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Vibration amplitude prediction method for turbine rotor sliding bearing based on YOLOv8 optimized attention mechanism
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Yachao LI1, Haoyu LIU2, Haokang XU2, Yuhan GUAN2, Zhantong QI2, Yujiong GU2
Thermal Power Generation | 2025, 54(5) : 122 - 131
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Thermal Power Generation | 2025, 54(5): 122-131
• Thermal energy science research •
Vibration amplitude prediction method for turbine rotor sliding bearing based on YOLOv8 optimized attention mechanism
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Yachao LI1, Haoyu LIU2, Haokang XU2, Yuhan GUAN2, Zhantong QI2, Yujiong GU2
Affiliations
  • 1.Huayuan Power Plant, CHN Energy, Hami 839000, China
  • 2.Department of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
Published: 2025-05-25 doi: 10.19666/j.rlfd.202412263
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The early faults of sliding bearings are highly concealed. To accurately predict their vibration amplitude, a deep learning model incorporating a YOLOv8-optimized CBAM attention mechanism is proposed. The CBAM module is embedded between the Backbone and Neck to enhance the model’s focus on critical vibration features. Additionally, an improved complete intersection over union loss function is employed to enhance object detection accuracy. Considering the nonlinear and non-stationary characteristics of vibration data, the empirical mode decomposition (EMD) method is integrated into the model to improve the accuracy of vibration state prediction. The experimental results show that, on the 600 MW steam turbine operation dataset, this method improves the detection accuracy by 2.85 percentage points and 8.50 percentage points compared with that of the conventional YOLOv8 and YOLOv7, respectively. Moreover, the root mean square error (RMSE) is reduces, and the mean absolute error (MAE) decreases. Furthermore, in high-noise environments, the model’s error fluctuation reduces by 30% compared with that of the conventional methods, demonstrating stronger generalization ability and stability.

attention mechanism  /  turbine vibration  /  YOLO  /  empirical mode decomposition
Yachao LI, Haoyu LIU, Haokang XU, Yuhan GUAN, Zhantong QI, Yujiong GU. Vibration amplitude prediction method for turbine rotor sliding bearing based on YOLOv8 optimized attention mechanism[J]. Thermal Power Generation, 2025 , 54 (5) : 122 -131 . DOI: 10.19666/j.rlfd.202412263
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doi: 10.19666/j.rlfd.202412263
  • Receive Date：2024-12-09
  • Online Date：2026-03-06
  • Published：2025-05-25
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  • Received：2024-12-09
Affiliations
    1.Huayuan Power Plant, CHN Energy, Hami 839000, China
    2.Department of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202412263
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表12种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
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Genus		 种数
Number of
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Percentage of total
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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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