Method for predicting the high-cycle fatigue remaining useful life of aero-engine blades based on physics-informed neural networks

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.