工矿自动化

Implementation method of mine wireless relay emergency communication system

SUN Jiping, XU Qing

2021, 47(5): 1-8.. doi: 10.13272/j.issn.1671-251x.17764

<Abstract>(191) <HTML> (17) <PDF>(25)

Abstract:
The characteristics of the mine wireless relay emergency communication system are analyzed in this study. The wireless transmission power is limited by explosion-proof, the mine wireless transmission attenuation is large, and the wireless transmission distance is short. The working frequency band is not limited on the premise that the wireless communication equipment working underground does not interfere with each other and does not affect the normal operation of the wireless equipment on the ground when being taken out of the mine. The relay links are distributed in a chain in the roadway, and the overall network is in a tree topology. In order to solve the impact of the broken cable (cable or optical cable) caused by the coal mine accident on the mine emergency communication system, the implementation method of mine wireless relay emergency communication system is proposed. This method separates the data transmission link from the protocol control link to improve the flexibility of link control and the transmission efficiency of routing protocol signaling. The receiving/transmitting frequency bands and sub-channel frequencies of each base station in the link are set according to the physical sequence of base stations, which solves the problem of mutual interference between relay stations at all levels. In data link relay transmission, each relay station in the link can exchange data continuously with the front and back nodes, which solves the problems of bandwidth loss, relay delay and system stability caused by multi-level relay. The frequency molecular channel mode of the relay station with spatial multiplexing of zones solves the problem of limited frequency division channel resources. The fixed node-based transparent routing strategy simplifies the level-by-level route addressing and route discovery process and improves data forwarding efficiency. The redundant base station down-link routing strategy of normal link avoids the channel interference problem caused by the mixed transmission of primary and redundant nodes. The link breakpoint recovery method of redundant base stations, mobile terminal bridging and local access coordination improves the anti-fault ability of the link.

Design of mine inverter performance test system

RONG Xiang

2021, 47(5): 9-15. doi: 10.13272/j.issn.1671-251x.17671

<Abstract>(159) <HTML> (15) <PDF>(19)

Abstract:
In order to solve the problems of incomplete test items, low test efficiency, poor safety and low torque load accuracy of the existing mine inverter test system, a mine inverter performance test system consisting of power supply system, load test bench and measurement and control system is designed by using AC feedback load technology. The power supply system provides control and power supply for the measurement and control system and the load test bench. The load test bench adopts the frequency conversion load mode of common bus to provide load environment for the tested inverter. The measurement and control system monitors the operation status of the system in real time, and controls the power supply system and the load test bench through the measurement and control host. Based on WinCC configuration software, the test system software including monitoring and control module, test management module and safety management module is developed. The monitoring and control module is used to realize project information management, data acquisition, processing, storage, and control the equipment through the measurement and control host. The test management module is mainly used to manage test items, test procedures, test methods, etc. It can test the performance of the inverter such as leakage blocking, instantaneous power failure protection, output short circuit protection, overload protection, open phase protection, over voltage and under voltage protection, light load, traction characteristics, temperature rise, etc. The safety management module ensures the safety and control of the test process in terms of operation safety protection, equipment safety protection and system operation safety. The constant torque load control algorithm is proposed to improve the torque load accuracy. The test results show that the system has high test accuracy, and the torque load error is less than 1% within the rated torque range. The AC feedback load method is adopted, and the system power consumption rate does not exceed 30%. The electromagnetic compatibility of the system meets the standard requirements.

Research progress of low-power methane sensor

WANG Haibo

2021, 47(5): 16-23. doi: 10.13272/j.issn.1671-251x.17754

<Abstract>(174) <HTML> (16) <PDF>(31)

Abstract:
In order to meet the requirements of low power consumption, miniaturization, short response time, high reliability and good safety of distributed wireless methane sensors, the working principles and research progress of low-power catalytic combustion, thermal conductivity and electrical conductivity methane sensors based on micro-electro-mechanical system technology and nano materials are introduced. This paper analyzes their advantages and disadvantages, and proposes the development direction and prospect of low-power methane sensors. ① The low-power catalytic combustion methane sensor can measure low-concentration methane. However, it is easy to be poisoned and the sensor has low stability. Due to the need of being high operated temperature, the low-power catalytic combustion methane sensor generally has high power consumption. The average power consumption of the sensors can be reduced to less than 2 mW under the work mode of pulse operation. However, its stability is not high. The future research directions are to improve packaging or catalytic materials to enhance its anti-poisoning ability, and to study low-power catalytic combustion methane sensors without manual calibration by combining advanced algorithms such as artificial intelligence and machine learning. ② The low-power thermal conductivity methane sensor can to measure methane in the full range. It can measure both low-concentration and high-concentration methane at the same time. It can operate stably in mines and has strong adaptability to the underground environment of mines. It has the prospect of application of distributed wireless methane sensors. The future development direction is to improve the circuit module to realize the sleep-wake operation mode, and to study the integration technology of sensor elements and peripheral circuits to realize the on-chip integrated thermal conductivity methane sensing system to reduce the overall operation power consumption. ③ The low-power conductivity methane sensors are divided into room temperature type and micro-heating plate type. The room temperature conductivity methane sensor has lower power consumption but longer response time. The micro-heating plate conductivity methane sensor has relatively low power consumption. Combined with specific nano materials, it can respond to methane at lower operating temperature, and has the application prospect of low-concentration methane monitoring. However, the micro-heating plate methane sensor is generally sensitive to ambient humidity. The baseline is easily shifted, the adhesion of sensitive materials to electrodes is poor, the device repeatability and reliability are poor, and the sensitive materials and packaging process need to be further improved. The magnetron sputtering method is applied to deposit semiconductor oxide sensitive materials onto the electrodes to improve the adhesion of the materials, thereby improving the repeatability and reliability of the device. At the same time, it is necessary to combine the algorithm to correct the baseline shift to ensure the stable operation of micro-heating plate conductivity sensors. ④ From the perspective of the whole sensing system, the power consumption of the peripheral circuit of the sensing element is sometimes even higher than that of the sensing element itself. The future direction is to study the on-chip integrated methane sensor, which can greatly reduce the power consumption of the peripheral circuit and obtain extreme low-power methane sensor. ⑤ It is proposed to study advanced sensor self-calibration algorithms to realize distributed wireless low-power methane sensors without manual calibration or self-calibration.

Design of underground micro energy harvesting device

ZHAO Duan, ZHAO Jinjin, HE Yongxin, ZHUO Minmin, CHEN Hui

2021, 47(5): 24-29. doi: 10.13272/j.issn.1671-251x.17768

<Abstract>(101) <HTML> (19) <PDF>(11)

Abstract:
Underground wireless sensor nodes have limited energy. Hence, energy harvesting technology can be used to convert underground environmental energy into electrical energy to supply power to the sensor nodes, thus extending the life cycle of wireless sensor nodes. The light intensity in the low light environment of the underground roadway is tested and analyzed. The results show that the light intensity is 50-170 Lux in the range of 1-2 m from the light source, which is in line with the working range of photoelectric materials such as amorphous silicon and perovskite. It verifies the feasibility of converting the underground low light energy into electrical energy. An underground micro energy harvesting device is designed by using 30 cm×40 cm amorphous silicon photovoltaic panels, BQ25505 power management chip and lithium batteries, which can convert the underground low light energy into electrical energy and store it. According to the characteristics of discontinuous energy in the underground environment, an energy caching mechanism is designed. In this mechanism, a rechargeable lithium battery with smaller capacity is used as the energy caching battery, and a lithium battery with larger fixed capacity is selected as the backup battery. When the voltage of the rechargeable lithium battery reaches the design value, the sensor node is powered by the rechargeable lithium battery. And when the voltage is insufficient, the backup battery is switched to supply power to ensure the normal operation of the wireless sensor node. The micro energy harvesting device has been tested in the laboratory and underground. The results show that the device can output milliwatt power with the light intensity above 50 Lux. When the underground light intensity reaches 170 Lux or more, the device can use the converted energy to power the low-power wireless sensor node without backup battery. When the light intensity does not reach 170 Lux, the energy caching mechanism coordinates the rechargeable lithium battery and the backup battery to power the wireless sensor node, which improves the node life cycle effectively.

Design of mine-used hot-wire-based optical fiber wind speed sensor

WANG Jiqiang, LI Zhen, MENG Hui, HOU Moyu, DONG Guofeng, YANG Qingshan, LIU Tongyu

2021, 47(5): 30-34. doi: 10.13272/j.issn.1671-251x.17740

<Abstract>(135) <HTML> (22) <PDF>(15)

Abstract:
Real-time and accurate acquisition of wind speed data is helpful for precise control of mine wind flow. The existing mine wind speed sensors such as hot-wire type, impeller type, ultrasonic vortex street type and differential pressure type are either susceptible to interference, or unable to detect breeze, or small ranged. Moreover, all these sensors obtain wind speed through electrical signals, and there are safety risks. Based on the light-to-heat conversion characteristics of doped optical fiber and fiber Bragg grating(FBG) temperature measurement principle, a mine-used hot-wire-based optical fiber wind speed sensor is designed. The light energy is converted into heat energy through the doped fiber, and demodulating the amount of FBG center wavelength drift under heat exchange is used to calculate the temperature change of the sensor probe and the wind speed. In the FBG demodulation process, the grating multiplexing technology is applied to realize multiple sensors sharing the same light source and demodulator, which reduces the cost. Laboratory test results show that the FBG center wavelength drift is non-linearly related to wind speed. In the lower wind speed range, the higher the pump light source power, the greater the FBG center wavelength drift range, and the higher the wind speed detection sensitivity. When the wind speed is 0-0.57 m/s, the sensor sensitivity is 1 370 pm/(m·s-1), and the highest resolution is 0.7 mm/s. The response time of the sensor decreases with the increase of wind speed, and the wavelength dynamic range increases with the increase of wind speed. The field test results show that the sensor can realize the detection of breeze and low wind speed in coal mines with good stability, and the monitoring value can show the change of wind speed on site accurately.

Research on mine safety situation forecast and early warning

LI Xiangong, SONG Xuefeng, ZHANG Minghui, TANG Run, LIU Feng

2021, 47(5): 35-39. doi: 10.13272/j.issn.1671-251x.17756

<Abstract>(90) <HTML> (12) <PDF>(15)

Abstract:
Based on the Internet of Things technology, obtaining the mine safety big data and making full use of the data are helpful to realize the forecast and early warning of mine safety situation. Taking the gas explosion accident as an example, by analyzing the cause of the accident, a mine safety situation evaluation index system is constructed, and each evaluation index is quantified. Based on the long and short-term memory(LSTM) network and the Bayesian network, a mine safety situation forecast model is proposed. According to the mine safety monitoring data, the mine safety situation evaluation index forecast values are obtained through the LSTM. The risk probability of mine safety accidents is inferred from Bayesian networks based on the evaluation index forecast values to obtain mine safety situation forecast. Based on the safety situation forecast results, an early warning mechanism is established. 4 warning levels and response departments are classified according to the warning situation, and corresponding early warning measures are established. An inversion of a gas explosion accident in a coal mine is used as an example, and the results show that the forecast results of mine safety situation based on LSTM and Bayesian network are consistent with the actual situation.

Research on visual semantic method of mine personnel behavior

WANG Gechen, YAN Yuhan, LIU Xiaowen, DING Enjie

2021, 47(5): 40-45. doi: 10.13272/j.issn.1671-251x.17775

<Abstract>(269) <HTML> (26) <PDF>(11)

Abstract:
The personnel behavior detection in underground coal mines is the focus of sensor mine construction. However, the existing personnel behavior detection methods based on electromagnetic waves, wearable devices and computer vision cannot integrate time, location, behavior, environment and other factors to judge whether the behavior of mine personnel is safe. A visual semantic method of mine personnel behavior is proposed, which generates statements describing personnel behavior in videos through characteristic extraction, semantic detection, characteristic reconstruction and decoding. The InceptionV4 network and the I3D network are used to extract the static and dynamic characteristics of the video images, and the parallel dual attention mechanism based on the spatial location attention model and the channel attention model is introduced into the InceptionV4 network so as to improve the characteristic extraction ability of the network. In order to solve the problem of the inconsistency between video content and visual semantics, the semantic detection network is introduced to add advanced semantic tags to video characteristics to generate embedded characteristics. The embedded characteristics are input into the decoder together with video characteristics and semantic characteristics, and the characteristic reconstruction module is introduced in the decoding process. Reconstructing video characteristics by obtaining the hidden layer state of the decoder enhances the correlation between video characteristics and description statements, and improves the accuracy of visual semantic generation. MSVD, MSR-VTT public data set and mine own video data set are used for experiments, and the results show that the method has good semantic consistency, can obtain the key semantics in the video accurately and better reflects the true meaning of the video.

Feature fusion based fault diagnosis of hoist inverter

WU Chuanlong, CHEN Wei, LIU Xiaowen, SHI Xinguo, LIU Ke, REN Xiaohong

2021, 47(5): 46-51. doi: 10.13272/j.issn.1671-251x.17772

<Abstract>(79) <HTML> (12) <PDF>(11)

Abstract:
The difficulty in fault diagnosis of mine hoist inverters lies in extracting the features that characterize faults. At present, signal processing methods are mainly used to obtain fault statistical features, or the fault depth features are extracted by neural networks. In the actual working environment, the hoist inverter is affected by factors such as background noise and load changes. Therefore, it is difficult to obtain features that can characterize the faults effectively by using a single feature extraction method, resulting in low fault diagnosis accuracy of the hoist inverter. In order to solve the above problems, a fault diagnosis method of hoist inverter based on the fusion of statistical features and depth features is proposed. Firstly, the Hilbert-Huang transform(HHT) is used to conduct modified ensemble empirical mode decomposition(MEEMD) of the inverter output current signal so as to obtain the fault statistical features. At the same time, the squeeze and excitation with densely connected convolutional network(SE-DenseNet) is used to extract the depth features of the output current signal. Secondly, the local fisher discriminant analysis(LFDA) is used to perform fusion and dimensionality reduction processing on the combination of the two features to obtain low-dimensional fusion features of statistical features and depth features. Finally, the low-dimensional fusion features are input to the extreme learning machine to obtain inverter fault classification. Experiments are conducted for a single IGBT open-circuit fault in the hoist inverter. The results show that the low-dimensional fusion features obtained by this method are more capable of fault characterization than single features, which improves the fault recognition accuracy effectively.

Optimal arrangement of wind speed sensor based on directed path matrix method

LI Bingrui, WANG Wei, CHEN Fengmei, LIU Na

2021, 47(5): 52-57. doi: 10.13272/j.issn.1671-251x.2020110066

<Abstract>(114) <HTML> (11) <PDF>(7)

Abstract:
The existing mine wind speed sensor arrangement methods have problems as follows. The determined sensor branch cannot measure the wind speed accurately because the wind speed is smaller than the sensor start wind speed. Most of the methods need to be listed multiple matrices and the calculation is complicated. Moreover, the sensor positions selected by some methods are unreasonable. In order to achieve mine full coverage air volume monitoring without blind area, and to monitor the air volume variation in all roadways with the minimum number of wind speed sensors, the coverage of sensor branches is analyzed by using the directed path matrix, and the optimal arrangement of wind speed sensors based on the directed path matrix method is proposed. This method determines the unique directed path matrix based on the wind flow direction of the ventilation network diagram, determines the coverage of the branches, and selects the branch with the largest coverage to determine the position of the wind speed sensor. The results show that the optimal arrangement of wind speed sensors based on directed path matrix method can achieve mine full coverage air volume monitoring without blind area, and the number of sensors is less than or equal to the number of independent directed paths. Calculation analysis shows that when sensors are arranged according to this method, there is a measurement error of 6% in one sensor branch, the lowest impact on the ventilation network is 0.52, and the lowest impact on other branches is 0. Moreover, the calculation error decreases as the number of sensors increases. If the impact of sensor branch error on the ventilation network is controlled to be less than 1, more than 12 wind speed sensors should be arranged.

Research on the prediction model of coal spontaneous combustion temperature based on random forest algorithm

ZHENG Xuezhao, LI Menghan, ZHANG Yanni, JIANG Peng, WANG Baoyuan

2021, 47(5): 58-64. doi: 10.13272/j.issn.1671-251x.17700

<Abstract>(106) <HTML> (15) <PDF>(8)

Abstract:
The prediction accuracy of the traditional coal spontaneous combustion temperature prediction model is poor. The requirement of parameter selection for the prediction model based on support vector machine (SVM) is high. And neural network-based prediction model is prone to overfitting. In order to solve the above problems, a prediction model of coal spontaneous combustion temperature based on random forest algorithm is proposed. The model uses the coal spontaneous combustion temperature program experiment to select O2 concentration, CO concentration, C2H4 concentration, CO/ΔO2 ratio and C2H4/C2H6 ratio as coal spontaneous combustion warning index data, processes the index data and divides the data into learning set and test set. The learning set is sampled to form a decision tree and split according to the optimal characteristics of the decision tree to form a random forest. The parameters of the random forest algorithm are optimized by the mean square error value and the determination coefficient (R2) to construct the random forest model. The test set data is input into the trained random forest model to obtain the prediction result of coal spontaneous combustion temperature. The model comparison results show that compared with the coal spontaneous combustion temperature prediction model based on the particle swarm optimization-back propagation(PSO-BP) neural network algorithm and the support vector machine algorithm, the R2 value in the random forest test phase is 0.869 7, the R2 value in the PSO-BP test phase is 0.783 6, and the R2 value in the SVM test phase is 0.835 0. The results shows that the prediction model of coal spontaneous combustion temperature based on RF algorithm can predict coal spontaneous combustion temperature more accurately and has strong robustness and universality. The model solves the problem that the prediction model of coal spontaneous combustion temperature based on PSO-BP neural network algorithm and the prediction model of coal spontaneous combustion temperature based on SVM algorithm are prone to overfitting.

Target identification and precise positioning method based on underground moving image collectio

LIU Yi, ZHAI Guisheng

2021, 47(5): 65-70. doi: 10.13272/j.issn.1671-251x.17765

<Abstract>(102) <HTML> (15) <PDF>(9)

Abstract:
The positioning accuracy of the existing underground positioning method fluctuates greatly and is difficult to be further improved. In order to solve the problem, a target identification and precise positioning method based on underground moving image collection is proposed. The environmental images are collected by using the camera carried by the positioning target, and the collected raw images are pre-processed by the adaptive histogram equalization method. The deep learning technology SSD algorithm and data enhancement SSD algorithm are used to identify the underground mark target, and the monocular distance measuring method based on the pinhole imaging principle is applied for ranging and positioning. The experimental results show that compared with two traditional algorithms of gray image matching algorithm and characteristic image matching algorithm, the SSD algorithm has better adaptability to distance and angle changes, and the effective detection rate still reaches 89.2% at 4.5 m. The data enhancement SSD algorithm improves the robustness and the detection accuracy rate is 1.7% higher than that of the SSD algorithm. The algorithm can better adapt to the complex environment. The results of underground application show that the target identification and precise positioning method based on underground moving image collection can achieve satisfactory results in the range of 2-10 m. The measurement accuracy decreases as the distance increases.

Low-speed high-torque direct drive roller control scheme of belt conveyor

ZHU Longji, MA Yongwang, WANG Shuying

2021, 47(5): 71-76. doi: 10.13272/j.issn.1671-251x.17739

<Abstract>(299) <HTML> (99) <PDF>(57)

Abstract:
In order to solve the problems of low efficiency and large start-up shock of the belt conveyor main roller which is driven by an asynchronous motor with a hydraulic coupling, a direct drive roller control scheme of belt conveyor based on an external rotor permanent magnet synchronous motor(PMSM) is proposed. In order to realize low-speed high-torque control, a vector control strategy based on the rotating coordinate system with zero direct axis current is used. In order to solve the problem of small counter-electromotive force and low accuracy of rotor position and speed estimation during low-speed operation, the high frequency pulse signal injection method is applied to estimate the rotor position and speed. The experimental platform of direct-drive roller speed control system is established to conduct steady-state characteristic test, start-up characteristic test and load characteristic test. It is verified that the PMSM direct-drive roller control system has fast torque response and stable adjustable speed in the speed range of 0-90 r/min, which can meet the start-up speed regulation requirements of low-spreed high-torque belt conveyor.

Fault identification of rolling bearing based on multi hidden layers wavelet convolution extreme learning neural network

HUANG Chongqia

2021, 47(5): 77-82. doi: 10.13272/j.issn.1671-251x.2020110036

<Abstract>(104) <HTML> (15) <PDF>(9)

Abstract:
The working environment of coal mine rotating machinery is harsh, and the actual collected rolling bearing vibration signals show the characteristics of obvious nonlinearity and non-stationarity. Therefore, it is difficult to extract bearing fault characteristics. The traditional rolling bearing fault identification method based on ‘manual characteristic extraction+pattern identification’ is influenced subjectively. In order to solve the above problems, a rolling bearing fault identification method based on multi hidden layers wavelet convolution extreme learning neural network (MHLWCELNN) is proposed. The method combines the advantages of 1D convolution neural network, auto-encoder, extreme learning machine and wavelet function. The local connection and weight sharing mechanism of 1D convolution neural network is used to reduce the parameters to be learned greatly. The auto-encoder makes the algorithm applicable to the unlabeled samples of bearing vibration signals. The extreme learning machine is applied to determine the output weight so as to avoid falling into local optimum and improve the training speed. The wavelet function is used as the activation function to improve the resolution of the bearing time and frequency domain signals, thus improving the fault identification rate. The experimental results show that compared with similar methods, MHLWCELNN has higher identification accuracy and smaller standard deviation, and can identify different fault types of rolling bearings more stably. The F1 value of MHLWCELNN is higher than that of similar methods, which verifies its effectiveness on unbalanced data sets. Gaussian wavelet has higher resolution in both time and frequency domains, and is suitable to act as an activation function. And it is more appropriate to set the proportion of training set as 80%.

Multipath QoS routing algorithm based on Ad Hoc network in mine environment

YAN Shuailing, ZHANG Lei

2021, 47(5): 83-87. doi: 10.13272/j.issn.1671-251x.2020110026

<Abstract>(103) <HTML> (10) <PDF>(5)

Abstract:
In order to improve the integrity and accuracy of data transmission in mine environment, a multipath QoS routing algorithm based on Ad Hoc network is proposed. The algorithm introduces the blockchain into the routing establishment process. Firstly, the nodes are block encapsulated so that each node uses the Merkle tree to maintain its neighbor nodes. Secondly, the path viability is updated according to the time delay and viability time of the neighbor nodes and the blocks are connected. Finally, the sum of intermediate node correlation values, path length and path formation sequence are used as path selection criteria in turn to filter the main and alternative paths for data transmission. The routing is maintained from three aspects as follows. ① The blockchain is applied so that each node retains the information of its neighbor nodes. The routing can be restored by querying the routing table of the previous node where the path is broken and using the neighbor nodes for substitution. ② After the reliability assessment of the main path drops to a certain level, the alternate path can be enabled. ③ The algorithm re-initiates routing querying at the source node. The simulation results show that compared with AODV, the DSR algorithm has a lower bit error rate and better path viability under different number of nodes, data packet transmission rate and node movement speed.

Path tracking control of crawler mobile platform based on bang-bang control with boundary layer

WU Mingyang, LI Xiaobo, DAI Jiahui, LEI Shiwei, PAN Changsong, XUE Chunrong

2021, 47(5): 88-94. doi: 10.13272/j.issn.1671-251x.2021030049

<Abstract>(137) <HTML> (19) <PDF>(8)

Abstract:
In order to realize the path tracking of the on-off valve-controlled crawler mobile platform that cannot be closed-loop speed regulated, considering the low speed of the platform and the ability to turn in place, a bang-bang path tracking control algorithm with a boundary layer without the bottom wheel speed control is proposed based on pure pursuit algorithm. Based on the geometric and kinematic relations, a target point tracking model of crawler mobile platform is established. Taking the heading angle deviation as the switching function, the method controls the forward and reverse rotation of the crawlers on both sides through the bang-bang algorithm, and introduces the boundary layer thickness parameter to reduce the switching frequency of the solenoid valve. According to the Lyapunov stability theory, it is proved that all states of the system outside the boundary layer are stable, but the heading angle deviation inside the boundary layer is unstable at the origin. Moreover, the smaller the look ahead distance is, the faster the heading angle deviation diverges. Based on the Matlab/Simulink and Recurdyn joint simulation platform, the rectangular target path is selected, and the simulation verifies the path pursuit effect of the bang-bang algorithm with different look ahead distances, and compares it with the pure pursuit algorithm of closed-loop speed regulation. A prototype test platform is built to conduct ground tests on the bang-bang path tracking control algorithm. The simulation and test results show that the bang-bang path tracking control algorithm can control the on-off valve-controlled crawler mobile platform to track straight or broken-line paths. Moreover, the tracking accuracy of the algorithm at corners is higher than that of the pure pursuit algorithm path of closed-loop speed regulation. The steady-state error is less than 9 cm, which meets the requirements of autonomous navigation walking of coal mine robots.

Scratch detection and removal method for coal microscopic images

LI Yao, LENG Siyu, LEI Meng, ZOU Liang

2021, 47(5): 95-100. doi: 13272/j.issn.1671-251x.2021020054

<Abstract>(176) <HTML> (7) <PDF>(18)

Abstract:
Coal microscopic image preprocessing mainly includes coal scratch detection and removal. It is difficult to extract spatial shape characteristics accurately and refine edge information effectively for coal scratch detection based on the Hough transform algorithm and it is prone to miss detection and false detection. In order to solve the above problems, a coal scratch detection method based on semantic segmentation is proposed. This method introduces the residual structure to improve the spatial attention model, and embeds the model into U-Net which uses the VGG convolutional layer as the image characteristic encoder to obtain the semantic segmentation of coal scratches. In order to solve the problem that the fast-moving image restoration algorithm makes the texture difference and visual artifacts between the coal scratch removal area and the surrounding area, an image restoration algorithm based on improved area matching is proposed to remove coal scratches. The effective removal of coal scratches is achieved by using k-nearest neighbor image block search, cross-scale and rotation angle search strategies, and an image block offset distance measurement based on Euclidean distance. The experimental results show that the coal scratch detection method based on semantic segmentation can reflect the edge details of coal scratches accurately, has better spatial characteristic analysis performance, and improves the accuracy of coal scratch detection. The method adopts the image restoration algorithm based on improved area matching to remove coal scratches. Therefore, the texture characteristics of the coal scratch removal area and the surrounding area are more consistent, and the overall visual effect of the image is improved.

Coal mine equipment condition monitoring system desig

CAO Xiangang, DUAN Xinyu, ZHANG Mengyuan, LEI Zhuo, LI Yanchuan

2021, 47(5): 101-105. doi: 10.13272/j.issn.1671-251x.2020120065

<Abstract>(90) <HTML> (9) <PDF>(16)

Abstract:
In order to solve the problem of low transmission efficiency caused by high concurrency of equipment monitoring data during the simultaneous operation of underground equipment groups in coal mines, a design scheme for coal mine equipment condition monitoring system is proposed. The system eliminates the heterogeneity of sensor networks effectively through the data integration gateway. Different sensors are registered in the data integration gateway, and the sensor network protocol adapters are used to call different sensor network protocol resolution interfaces to eliminate the heterogeneity of sensor networks, generate unified format of Java Script Object Notation (JSON) data, and send the data to the corresponding message push service. Through the point-to-point transmission through the Queue channel in the ActiveMQ message queue, the data transmission service pushes messages to the network transmission model in real time to obtain high concurrent transmission of equipment status data and ensure the real-time and reliability of monitoring data. The Netty network transmission model is used to avoid the increase in server load caused by empty polling and improve the efficiency of monitoring data transmission. In the process of data collection, multiple equipment operating at the same time lead to an increase in the frequency of data sampling and the number of concurrent requests from sensor terminals. The Epoll mode in Netty model prioritizes the ready I/O connections so as to reduce the empty polling. The test results show that as the number of concurrent requests in the system increases, the CPU usage of the system with the Java NIO model is 28% higher than that of the system with the Netty model. When the number of concurrent requests in the system is the same, the average response time of the system with the Java NIO model is longer than that of the system with the Netty model. The application of Netty model can improve the high concurrency processing capability of coal mine equipment condition monitoring system effectively and meet the requirements of high efficient transmission of equipment monitoring data.

Research on automatic location of single-phase leakage fault zone in coal mine power network

GAO Hongjie, ZHAO Jianwen, GUO Xiucai

2021, 47(5): 106-111. doi: 10.13272/j.issn.1671-251x.2021030057

<Abstract>(111) <HTML> (14) <PDF>(16)

Abstract:
The necessity of automatic single-phase leakage fault zone location is analyzed in this paper from two aspects, namely, the need for leakage detection and the value of leakage fault zone location. Three single-phase leakage fault mechanism models' principles are introduced, namely, fault steady-state process sequence network model, fault transient state process sequence network model and transient steady-state integrated sequence network model, and the problems are pointed out as follows. The steady-state characteristics are applicable to systems with ungrounded neutral points, and in the system with grounded arc suppression coil, there are blind spots in the steady-state characteristics. The inconsistency of transient steady-state modeling methods leads to inconsistent subsequent fault signals. For research on single-phase leakage fault mechanism models, it is suggested to integrate the transient and steady-state processes for accurate modeling and explore the data-driven fault process research. The principle of the fault characteristic signal processing method based on wavelet (packet) and other algorithms is introduced, and the problems are pointed out as follows. The existing fault characteristic law studies mainly focus on line selection and distance measurement. There are few research on the signal characteristics and its distribution of the upstream and downstream zones of the fault. The obtained fault characteristic law is mainly for permanent faults, and there are few researches on arc faults. The extracted signal characteristics are aliased. For the study of fault characteristic law, it is suggested to study the mechanism and characteristic law of arc faults and study the practical and effective signal analysis and characteristic extraction algorithms. The principle of single-phase leakage fault zone location method based on steady state and transient state quantities is introduced, and the problems are pointed out as follows. The steady-state characteristics are easily affected by the compensation effect of the arc suppression coil, which leads to inaccurate zone location. The transient characteristics are fast decaying and unstable, and cannot be used to locate faults such as voltage zero crossing and high resistance grounding. The single-phase leakage fault characteristics are weak and easily affected by unstable fault arcs and random factors. For research on automatic single-phase leakage fault zone location methods, it is suggested to apply pattern identification, artificial intelligence, digital twin and other technologies. These technologies are used to explore fault zone location methods for small-current grounding systems applicable to different neutral point operation modes, and eliminate the influence of arc suppression coils on fault identification. For weak and complex fault signals, special high-precision sensors are studied to achieve accurate acquisition of signal detection.

Tunneling equipment camera mirror cleaning device

DONG Mengyang

2021, 47(5): 112-115. doi: 10.13272/j.issn.1671-251x.2021010010

<Abstract>(102) <HTML> (18) <PDF>(30)

Abstract:
The tunneling working surface is characterized by large dust and high humidity, and coal mud is easily attached to the camera mirror when the tunneling equipment is working. In order to solve the above problems, a camera mirror cleaning device for tunneling equipment is proposed. The device uses nozzles to blow and dry the coal mud attached to the camera mirror. The rotary table drives the pneumatic motor to rotate to the center of the camera mirror, and the pneumatic motor drives the dust removal brush to rotate and clean so as to remove the coal mud. However, there are limitations in the working parameters of the dust removal brush (bristle compression relative to the camera mirror, bristle diameter and iron sheet mounting pitch). During the rotation of the rotary table, if the camera housing exerted too much force on the dust removal brush, the rotary table would not be able to rotate to the predetermined position. In order to determine the reasonable working parameters of the dust removal brush, the cleaning effect of the device under different working parameters of the dust removal brush is tested. The test results show that when the iron sheet mounting pitch is 8 mm, the bristle diameter is 0.2 mm, and the bristle compression relative to the camera mirror is 2.6-5.3 mm, the working performance of dust removal brush is the best, the coal mud removal rate is more than 90%, and the coal mud removal rate of the camera mirror center is 100%.

3D model lightweight technology

CHEN Long, GUO Jun, ZHANG Jianzhong

2021, 47(5): 116-120. doi: 10.13272/j.issn.1671-251x.17723

<Abstract>(314) <HTML> (10) <PDF>(40)

Abstract:
When the data volume of the 3D model increases, the model loading speed decreases and the browsing is not smooth. In order to solve the above problems, a 3D model lightweight technology based on the analysis of Cesium's model format 3DTiles is proposed. The texture images in the model are merged according to the material and the model mesh to achieve correct texture mapping and reduce the number of DrawCall calls. The texture and vertices are compressed to reduce the file size of the texture image. Mipmap is used to generate texture images of different resolutions, which is helpful for network transmission. LOD and triangle simplification technology are used to reduce the number of vertices and triangles of the model gradually. According to the complexity of the model, the depth of the octree is adaptively selected to load a certain part of the model on demand so as to reduce the amount of GPU rendering data and improve the smoothness of the 3D scene. Based on the Cesium platform, the frame rate before and after the lightweight processing of the 3D model is tested. The results show that the frame rate is increased after the lightweight processing of the 3D model, which achieves the purpose of efficient loading of the 3D model and smooth browsing.

2021 Vol. 47, No. 5

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