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- Network Launch 2022, Volume 48, Issue 1 Cover Catalogue Previous Issue
Online First have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
2022, 48(1): 1-6.
doi: 10.13272/j.issn.1671-251x.17881
Abstract: The paper puts forward a perception and alarm method of rock burst and coal and gas outburst based on temperature. The infrared thermal imager is used to monitor the temperature of objects, and the methane sensor is used to monitor the concentration of ambient methane. When the temperature of the objects is higher than the ambient temperature of the coal mine and the temperature of the exposed coal rock, and the number, volume and area of objects that are higher than the ambient temperature and the temperature of the exposed coal rock are large, it is determined that rock burst, coal and gas outburst, mine fire or gas and coal dust explosion accidents have occurred. The temperature of the high temperature object is further determine. If it is greater than the set threshold, it is determined that a mine fire or a gas and coal dust explosion accident has occurred. Otherwise, it is determined that a rock burst or a coal and gas outburst accident has occurred. The change of methane concentration is further analyzed. If the methane concentration rises rapidly, it is determined that a coal and gas outburst accident has occurred. Otherwise, it is determined that a rock burst accident has occurred. The paper puts forward a perception and alarm method of rock burst and coal and gas outburst based on velocity. The lidar, millimeter-wave radar, ultrasonic radar, binocular vision camera are used to monitor the moving speed of objects. The methane sensors are applied to monitor the concentration of ambient methane. When the moving speed of the object is not less than the set threshold, it is determined that rock burst, coal and gas outburst or gas and coal dust explosion accident have occurred. The number, volume and area of objects with abnormal velocity is further determined. If the number of objects with abnormal velocity is small, the volume and area are small, it is determined that a gas and coal dust explosion accident has occurred. If the number of objects with abnormal velocity is large, the volume and area are large, it is determined that a rock burst or a coal and gas outburst accident has occurred. The changes of methane concentration are further analyzed. If the methane concentration increases rapidly, it is determined that a coal and gas outburst accident has occurred. Otherwise, it is determined that a rock burst accident has occurred. A multi-information fusion method for perception, alarming and judging disaster source of rock burst and coal and gas outburst is proposed. The method monitors and integrates various information such as temperature, speed, acceleration, burial depth, sound, air pressure, wind speed, wind direction, dust, methane concentration, equipment status, micro-seismic, geosound, stress, infrared radiation, electromagnetic radiation and images so as to monitor the pressure and coal and gas outbursts. The source of the disaster is determined through the magnitude of parameter changes at different locations, the sequence relationship and sensor damage.
2022, 48(1): 7-15.
doi: 10.13272/j.issn.1671-251x.2021060015
Abstract: The alternating measurement mode roadheader positioning technology will produce cumulative measurement error in the process of multiple alternating measurement, which will affect the positioning precision of roadheader. At present, the research mainly focuses on the causes of single measurement error, error distribution law and error reduction methods, but there is no research results on the error distribution law of multiple alternating measurement. By analyzing the working principle and positioning process of alternating measurement mode roadheader positioning system, the positioning error model of roadheader is established. The accuracy of the error model is verified by the graphic method, and the results show that the positioning errors obtained by the graphic method and the error model are basically the same, and and there are only 10−3 orders of magnitude errors between them. The impact of angle measurement error, distance measurement error, moving step length and distance between the roadheader and measuring platform on roadheader positioning error is studied by error model. The results show that the larger the angle measurement error, the larger the curvature of the positioning error curve, that is, the faster the error grows. And the YT axis positioning error grows faster than the XT axis. The distance measurement error has a greater impact on the XT axis positioning error, and the smaller the distance measurement error, the smaller the initial XT axis positioning error. However, the error change speed is not affected. As the moving step length increases, the YT axis positioning error curvature increases, that is, the YT axis positioning error growing speed increases. The impacts of the distance between the roadheader and measuring platform and the moving step length on roadheader positioning error are basically equivalent. The orthogonal test method is used to analyze the impact degree of each factor on the positioning error of roadheader. The results show that the distance measurement error has the greatest impact on the positioning error of the XT axis, followed by the angle measurement error. The moving step length and the distance between the roadheader and the measuring platform have the smallest impact and the two have the same degree of impact. The angle measurement error has the greatest impact on the positioning error of the YT axis, followed by the moving step length and the distance between the roadheader and the measuring platform, and the impacts of the two are the same. The impact of the distance measurement error is the smallest. The range analysis method is used to obtain the optimal parameter combination to reduce the positioning error.
2022, 48(1): 16-20.
doi: 10.13272/j.issn.1671-251x.2021070027
Abstract: In order to solve the problems of the existing shearer drum load identification methods, such as difficult implementation of related algorithms, complex engineering implementation mode and high application difficulty, through analyzing the characteristics of the audio signal during shearer operation, a shearer drum load identification method based on audio recognition is proposed. In order to ensure that the audio signal in each analysis period has the same load condition under the same operation standard, the cutting current and the traction speed are introduced into the dynamic energy calculation as variables, and the dynamic energy normalization algorithm (DENA) is adopted to normalize the original audio signal of the shearer. The normalized signal is compared and analyzed with the signal in the standard operation condition library, and the difference between the two is judged by the maximum dissimilarity coefficient, so as to determine the characteristics of the drum load and realize the identification and judgment of the drum load. The test results show that DENA can effectively suppress the noise energy in the audio signal and improve the resolution of the key characteristic values in the audio signal. The boundary of the characteristic parameters of the audio signal is obvious when the shearer cuts coal and rock, and there is no cross aliasing phenomenon. Under ideal conditions, that is, when the maximum dissimilarity coefficient is less than 0.189, the total coal-rock interface recognition rate can reach 78.6%.
2022, 48(1): 21-25.
doi: 10.13272/j.issn.1671-251x.2021070043
Abstract: At present, many studies have come to the conclusion that the coal seam contains primary CO gas, but the possibility of CO being adsorbed by coal after CO generated in drilling construction is not considered. In order to explore whether there is primary CO in spontaneous combustion coal seam in Northwest China, the original coal seam in-situ drilling detection method is used to detect primary CO. Three test boreholes are arranged in a row along the roadway side in the solid coal area not affected by mining. After the boreholes are sealed, high-purity N2 is used to replace the gas in the closed gas chamber, and the gas in the boreholes is extracted by a special air pump, so as to eliminate the impact of CO generated by coal oxidation on the test results during the construction of in-situ detection boreholes. On the basis of analyzing the source possibility of primary CO in coal seam and its emission theory, the variation characteristics of gas concentration in closed borehole with time are discussed. The results show that volume fraction of O2 and CO in the sealed borehole decrease rapidly with the extension of sealing time, and the volume fraction of O2 is stable below 2% after 12 days. After 12 days, the CO volume fraction is lower than 10−12, and no CO gas is detected by gas chromatograp. The gas in the borehole is mainly N2. It is concluded that there is no primary CO gas in the tested coal seam. The results of coal breaking test in N2 environment and coal sample oxidation test at normal temperature and constant temperature show that CO gas detected at the initial stage of borehole sealing comes from coal breaking operation in drilling construction.
2022, 48(1): 26-32.
doi: 10.13272/j.issn.1671-251x.2021100006
Abstract: In order to solve the problem of autonomous deviation control of roadheader in complex roadway environment, the paper analyzes the deviation reasons of roadheader, defines the functional requirements of deviation correction control of roadheader, proposes a deviation correction control system of coal mine roadheader based on digital twin, and introduces the system composition. Taking the roadheader central position control as an example, the system deviation correction control mechanism is analyzed, and a deviation correction control method of the roadheader based on binocular vision image information is proposed. Taking the roadway image detected by binocular vision as the basic data, by extracting the characteristics of the roadway image and analyzing the relationship between the roadway coordinate system and the roadheader coordinate system, the position and attitude parameters of the roadheader relative to the roadway space are calculated, and the deviation correction control of the roadheader is carried out according to the solution results. The digital model and the positioning and orientation parameter database of the roadheader and the roadway are constructed, and the virtual remote deviation correction control of the roadheader is realized through the virtual-real mapping relationship. The experimental results show that the deviation correction control system based on digital twin can compensate the yaw angle and offset distance of the roadheader under different working conditions effectively. The deviation correction process can be displayed on the monitoring interface in real time, and the simulation results of deviation correction path planning are consistent with the actual working conditions.
2022, 48(1): 33-38.
doi: 10.13272/j.issn.1671-251x.2021070052
Abstract: The traditional pedestrian dead reckoning (PDR) algorithm has low positioning precision due to the accumulated errors of step size and heading, which can not meet the requirements of precise positioning of underground personnel. In order to solve the problem, a PDR algorithm for precise positioning of underground personnel based on long short-term memory (LSTM) personalized step size estimation is proposed. Firstly, the acceleration and gyroscope inertia information in the movement of underground personnel is collected, and the movement distance of each step is calculated to construct step size data. The LSTM model of personalized step size estimation of the underground personnel is obtained through off-line training. Secondly, in the online prediction stage, the underground personnel movement data such as acceleration, gyroscope and geomagnetism are collected in real-time through the mine intrinsically safe smart phone. The underground personnel movement step and step size of each step are obtained by using the step detection algorithm and personalized step size estimation model respectively. The heading angle is obtained by using the Kalman filtering and heading estimation algorithm. Finally, the current position of underground personnel is predicted according to step size estimation and heading angle. In Inner Mongolia Ordos Gaotouyao Coal Mine, the underground personnel movement data is collected for testing, and the results show as follows. The PDR algorithm for precise positioning of underground personnel based on LSTM personalized step size estimation has a step detection precision of 96.5% and a step size prediction precision of 90%. The algorithm has a relative positioning error of 2.33% in the real underground environment, which improves the personnel positioning precision in coal mine.
2022, 48(1): 39-43.
doi: 10.13272/j.issn.1671-251x.2021080018
Abstract: In the traditional belt conveyor coal flow detection device, the nuclear belt scale has certain safety and environmental protection hidden dangers, and the detection precision of electronic belt scale is easily affected by the factors such as belt tension and stiffness. Moreover, non-contact detection methods based on technologies such as ultrasound, linear laser stripes and binocular vision have problems such as poor real-time performance and large measurement errors. A coal flow detection method for conveyor belt based on time-of-flight(TOF) depth image restoration is proposed. The TOF camera is used to obtain the coal conveying image of the conveyor belt. The TOF image is equalized, and the frame difference method and the boundary following algorithm are used to remove the background noise and obtain the coal region of interest. In order to solve the problem of inaccurate edge information caused by flying pixel noise and multi-path error noise at the edge of TOF depth image, the intensity image-guided depth image restoration algorithm is proposed. The Canny edge detection algorithm is used to find similar edges between the depth image and the intensity image. Based on the effective edge information of the intensity image, the unreliable data of the edge of the depth image is corrected. Furthermore, the high-precision depth images are obtained based on Navier-Stokes equation and median filter. The coal area is divided at the pixel level, the coal volume calculation model is established to obtain coal flow of conveyor belt by combining the conveyor belt speed. The experimental results show that the detection error is less than 3.78%, the standard deviation is less than 0.491 and the average processing time is 83 ms, which meets the actual production requirements.
2022, 48(1): 44-51.
doi: 10.13272/j.issn.1671-251x.2021070048
Abstract: The amount of monitoring image information in unmanned working area of mine is large, and the hardware performance requirements are high in the image transmission and storage stage, which causes the problems of increased energy consumption and sudden decrease of the service life of sensor nodes. At present, when reconstructing mine monitoring image signal, the precision of compressive sensing measurement matrices such as Gause and Bernoulli is low. In order to solve the above problems, a new block Pascal compressive sensing measurement matrix (BPCSM) is designed. The BPCSM matrix uses the idea of non-uniform sampling and blocking in time domain, arranges multiple identical small-size Pascal matrices in a diagonal manner, and combines with the joint orthogonal matching tracking algorithm so as to realize the compression sampling and reconstruction of underground monitoring image signals. And the characteristics of orderly arrangement of row elements of Pascal matrices are used to strengthen the sampling of low frequency band of image signals so as to improve the reconstruction precision. The experimental results show that the reconstruction precision of BPCSM matrix for mine monitoring image signals is much higher than that of the commonly used measurement matrices such as Gause and Bernoulli. When the sampling rate is 0.3, the peak signal-to-noise ratio (PSNR) of the miner image reconstructed based on BPCSM matrix is about 26 dB, and the miner's facial contour is clear. When the sampling rate is 0.5, the PSNR of the miner image reconstructed based on BPCSM matrix has reached 30 dB, which can recover almost all the details of the miner image, indicating the better reconstruction performance of the BPCSM matrix. By selecting the appropriate Pascal matrix size, the reconstruction performance of the image signal can be further improved to meet the application requirements of the mine environment.
