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Method for predicting the high-cycle fatigue remaining useful life of aero-engine blades based on physics-informed neural networks
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Yu ZHANG1, Pei LIU1, 2, Qingcheng LIU1, Kexin HAN1, Weimin WANG1, 2, Jinji GAO1, 2
Journal of Vibration Engineering | 2025, 38(6) : 1190 - 1198
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Journal of Vibration Engineering | 2025, 38(6): 1190-1198
Method for predicting the high-cycle fatigue remaining useful life of aero-engine blades based on physics-informed neural networks
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Yu ZHANG1, Pei LIU1, 2, Qingcheng LIU1, Kexin HAN1, Weimin WANG1, 2, Jinji GAO1, 2
Affiliations
  • 1.State Key Laboratory of High-End Compressor and System Technology,Beijing University of Chemical Technology,Beijing 100029,China
  • 2.Beijing Key Laboratory of Health Monitoring and Self-Recovery for High-End Mechanical Equipment,Beijing University of Chemical Technology,Beijing 100029,China
Published: 2025-06-10 doi: 10.16385/j.cnki.issn.1004-4523.2025.06.007
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As a core component of an aero-engine, the structural integrity of a blade directly determines the engine’s performance and flight safety. Under extreme working conditions such as high temperature, high pressure, and high-speed rotation, blades are prone to generating micro-cracks under the action of complex stress fields. Once cracks propagate and cause blade fracture, they will trigger chain damage, posing significant safety hazards. Based on the damage tolerance concept, the critical duration during which a blade can still operate safely after crack initiation is defined as the remaining useful life (RUL).To address this, this study proposes a mechanism-data dual-driven RUL prediction method integrating the Paris crack propagation law and physics-informed neural networks (PINN). By constructing a loss function that incorporates physical constraints, this method regularizes and constrains the gradients of the neural network. It enables inverse identification of crack propagation parameters while effectively improving the model’s prediction accuracy under limited monitoring data. For aero-engine blades and CT (compact tension) specimens, compared with traditional physical models and data-driven methods, the proposed method dynamically updates characteristic parameters to adapt to system changes, significantly reducing prediction errors under limited sample conditions. Additionally, the PINN model developed in this study features lightweight architecture and fast inference capabilities, meeting the requirements of online monitoring and predictive maintenance. This method provides a new technical pathway for health management and intelligent operation and maintenance of aero-engines.

remaining useful life (RUL)  /  fatigue life prediction  /  fatigue crack propagation  /  PINN  /  aero-engine blade
Yu ZHANG, Pei LIU, Qingcheng LIU, Kexin HAN, Weimin WANG, Jinji GAO. Method for predicting the high-cycle fatigue remaining useful life of aero-engine blades based on physics-informed neural networks[J]. Journal of Vibration Engineering, 2025 , 38 (6) : 1190 -1198 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.06.007
Year 2025 volume 38 Issue 6
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.2025.06.007
  • Receive Date:2025-05-06
  • Online Date:2026-02-12
  • Published:2025-06-10
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History
  • Received:2025-05-06
  • Revised:2025-06-04
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Affiliations
    1.State Key Laboratory of High-End Compressor and System Technology,Beijing University of Chemical Technology,Beijing 100029,China
    2.Beijing Key Laboratory of Health Monitoring and Self-Recovery for High-End Mechanical Equipment,Beijing University of Chemical Technology,Beijing 100029,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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