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Research on optimal arrangement strategy of top coal caving support sensors based on vibration characteristics of coal and gangue
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Yao WANG1, Shanguo YANG1, 2, 3, Mingke WU1, Bin MENG1, Zheng YANG1, Houguang LIU1, 2, 3
Journal of Mechanical Strength | 2025, 47(1) : 68 - 75
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Journal of Mechanical Strength | 2025, 47(1): 68-75
Experimental Research·Testing Technology
Research on optimal arrangement strategy of top coal caving support sensors based on vibration characteristics of coal and gangue
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Yao WANG1, Shanguo YANG1, 2, 3, Mingke WU1, Bin MENG1, Zheng YANG1, Houguang LIU1, 2, 3
Affiliations
  • 1.School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
  • 2.Jiangsu Province and Education Ministry Co-sponsored Collaborative Innovation Center of Intelligent Mining Equipment,Xuzhou 221116, China
  • 3.National Key Laboratory of Intelligent Mining Equipment Technology, Xuzhou 221116, China
Published: 2025-01-15 doi: 10.16579/j.issn.1001.9669.2025.01.008
Outline
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Aiming at the research on intelligent identification of caving coal and gangue, in order to provide a complete and effective vibration signal acquisition scheme of coal and gangue, an optimal layout strategy of tail beam sensors of caving coal hydraulic supports based on vibration characteristics of coal and gangue was proposed. Firstly, the modal analysis of the tail beam model was carried out, extracting the vibration mode matrix,and the effective independent method was used to select the measuring points. Secondly, the vibration signals of coal falling and gangue falling at the corresponding primary measuring points from the tail beam test bench were obtained, and the feature extraction was carried out. Thirdly, the extracted features were visualized by t-distributed stochastic neighbor embedding(t-SNE) dimensionality reduction, and five features which were sensitive to the distinction between coal and gangue signals were selected as target features. Finally, the probability density functions of target features were estimated by the kernel density estimation method. The K-L(Kullback-Leibler) divergence was used to evaluate the approximation between combined signal of each measuring point and the complete signal and the difference between characteristics of coal and gangue. The evaluation indexes of coal and gangue vibration signals were constructed. Combined with Fisher information matrix criterion, a comprehensive evaluation index was formed to determine the optimal scheme of tail beam sensor arrangement. The results show that the sensor arrangement scheme determined by this method not only reduces the number of sensors on the basis of satisfying modal observability, but also makes the measured vibration signals have better coal gangue difference and information integrity.

Top coal caving  /  Vibration signal  /  Optimal sensor placement  /  Hydraulic support tail beam  /  Effective independence method  /  K-L divergence
Yao WANG, Shanguo YANG, Mingke WU, Bin MENG, Zheng YANG, Houguang LIU. Research on optimal arrangement strategy of top coal caving support sensors based on vibration characteristics of coal and gangue[J]. Journal of Mechanical Strength, 2025 , 47 (1) : 68 -75 . DOI: 10.16579/j.issn.1001.9669.2025.01.008
  • National Natural Science Foundation of China(52274162)
Year 2025 volume 47 Issue 1
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Article Info
doi: 10.16579/j.issn.1001.9669.2025.01.008
  • Receive Date:2024-05-13
  • Online Date:2026-03-18
  • Published:2025-01-15
Article Data
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History
  • Received:2024-05-13
  • Revised:2024-06-11
Funding
National Natural Science Foundation of China(52274162)
Affiliations
    1.School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
    2.Jiangsu Province and Education Ministry Co-sponsored Collaborative Innovation Center of Intelligent Mining Equipment,Xuzhou 221116, China
    3.National Key Laboratory of Intelligent Mining Equipment Technology, Xuzhou 221116, China

Corresponding:

YANG Shanguo, E-mail:
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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