regularization parameters and width of kernel function, and the least squares support vector machine algorithm model is established. The wireless acquisition and reception terminal is used to collect wireless information data of undetermined site, and its small area is calculated by the least squares support vector machine algorithm model. Finally, the wireless information data in the small area is used for real-time positioning by weighted K-nearest neighbor algorithm. The experimental results show that the algorithm has high convergence speed and high classification accuracy, the classification accuracy is 98.87%; and has high positioning accuracy, the average positioning error is 1.51 m, which is 18.82% higher than that of the least squares support vector machine algorithm without optimization.
Prediction algorithm of coal and gas outburst based on IPSO-Powell optimized SVM
WU Yaqin, LI Huijun, XU Danni
2020, 46(4): 46-53. doi: 10.13272/j.issn.1671-251x.2019110018
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In view of problems of coal and gas outburst prediction algorithm based on support vector machine(SVM) that prediction accuracy and reliability are not high, classification of nonlinear data is not considered when selecting kernel function, and extraction effect of influence factors of coal and gas outburst with nonlinear distribution is poor, a coal and gas outburst prediction algorithm which combines improved particle swarm optimization (IPSO) algorithm with Powell algorithm(IPSO-Powell) to optimize SVM was proposed. Firstly, main control factors of coal and gas outburst, namely initial velocity of gas emission, gas pressure, mining depth, gas content and failure type of coal body is extracted through grey correlation analysis and used as input samples of the algorithm. Then, IPSO algorithm is used to improve precocious convergence of particle swarm optimization (PSO), and Powell algorithm is used to search the local optimal solution, the penalty coefficient and Gaussian kernel function parameters of the SVM algorithm are optimized, the optimal parameter combination of SVM is obtained. Finally, the main control factors of coal and gas outburst are input to the SVM for classification , and compared with the actual test set classification results to achieve coal and gas outburst prediction. The simulation results show that compared with the SVM algorithm, GA-SVM algorithm and PSO-SVM algorithm, the application of IPSO-Powell optimized SVM algorithm for coal and gas outburst prediction has higher prediction accuracy, and improves the computational efficiency of the SVM solution process, which can meet the accuracy and reliability requirement of coal and gas outburst prediction with an accuracy rate of 95.9%.
Research on transmission characteristics of magnetically coupled resonant wireless power transfer system
SUN Xiangyu, GONG Lijiao, LI Hongwei, JIN Zhengwei
2020, 46(4): 54-59. doi: 10.13272/j.issn.1671-251x.2019090001
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In view of the influence of coil parameters and load resistance changes on transmission performance of magnetically coupled resonant wireless power transfer(MCR-WPT) system, output power and efficiency of the system are derived using equivalent circuit model of two coil structure MCR-WPT system. The relationship between coil mutual inductance, load resistance and system output power and transmission efficiency, as well as the relationship between coil wire diameter, coil turns and mutual inductance are analyzed. Three-dimensional coil model is established using finite element simulation software COMSOL, and multiple sets of two-coil MCR-WPT experimental systems are built to verify the theoretical analysis results. Research results show that by increasing coil wire diameter and turns, the output power and transmission efficiency of the system can be improved, but the influence of turns on transmission efficiency is more obvious than that of coil wire diameter, and the transmission distance increases when the maximum output power is obtained as the turns increases; as the load resistance continues to increase, the system output power and transmission efficiency both increase first and then decrease, proving that output power and transmission efficiency both have maximum value, but the optimal load is different when the system output power and transmission efficiency reach the maximum value, that is, there is no optimal load resistance that can maximize the output power and transmission efficiency at the same time.
Influence of coil spacing on performance of resonant wireless power transmission system
QIAO Zhenpeng, WANG Saili, GUO Guo, YANG Zheng, LIU Tao, YU Kaiwei
2020, 46(4): 60-65. doi: 10.13272/j.issn.1671-251x.2019090007
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For coil spacing is a key indicator to measure wireless power transfer distance, influence of coil spacing on performance of resonant wireless power transmission system was studied. A four-coil magnetically coupled resonant wireless power transmission system model and a PCB planar spiral coil model were established. The influence of the distance between source coil, transmitting coil, receiving coil and load coil on system transmission efficiency and output voltage were analyzed through Matlab simulation, and an experimental platform was built to verify the simulation results. The results show that the coil spacing has a significant effect on the transmission efficiency of the system. The load voltage output by the system presents a law of increasing first and then decreasing as the increase of spacing d23 between transmitting coil and receiving coil and the spacing d12 between the source coil and transmitting coil. Appropriate d23 and d12 can make the system output voltage reach the maximum value, and the increase of the distance d34 between the source coil and the transmitting coil makes the output voltage gradually decrease. Therefore, the receiving coil and load coil should be as close as possible in the system design.
Research on open-circuit fault diagnosis of three-level inverter
WAN Hong, REN Xiaohong, FAN Jinyu, YU Xiao, DING Enjie
2020, 46(4): 66-74. doi: 10.13272/j.issn.1671-251x.2019070045
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In view of problems of complicated calculation and low accuracy existed in traditional open-circuit fault diagnosis methods of three-level inverter, an open-circuit fault diagnosis method of three-level inverter based on wavelet analysis and particle swarm optimization support vector machine (WT-PSO-SVM) was proposed. On the basis of analyzing the characteristics of the three-phase current signal of the three-level inverter, the current signal is decomposed by using the three-layer wavelet, and the energy of each frequency band is extracted as the fault feature. After the energy was extracted by wavelet transform, the extracted energy under partial faults is very close and cannot be distinguished effectively, and then the positive half-cycle proportional coefficient is introduced as auxiliary feature. The normalized energy and the positive half-cycle proportional coefficient are used as feature vectors to input support vector machines for classification training, and the parameters of support vector machine are optimized by particle swarm optimization algorithm to achieve the best classification effect, so as to realize fault diagnosis. The experimental results show that the WT-PSO-SVM method can effectively identify open-circuit faults of the three-level inverter, which has higher diagnostic accuracy and speed than other fault diagnosis methods, and still has a higher fault identification accuracy of 97.3% in the case of variable load and noise.
Inspection behavior recognition of underground power distribution room based on improved two-stream CNN method
DANG Weichao, ZHANG Zejie, BAI Shangwang, GONG Dali, WU Zhefeng
2020, 46(4): 75-80. doi: 10.13272/j.issn.1671-251x.2019080074
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The monitoring video of underground power distribution room has a long duration and complex behavior types, and the traditional two-stream convolutional neural network (CNN) has poor recognition effect on such behaviors. In view of the problem, the two-stream CNN method was improved, and a method of inspection behavior recognition of underground power distribution room based on improved two-stream CNN was proposed. Through scene analysis, the inspection behaviors are divided into five types: standing detection, squatting detection, walking, standing record, and sitting down record, and the inspection behavior dataset IBDS5 is produced. Each inspection behavior video is divided into three parts, corresponding to the start of inspection, middle inspection and end of inspection; RGB images representing spatial features and continuous optical flow images representing motion features are obtained by random sample from three parts of the video, and the images are input to spatial flow network and time flow network respectively for feature extraction; weighted fusion of predicted features of the two networks are performed to obtain inspection behavior recognition results. The experimental results show that the spatial-temporal and dual-stream fusion network based on ResNet152 network structure with a weight ratio of 1∶2 has high recognition accuracy, and Top-1 accuracy reaches 98.92%;the recognition accuracy of the proposed method on the IBDS5 dataset and the public dataset UCF101 are better than existing methods such as 3D-CNN and traditional two-stream CNN.
A fault diagnosis method of belt conveyor
ZHANG Zhe, TAO Yunchun, LIANG Rui, CHI Peng
2020, 46(4): 81-84. doi: 10.13272/j.issn.1671-251x.2019120001
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Aiming at problems of insufficient fault state sample data and low accuracy in fault diagnosis of belt conveyor by traditional shallow neural network, a fault diagnosis method of belt conveyor based on synthetic minority oversampling technique (SMOTE) and deep belief network (DBN) was proposed. Fault state sample data of belt conveyor is generated by SMOTE to overcome imbalance distribution of the sample data. The sample data is input into DBN, fault features in the data are extracted by means of unsupervised layer-by-layer training, and fault diagnosis ability is optimized by means of supervised fine-tuning to achieve accurate fault diagnosis of belt conveyor. The simulation results show that the method improves fault diagnosis accuracy of belt conveyor.
Research on a bearing early fault features extraction method
ZHANG Meng, MIAO Changyun, MENG Deju
2020, 46(4): 85-90. doi: 10.13272/j.issn.1671-251x.2019090020
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In view of problems that early fault signals of rolling bearings are submerged by background noise and fault characteristics are not obvious, a bearing early fault feature extraction method based on wavelet packet decomposition and CEEMD was proposed. Matlab software is used to perform rapid spectral kurtosis analysis on the collected vibration signals, and the center frequency and bandwidth of the band-pass filter is determined according to maximum kurtosis principle and used to design the band-pass filter. Wavelet packet decomposition and CEEMD decomposition are perform to the signal filtered by the band-pass filter, and effective intrinsic modal function (IMF) components are selected according to the kurtosis and correlation coefficient and used to reconstruct the wavelet packet signal. Characteristic frequency of bearing early fault signal is extracted by envelope spectrum analysis of the reconstructed wavelet packet signal. The method reduces background noise interference through spectral kurtosis analysis, enhances the fault impact signal through wavelet packet decomposition, and combines CEEMD with wavelet packet decomposition to solve the problem of modal aliasing and invalid components in classical EMD decomposition. The simulation results show that compared with traditional envelope demodulation algorithm, the background noise of the reconstructed signal is suppressed and the fault feature component is prominent, which verifies the feasibility and effectiveness of the proposed method.
Research on application of LoRa networking technology in belt conveyor transportation monitoring system
CHEN Xiaojing
2020, 46(4): 91-97. doi: 10.13272/j.issn.1671-251x.2019090038
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In view of the lack of wireless data transmission channel in belt transportation monitoring system, it is impossible to realize functions of self-diagnosis information upload after disconnection, wireless sensor access and emergency relay communication after disconnection, a LoRa wireless networking technology scheme applied to belt conveyor transportation monitoring system was proposed. In view of characteristics that chain networking structure is more suitable for multi-point extension of belt conveyor transportation monitoring system, LoRa chain networking mode is adopted. According to requirements of LoRa chain networking transmission mode, data access relationship between nodes is determined, the host node and the child node are designed as a master multi-slave transmission method, the node is routed according to adjacent relationship, so as to form single-layer chain network. In order to meet demand of data transmission, the communication mechanism of patrol inspection and response is adopted. The communication process follows regular time sequence, and the channel is used in an orderly manner. According to the above scheme, hardware and software design of LoRa wireless networking transmission node for belt conveyor transportation monitoring system are presented and tested. The test results show that the transmission performance of LoRa chain networking mode is reliable and stable, the power consumption of nodes in wireless transmission state is less than 300 mW, the transmission delay of 10 nodes is 1.12 s, and the packet loss rate is less than 0.6%, which can meet requirements of belt conveyor transportation monitoring system for wireless networking transmission function.
Research on deformation control of roadway in reverse fault fracture zone of Tunbao Coal Mine
LIU Ming, CAO Minyuan, WU Yuhai, LI Bo
2020, 46(4): 98-103. doi: 10.13272/j.issn.1671-251x.17532
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In reverse fault fracture zone of Tunbao Coal Mine, the coal pillar side roadway is affected by mining, which leads to large roadway deformation, roadway floor heave, coal pillar side wall heave, and local subsidence of roadway roof. Anchor stress was monitored by anchor dynamometer, and roadway stress distribution and roadway deformation were studied by means of PASAT-M detection. The results show that the peak value of anchor stress on coal pillar side of belt roadway appears at 10-15 m of the leading working face and the one of anchor stress on upper side of track roadway appears at 5-10 m of the leading working face. The anchor stress, surrounding rock stress and roadway deformation at different positions are all affected by T2 reverse fault, and the stress and deformation are distributed near the reverse fault. The medium risk area of surrounding rock stress is relatively concentrated within 25 m from the reverse fault. According to the research results, a comprehensive control measure of roadway reinforcement and large pore pressure relief was proposed. The results of engineering practice show that the measure provides a stable supporting force for surrounding rock and can apply to preventing and controlling surrounding rock deformation in the area affected by the reverse fault of Tunbao Coal Mine.
Design of automation device for durability test of manual reversing valve of hydraulic support
ZHAO Rui
2020, 46(4): 104-108. doi: 10.13272/j.issn.1671-251x.2020010008
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For problems of long test cycle, low efficiency and unable to realize automatic test in durability test of manual reversing valve of hydraulic support, an automation device for durability test of manual reversing valve of hydraulic support was designed. The device is based on reciprocating movement of hydraulic cylinder, and uses push-pull special tool of the manual reversing valve handle to drive handle of the manual reversing valve to swing from left to right to achieve purpose of automatic reversing. At the same time, software of measurement and control system is used to control actions of starting, reversing, collecting pressure, judging and counting of the device, so as to realize automatic test of durability test of the manual reversing valve. The practical application results show that the device has characteristics of simple structure, adjustable loading speed and high test efficiency, and durability test cycle is shortened from 165.6 h to 31.2 h.
Filtering capacitor resolution scheme of switching converter in mine-used intrinsic safety type power supply
CHEN Huili, LI Jie
2020, 46(4): 109-112. doi: 10.13272/j.issn.1671-251x.2019070085
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For problems of large output voltage ripple-wave and poor intrinsic safety performance existed in mine-used intrinsic safety type power supply caused by large filtering capacitor of switching converter, a filtering capacitor resolution scheme was proposed by use of active capacitor technology. Working principle, control strategy and mathematical model of the active capacitor were analyzed. The active capacitor technology transfers instantaneous power difference of DC bus to a non-DC bus capacitor for energy balance through controlling an internal full-bridge converter, so as to realize filtering capacitor resolution of DC bus. Application of the active capacitor technology in mine-used intrinsic safety type power supply was introduced by taking a power supply based on Buck converter as an example. The simulation and experiment results show that the active capacitance technology can meet intrinsic safety output requirement with small capacitor.
LI Bo.Analysis of influence of random vibration on mine-used vehicle-mounted explosion-proof lithium ion storage battery［J］.Industry and Mine Automation，2020,46（4）：113-116.
LI Bo
2020, 46(4): 113-116. doi: 10.13272/j.issn.1671-251x.2019050055
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At present,current sinusoidal vibration test method for mine-used lithium ion storage battery cannot fully reflect fatigue life and reliability of the battery structure, as well as vibration response of its internal structure and vibration characteristics under excitation state. Aiming at the above problems, the virtual excitation method commonly used in random vibration is used to analyze vibration response of mine-used vehicle-mounted explosion-proof lithium-ion storage battery. The change of explosion-proof performance and electrical performance of the battery under vibration condition is analyzed from four aspects of acceleration response, contact resistance, temperature and clearance of explosion-proof cavity. After 8 h vibration test of the tested lithium ion storage battery, although explosion-proof clearance at the flameproof cavity and shell cover still meets requirements of class I explosion-proof electrical equipment, the explosion-proof clearance significantly increases, and there is a risk of explosion-proof failure (explosion transmission) in long-term use under this working condition. Due to the process, material or assembly during fastening period, contact resistance of positive terminal post of the battery becomes larger, which significantly increases local heating. If temperature continues to rise, the lithium ion storage battery film would melt resulting in the risk of combustion or even explosion. The results show that the random vibration test method can fully expose defects of product structure design, provide effective data support for the overall structure, stress change, working stability analysis and fatigue life prediction of vehicle-mounted lithium ion battery under vibration conditions.

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