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Research on Identification Technology of Hydrodynamic Excitation Disease of Gate
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Jian-wei GAO1, Jia ZHU1, Jun-jie LI2, Wen-jie SHEN1, Qiu HE1, Yong CHEN1, Qing-lin JIANG2, Jian-bin GUO2
Water Resources and Power | 2025, 43(9) : 110 - 113
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Water Resources and Power | 2025, 43(9): 110-113
Research on Identification Technology of Hydrodynamic Excitation Disease of Gate
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Jian-wei GAO1, Jia ZHU1, Jun-jie LI2, Wen-jie SHEN1, Qiu HE1, Yong CHEN1, Qing-lin JIANG2, Jian-bin GUO2
Affiliations
  • 1.East China Tongbai Pumped Storage Power Generation Corporation Limited, Hangzhou 310000, China
  • 2.School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
Published: 2025-09-25 doi: 10.20040/j.cnki.1000-7709.2025.20242180
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Affected by hydrodynamic excitation and other factors, the opening-closing operation of hydraulic gates exhibits multi-field coupling effects and complex nonlinear dynamic characteristics, leading to difficulties in identifying equipment safety states. Test data of gate operation demonstrate that artificial neural network algorithms can identify hydrodynamic excitation disease features and accurately predict its development trends. To address this, BP and GA-BP neural networks were employed to construct identification and prediction models for hydrodynamic excitation disease. These models were applied to identify and forecast the effective values of reel vibration, with model performance evaluated using metrics including Relative Error (RRE), Mean Absolute Percentage Error (MMAPE), and Root Mean Square Error (RRMSE). Compared to the BP model, the results indicate that the GA-BP model achieves reductions of 20.77% in RRE, 4.74% in MMAPE, and 6.27% in RRMSE, demonstrating superior fitting to measured samples and enhanced stability with extended prediction durations, thus providing critical technical support for engineering risk mitigation and hazard prevention.

hydraulic gate  /  GA-BP neural network  /  engineering safety  /  winch opening and closing operation  /  hydrodynamic excitation disease
Jian-wei GAO, Jia ZHU, Jun-jie LI, Wen-jie SHEN, Qiu HE, Yong CHEN, Qing-lin JIANG, Jian-bin GUO. Research on Identification Technology of Hydrodynamic Excitation Disease of Gate[J]. Water Resources and Power, 2025 , 43 (9) : 110 -113 . DOI: 10.20040/j.cnki.1000-7709.2025.20242180
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Year 2025 volume 43 Issue 9
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Article Info
doi: 10.20040/j.cnki.1000-7709.2025.20242180
  • Receive Date：2024-11-08
  • Online Date：2025-12-15
  • Published：2025-09-25
Article Data
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History
  • Received：2024-11-08
  • Revised：2024-12-08
Funding
Affiliations
    1.East China Tongbai Pumped Storage Power Generation Corporation Limited, Hangzhou 310000, China
    2.School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2025.20242180
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
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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