用逻辑约束强化符号学习，精准预测涡轮叶片疲劳寿命。
Reinforced Symbolic Learning with Logical Constraints for Predicting Turbine Blade Fatigue Life
- 结合逻辑约束与强化学习，自动推导物理可解释的寿命公式
- 在GH4169和TC4材料上，精度优于或相当六种经验公式与五种模型
- 适合需要可解释性与高可靠性的航空发动机寿命预测场景
准确预测涡轮叶片疲劳寿命对保障航空发动机安全可靠至关重要。该领域核心挑战在于揭示机械性能与疲劳寿命之间的内在关系。本文提出增强型符号学习（RSL）方法，通过符号优化生成关联机械性能与疲劳寿命的预测公式。RSL在优化过程中引入逻辑约束，确保生成公式具备物理意义与可解释性；同时采用深度强化学习高效引导符号回归，提升模型准确性。所提RSL方法在两种涡轮叶片材料（GH4169与TC4）上验证，相比六种经验公式与五种机器学习算法，不仅产生更易解释的公式，且预测精度达到最优或相当水平。此外，通过有限元仿真获取叶片关键点的机械性能数据，并用于多种工况下的疲劳寿命预测。
原文摘要 · Abstract (English)
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.
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