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Online monitoring of shaft structure performance driven by digital twins
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Xiaofen JIA1, Yuchen ZHAO2, Baiting ZHAO2, Rui HU3, Zhenhuan LIANG3
China Safety Science Journal | 2026, 36(2) : 66 - 76
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China Safety Science Journal | 2026, 36(2): 66-76
Safety Technology and Engineering
Online monitoring of shaft structure performance driven by digital twins
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Xiaofen JIA1, Yuchen ZHAO2, Baiting ZHAO2, Rui HU3, Zhenhuan LIANG3
Affiliations
  • 1State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science and Technology, Huainan Anhui 232001, China
  • 2School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan Anhui 232001, China
  • 3School of Artificial Intelligence, Anhui University of Science and Technology, Huainan Anhui 232001, China
Published: 2026-02-28 doi: 10.16265/j.cnki.issn1003-3033.2026.02.0105
Outline
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To address the current issues of low intelligence level in coal mine working faces and insufficient research on the performance monitoring of shaft structures, a digital twin-based performance monitoring method for vertical shafts is proposed. Firstly, a five-dimensional framework for the digital twin of vertical shafts is proposed based on the operational mechanism and performance monitoring requirements of the shafts. Secondly, a digital twin of the shaft is established by combining virtual-real mapping technology with a finite element surrogate model for grid dimensionality reduction. The structural performance of the vertical shaft is predicted online through artificial neural network technology, where the predicted data is the real-time prediction of shaft structure performance data obtained during the shaft operation process using a shaft structure performance prediction model. The prediction model for the structural performance of the vertical shaft adopts the RBF surrogate model, and the Unity3D virtual engine platform is built to integrate the above functions and achieve online prediction of the structure performance of the vertical shaft. The results indicate that during the operation, by simulating 120 sets of stress and strain data under different working conditions, the average coefficient of determination between predicted and simulated values is 0.995 5, indicating a high correlation between the predicted strain and simulated strain, thus verifying the feasibility of the digital twin framework for vertical shafts. This provides an effective reference for the digital improvement of vertical shafts.

digital twin  /  shaft  /  structure performance monitoring  /  grid dimensionality reduction  /  surrogate model  /  radial basis function(RBF)
Xiaofen JIA, Yuchen ZHAO, Baiting ZHAO, Rui HU, Zhenhuan LIANG. Online monitoring of shaft structure performance driven by digital twins[J]. China Safety Science Journal, 2026 , 36 (2) : 66 -76 . DOI: 10.16265/j.cnki.issn1003-3033.2026.02.0105
Year 2026 volume 36 Issue 2
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2026.02.0105
  • Receive Date:2025-09-21
  • Online Date:2026-07-08
  • Published:2026-02-28
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History
  • Received:2025-09-21
  • Revised:2025-12-04
Funding
Affiliations
    1State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science and Technology, Huainan Anhui 232001, China
    2School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan Anhui 232001, China
    3School of Artificial Intelligence, Anhui University of Science and Technology, Huainan Anhui 232001, China
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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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