Reinforced Symbolic Learning with Logical Constraints for Predicting Turbine Blade Fatigue Life

TL;DR

This paper presents Reinforced Symbolic Learning (RSL), a novel method combining logical constraints and reinforcement learning to derive interpretable and accurate fatigue life prediction formulas for turbine blades.

Contribution

The paper introduces RSL, integrating logical constraints and deep reinforcement learning into symbolic regression for physically meaningful turbine blade fatigue models.

Findings

RSL outperforms existing empirical formulas and machine learning models in accuracy.

RSL generates more interpretable and physically meaningful formulas.

Finite element simulations support the effectiveness of RSL in real-world scenarios.

Abstract

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.