Coal top detection by conductively guided borehole radar wave imaging for open cut blast‐hole drilling

Zhou, Binzhong; Werken, Matthew van de; Huo, Jianjian

doi:10.1111/1365-2478.12874

Cited by 4 publications

(1 citation statement)

References 47 publications

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“…Ankita Singh et al 7 combined with the gray level information of coal and rock, selected the gray level threshold to segment the coal and rock image, and designed the gray level symbiosis matrix to achieve feature extraction of the segmented image, so as to achieve the purpose of identifying coal and rock with different properties. Matthew Van De Werken et al 8 realized real-time prediction of coal seams and coal seam roof rocks by using new forward-looking imaging technology based on borehole radar technology. Sushma Kumari et al, based on intelligent vision enhancement technology, realized depth perception of cutting targets of mining machinery through real-time image mosaic, image enhancement, CNN network and other processing methods, and achieved the goal of intelligent mining of mining machinery under harsh conditions.…”

Section: Introductionmentioning

confidence: 99%

Research on Key Technology of Coal and Rock Cutting State Identification

Zhang

Zhao

Bai-sheng

2022

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

Research on Key Technology of Coal and Rock Cutting State Identification

Zhang

Zhao

Bai-sheng

2022

Preprint

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Analysis and construction of the coal and rock cutting state identification system in coal mine intelligent mining

Zhang

Zhao

Bai-sheng

2023

Sci Rep

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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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Coal falling trajectory and strength analysis of drum of shearer based on a bidirectional coupling method

Zhang,

Zhao,

Shi

2024

Sci Rep

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The cutting and crushing of coal and rock containing gangue is the result of the coupling effect of multiple factors. The geometric parameters of the working mechanism, the kinematic parameters of the shearer, and the physical and mechanical properties of the coal and rock to be cut all affect the cutting and crushing process of the shearer. To study the coal falling trajectory of cutting coal and rock using a spiral drum, optimal cutting parameters were obtained, efficient cutting using a spiral drum was achieved, and analysis of the coal falling trajectory and strength of the drum of a shearer based on bidirectional coupling technology was proposed based on particle discrete element contact theory and virtual prototype technology. The discrete element method multi-flexible body dynamics two-way coupling method was used to obtain cutting and interactive information about the spiral drum for a complex coal seam with gangue. The cutting conditions of the spiral drum under different cutting depths, rotational speeds, and traction speeds were determined. The movement status of coal and rock particles was monitored under different working conditions. Coal falling trajectory equations for the coal and rock particles were compiled under different working conditions, and the coal falling trajectory curve was drawn. The optimal coal loading rate was used as the measurement standard for the coal falling trajectory, and the optimal coal falling trajectory of the drum was obtained through full factor experiments. The load of the drum and pick was extracted, their stress and deformation were analyzed, and fatigue life analysis was performed on the pick with the highest stress. The results indicate that the maximum deformation occurs on the cutting teeth that are cutting hard gangue. The stress of the tooth seat is mainly concentrated at the root of the tooth seat, and its maximum equivalent stress is less than the yield limit value of the selected material. Therefore, the material selection and structural design of the drum are safe and reliable. By building a coal mining machine cutting coal and rock experimental platform and monitoring the working status of the designed spiral drum, it meets the usage requirements. Based on industrial experiments conducted underground, the measured average coal loading rate of the shearer drum was 46.31%, achieving stable operation and verifying that the designed drum of the shearer has an efficient cutting ability.

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Coal top detection by conductively guided borehole radar wave imaging for open cut blast‐hole drilling

Cited by 4 publications

References 47 publications

Research on Key Technology of Coal and Rock Cutting State Identification

Research on Key Technology of Coal and Rock Cutting State Identification

Analysis and construction of the coal and rock cutting state identification system in coal mine intelligent mining

Coal falling trajectory and strength analysis of drum of shearer based on a bidirectional coupling method

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