Drum of Shearer undertakes the main function of coal falling and loading, and its performance directly affects the working efficiency of the shearer. Therefore, in order to realize the analysis of the performance of the shearer drum, the MG2 × 55/250-BW shearer drum was the engineered object. Combining the physical and mechanical properties experiment results of coal samples, the coupling model of the drum cutting in complex coal seam was established using discrete element method. The falling-coal characteristics of the spiral drum were studied under different working conditions, and the falling-coal trajectories of the coal and rock particles were fitted. Based on a virtual prototype, the variations of the coal loading rate and lump coal rate with different design parameters were determined by studying the falling-coal effect and loading performance of the drum. Considering the drum performance, multi-objective optimization theory was used to determine the optimal operating and structural parameters. The results indicate that, in the process of drum cutting, the cutting depth has the most significant effect on the coal loading rate, while, the blade spiral angle has the least significant. Moreover, with the increase of the cutting depth of drum and the traction speed, the lump coal rate increases. While, with the increase of the drum rotation speed and the blade spiral angle, the lump coal rate decreases. It is found that when the cutting depth of the drum is 597 mm, the traction speed is 5.4 m/min, the drum rotation speed is 104.8 r/min, and the blade spiral angle is 12° the performance of the drum is optimal. Compared with the falling-coal trajectories before optimization, the displacements of the coal and rock particles ejected along the optimal falling-coal trajectories increase in the coal loading direction. The loading rate and lump coal rate of the drum increase by 6.05% and 12.27%, respectively. The load fluctuation of the drum decreases, and the productivity increases.
This paper combined the field test sampling, construction technology of complex coal seam, virtual prototype technology, bidirectional coupling technology, data processing theory, image fusion method, and the deep learning theory. It focuses on the key technologies of the coal and rock cutting state identification, such as "the acquisition and processing of the coal and rock cutting state data information set, sample expansion, identification and classification". The EDEM and RecurDyn were used to build the high-precision three-dimensional simulation model of complex coal seam and the bidirectional coupling model between shearer cutting parts, and the simulation group was set based on the actual cutting state of the shearer. Based on the simulation results of Discrete Element Method-Multi Flexible Body Dynamics (DEM-MFBD), the one-dimensional original vibration acceleration signals of the key components of the shearer cutting part were determined, including spiral drum, rocker arm shell, and square head. After transforming one-dimensional original signal data into the time-frequency images by STFT, morphological wavelet image fusion technology was used to realize the effective fusion of characteristic information of spiral drum, rocker arm shell, and square head under different working conditions. The basic database for coal and rock cutting state recognition was also constructed to provided original samples with rich and precise characteristic information for the expansion of recognition system data. Based on the deep learning theory, a DCGAN-RFCNN recognition network model with strong ability to perceive the cutting state information of coal and rock and rapid recognition and classification was constructed. Also, the experimental platform of shearer cutting coal and rock was built, where the coal and rock cutting state recognition network was trained and tested based on the migration learning theory. Through the statistical test results, the accuracy of coal and rock cutting state recognition is 98.64%, which realizes the accurate recognition of coal and rock cutting state.
The recognition of cutting state of coal-rock is the key technology to realize “unmanned” mining in coal face. In order to realized real-time perception and accurate judgment of coal-rock cutting state information, this paper combined the field test sampling, construction technology of complex coal seam, virtual prototype technology, bidirectional coupling technology, data processing theory, image fusion method, and deep learning theory to carry out multi domain deep fusion experimental research on multi-source heterogeneous data of coal and rock cutting state. The typical complex coal seam containing gangue, inclusion, and minor fault in Yangcun mine of Yanzhou mining area was taken as the engineering object. The high-precision three-dimensional simulation model of the complex coal seam that can update and replace particles was constructed. Based on the simulation results of Discrete Element Method-Multi Flexible Body Dynamics (DEM-MFBD), the one-dimensional original vibration acceleration signals of the key components of the shearer cutting part were determined, including spiral drum, rocker arm shell, and square head. After transforming one-dimensional original signal data into two-dimensional time–frequency images by Short-time Fourier Transform, morphological wavelet image fusion technology was used to realize the effective fusion of characteristic information of spiral drum, rocker arm shell, and square head under different working conditions. Based on the deep learning theory, the DCGAN-RFCNN (Deep Convolutional Generative Adversarial Networks-Random Forest Convolutional Neural Networks) coal and rock cutting state recognition network model was constructed. Combining convolution neural network with random forest recognition classifier, RFCNN coal and rock cutting state recognition classification model was constructed, and the recognition network model was trained to obtain the model recognition results. Through the comparative experimental analysis of the RFCNN network model with different recognition network models and different synthetic sample numbers in the recognition network, the effectiveness of the recognition network model was verified. The results show that: When synthetic samples are not included in each working condition in the RFCNN model, the average recognition rate is 90.641%. With the increase of the number of synthetic samples, the recognition rate of coal and rock cutting state increases. When the number of synthetic samples added to each working condition reaches 5000, the recognition effect is the best, and the average recognition rate reaches 98.344%, which verifies the superiority of enriching the data set by using the improved DCGAN network. Also, the RFCNN outperformed the other variants: it obtained higher recognition accuracy by 25.085, 21.925 and 19.337%, respectively, over SVW, CNN, and AlexNet. Also, the experimental platform of shearer cutting coal and rock was built, where the coal and rock cutting state recognition network was trained and tested based on the migration learning theory. Through the statistical test results, the accuracy of coal and rock cutting state recognition is 98.64%, which realizes the accurate recognition of coal and rock cutting state.
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