Reinforced Symbolic Learning with Logical Constraints for Predicting Turbine Blade Fatigue Life
This addresses the challenge of ensuring safety and reliability in aircraft engines by providing interpretable predictions for turbine blade fatigue life, representing an incremental improvement with a novel hybrid method.
The paper tackled the problem of predicting turbine blade fatigue life by introducing Reinforced Symbolic Learning (RSL), a method that derives interpretable formulas linking mechanical properties to fatigue life, achieving superior or comparable predictive accuracy compared to empirical formulas and machine learning algorithms.
Accurate prediction of turbine blade fatigue life is essential for ensuring the safety and reliability of aircraft engines. A significant challenge in this domain is uncovering the intrinsic relationship between mechanical properties and fatigue life. This paper introduces Reinforced Symbolic Learning (RSL), a method that derives predictive formulas linking these properties to fatigue life. RSL incorporates logical constraints during symbolic optimization, ensuring that the generated formulas are both physically meaningful and interpretable. The optimization process is further enhanced using deep reinforcement learning, which efficiently guides the symbolic regression towards more accurate models. The proposed RSL method was evaluated on two turbine blade materials, GH4169 and TC4, to identify optimal fatigue life prediction models. When compared with six empirical formulas and five machine learning algorithms, RSL not only produces more interpretable formulas but also achieves superior or comparable predictive accuracy. Additionally, finite element simulations were conducted to assess mechanical properties at critical points on the blade, which were then used to predict fatigue life under various operating conditions.