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Identification and Location of Leakage Aperture of Underground Water Supply Pipeline in Coal Mine
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Jing-yi DU1, Zhen CHEN1, *, Jia-wei ZHANG1, Chen LI2, Rui GAO1, Peng WANG3
Science Technology and Engineering | 2025, 25(8) : 3296 - 3303
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Science Technology and Engineering | 2025, 25(8): 3296-3303
Automation and Computational Technology
Identification and Location of Leakage Aperture of Underground Water Supply Pipeline in Coal Mine
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Jing-yi DU1, Zhen CHEN1, *, Jia-wei ZHANG1, Chen LI2, Rui GAO1, Peng WANG3
Affiliations
  • 1 College of Electrical and Control Engineering Xi'an University of Science and Technology Xi'an 710054 China
  • 2 College of Communication and Information Engineering Xi'an University of Science and Technology Xi'an 710054 China
  • 3 College of Mechanical Engineering Xi'an University of Science and Technology Xi'an 710054 China
Published: 2025-03-18 doi: 10.12404/j.issn.1671-1815.2402034
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In order to quickly identify the location of the leakage point and the leak aperture in the coal mine, a model was proposed for identifying and locating the leak aperture by using the pressure and flow signals generated when the water supply pipeline leaked. Modal energy entropy and genetic algorithm combined with envelope entropy were used to optimize the parameters of variational mode decomposition (VMD), and then VMD was used to denoise the pressure signal. Convolutional neural network (CNN) was used to extract the deep feature sequence of pressure and flow signal, and the long short-term memory network (LSTM) was used to extract the time sequence of deep feature sequence to identify and locate the leak aperture. The experimental results show that compared with Kalman filter, mean value filter and low-pass filter, the variational modal decomposition with optimized parameters has higher root-mean-square error (RMSE), mean absolute error (MAE), signal-to-noise ratio (SNR) and normalized cross correlation (NCC), which indicates that it can effectively reduce noise components and retain effective signals. Compared with LSTM, the MAE, mean absolute percentage error (MAPE) and RMSE of CNN-LSTM in leak location decrease by 65.97%, 61.22% and 59.11%. In the identification of leak aperture, MAE decreases by 12.04%, MAPE decreases by 22.45%, and RMSE decreases by 3.29%, which proves that CNN-LSTM can make full use of the spatial and temporal characteristics of pipeline pressure and flow signals to identify the leak location and aperture, and its detection effect is more accurate and stable than LSTM.

variational mode decomposition (VMD)  /  convolutional neural networks (CNN)  /  long short-term memory (LSTM)  /  modal energy entropy  /  genetic algorithm (GA)  /  envelope entropy
Jing-yi DU, Zhen CHEN, Jia-wei ZHANG, Chen LI, Rui GAO, Peng WANG. Identification and Location of Leakage Aperture of Underground Water Supply Pipeline in Coal Mine[J]. Science Technology and Engineering, 2025 , 25 (8) : 3296 -3303 . DOI: 10.12404/j.issn.1671-1815.2402034
Year 2025 volume 25 Issue 8
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Article Info
doi: 10.12404/j.issn.1671-1815.2402034
  • Receive Date:2024-03-21
  • Online Date:2025-07-29
  • Published:2025-03-18
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  • Received:2024-03-21
  • Revised:2024-12-16
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    1 College of Electrical and Control Engineering Xi'an University of Science and Technology Xi'an 710054 China
    2 College of Communication and Information Engineering Xi'an University of Science and Technology Xi'an 710054 China
    3 College of Mechanical Engineering Xi'an University of Science and Technology Xi'an 710054 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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