Aiming at the problems of low accuracy of coal gangue recognition and difficult recognition of mixed gangue rate, a coal rock recognition method based on modal fusion of RGB and infrared is proposed. A fully mechanized coal gangue transportation test bed is built, RGB images are obtained by camera, and infrared images are obtained by industrial microwave heating system and infrared thermal imager. the image data of the whole coal, whole gangue, and coal gangue with different gangue mixing as training and test samples, identify the released coal gangue and its mixing rate. The AlexNet, VGG-16, ResNet-18 classification networks and their convolutional neural networks with modal feature fusion are constructed. results: The classification accuracy of ResNet networks on RGB and infrared image data is higher than AlexNet and VGG-16 networks. The early convergence network performance of ResNet is verified through the convergence of different models. The recognition rate of the network is 97.92 the confusion matrix statistics, which verifies the feasibility of the application of modal fusion method in the field of coal gangue recognition. The fusion of modal features and early models of ResNet coal gangue, which is the basic premise for realizing intelligent coal caving.
Based on the two-way coupling technology of Discrete Element Method-Multi Flexible Body Dynamics (EDM-FMBD), a virtual caving coal wall is established by using the discrete element software, EDEM. The rigid flexible coupling model of the tail beam of caving supports is established by using multibody dynamics software, RecurDyn. The stiffness of the oil cylinder is calculated by using the solid–liquid spring coupling theory and is replaced by a spring. By simulating the process of a coal rock collapse impacting the tail beam, the dynamic signal from the coal rock collapse impacting the tail beam to crushing in the coal caving stage of the comprehensive caving working face is studied, and the test is carried out underground. The angular acceleration at the hinge point of the tail beam is the largest and shows a variation pattern of "large at both ends and small in the middle". The definition of a "low amplitude band" on the surface of the tail beam is proposed. The force signal at the hinge point of the front link is the strongest and is the best measurement point for the force sensor; the angular acceleration signal at the hinge point of the tail beam is the strongest and it is the best measurement point for the angular acceleration sensor. The results have practical implications for the identification of the coal gangue and the adaptive control of support for integrated top coal mining.
The development of intelligent and unmanned coal mining has put forward higher requirements on the service life and dynamic reliability of shearer cables. However, it is difficult to comprehensively consider the complexity of hosting conditions of coal mining working face and the dynamic characteristics of cables in different towing systems in the design and development of cables. The cables are periodized by pitch and have the same cross-sectional structure and properties. Based on the homogenization theory and volume average principle, the cable was assumed to be an orthotropic elastomer, and the tensile experimental method and finite element method were combined to calibrate the cable equivalent mechanical parameters. Based on the Absolute Node Coordinate Formulation (ANCF) method, the rigid-flexible coupled virtual prototype co-simulation model of shearer cable towing system was constructed to obtain the kinetic and kinematic parameters of each node of the cable and study the dynamic gradual change characteristics of the cable in different working areas. This research method has an important theoretical significance and engineering application value for the acquisition of dynamic characteristic parameters of shearer cables and the optimal design and dynamic reliability of cables.
:In order to improve the reliability of shearer's planet carrie in complicated seam, using the MG400/951-WD shearer model as the research object, baseing on the cutting coal theory, drum's general force and torque load is obtained by Matlab. Combing with rigid-flexible virtual prototype coupling established a virtual prototype model with flexible planet carrie, weak links are found through the simulation. Combing with the theory of reliability sensitivity design, robust design theory and the theory of performance degradation, the influence of the planet carrie's design variables to reliability sensitivity gradient is analyzed, a multi-objective optimization evaluation function of planet carrie is established, the optimal design variables is obtained by improved particle swarm optimization. The results show that the maximum stress decreased by 56.388% and design variable sensitivity tends to be stable.Combing with the theory of reliability sensitivity design, robust design theory and the theory of performance degradation, a theory of collaborative coupled virtual prototype technology with reliability design is proposed, which has important engineering application values.
In order to improve the accuracy of simulation parameters used in the discrete element simulation test of a fully mechanized top-coal caving process and further explore the intelligent fully mechanized coal caving technology, this research work studies the influence of particle characteristics on the dynamic response of tail beams under the impact of caving coal rock in the process of coal caving. Based on the interface technology, the EDEM–RecurDyn–AMESim multi-domain collaborative simulation top-coal caving support is a built-model of a hydraulic mechanical integration system for scraper conveyors, which is used to simulate the coal caving process of the top-coal caving support to obtain the vibration signal of the tail beam of the top-coal caving support. This model can also be used to convert it into a two-dimensional time-spectrum image using the short-time Fourier transform (STFT) algorithm. Several groups of simulation tests were carried out on different particle radii, standard deviation of particle normal distribution, and particle slenderness ratio. The time-domain information and frequency-domain information obtained from the simulation were analyzed and compared. Combined with the vibration signal of the tail beam measured on the spot, the optimal setting parameters of the multi-field collaborative virtual prototype simulation were obtained. Compared with the data measured in the coal mine, the relative error of the maximum vibration value of the tail beam is only 3.8%, the minimum relative error is 5.5%, and the relative error of the root mean square value is 14%, which verifies the method and simulation results. This method solves the problems of difficult on-site sampling, high risk coefficient, and high test cost and promotes the development of an intelligent process of coal mining.
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