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Remaining useful life prediction for fatigue crack growth based on VSG-ELM model
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Weidong ZHENG1, Wei XIONG1, Xiaoyan LI1, Peiqiang BAI1, Siyu LIN1, Xionghua CUI2, Yanjun LYU3, Rui SHI3
Thermal Power Generation | 2025, 54(2) : 145 - 153
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Thermal Power Generation | 2025, 54(2): 145-153
• Power generation technology forum •
Remaining useful life prediction for fatigue crack growth based on VSG-ELM model
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Weidong ZHENG1, Wei XIONG1, Xiaoyan LI1, Peiqiang BAI1, Siyu LIN1, Xionghua CUI2, Yanjun LYU3, Rui SHI3
Affiliations
  • 1.Yuhuan Power Plant, Huadian Energy Co., Ltd., Taizhou 317604, China
  • 2.Xi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, China
  • 3.School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
Published: 2025-02-25 doi: 10.19666/j.rlfd.202406148
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Overdue service of thermal power units has become a trend, but fatigue crack of turbine rotor steel seriously affects the operation safety of steam turbine units. Due to the lack of the fatigue crack growth (FCG) test data of rotor steel, and large computation cost for stochastic model modeling and solution, the estimation of fatigue crack remaining useful life (RUL) is currently insufficient. On the basis of fatigue crack growth tests and analysis on its random models, a modified Gaussian membership information expanded (GMIE) sample domain method is proposed to generate virtual samples based on mega trend diffusion (MTD). Meanwhile, an extreme machine learning (ELM) neural network combined with the expective regression (ER) model is used to predict the RUL of fatigue crack propagation. The RUL of fatigue crack propagation under a specific cycle is calculated. By comparing the results with the RUL probability density function (PDF) curve and fatigue crack propagation curve of the existing numerical analysis methods, it shows that mean absolute percentage error (δMAPE) is 2.78%, which verifies the effectiveness of the proposed method and provides robust support for safe operation of the turbine rotor systems.

mega-trend-diffusion  /  fatigue crack propogation  /  VSG-ELM  /  remaining useful life prediction
Weidong ZHENG, Wei XIONG, Xiaoyan LI, Peiqiang BAI, Siyu LIN, Xionghua CUI, Yanjun LYU, Rui SHI. Remaining useful life prediction for fatigue crack growth based on VSG-ELM model[J]. Thermal Power Generation, 2025 , 54 (2) : 145 -153 . DOI: 10.19666/j.rlfd.202406148
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Article Info
doi: 10.19666/j.rlfd.202406148
  • Receive Date：2024-06-02
  • Online Date：2026-03-06
  • Published：2025-02-25
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  • Received：2024-06-02
Affiliations
    1.Yuhuan Power Plant, Huadian Energy Co., Ltd., Taizhou 317604, China
    2.Xi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, China
    3.School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202406148
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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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