The in-service life of turbine blades directly affects the on-wing lifetime and operating cost of aircraft engines. It would be essential to accurately evaluate the remaining useful life of turbine blades for safe engine operation and reasonable maintenance decision-making. In this paper, a machine learning-based mechanism with multiple information fusion is proposed to predict the remaining useful life of high-pressure turbine blades. The developed method takes account of the in-service operating factors such as the high-pressure rotor speed and exhaust gas temperature, as well as the engine operating environments and performance degradation. The effectiveness of this method is demonstrated on simulated test cases generated by an integrated blade creep-life assessment model, which comprises engine performance, blade stress, thermal, and creep life estimation models. The results show that the proposed method provides a prospective result for in-service life evaluation of turbine blades and is of significance to evaluating the engine on-wing lifetime and making a reasonable maintenance plan.

涡轮叶片的使用寿命直接影响飞机发动机的在翼寿命和运行成本。准确评估涡轮叶片的剩余使用寿命对于发动机安全运行和合理的维护决策至关重要。在本文中，提出了一种基于机器学习的多信息融合机制来预测高压涡轮叶片的剩余使用寿命。所开发的方法考虑了高压转子速度和排气温度等在役运行因素，以及发动机运行环境和性能退化。该方法的有效性在由集成叶片蠕变寿命评估模型生成的模拟测试案例中得到证明，该模型包括发动机性能、叶片应力、热、和蠕变寿命估计模型。结果表明，该方法为涡轮叶片在役寿命评估提供了前瞻性结果，对评估发动机在翼寿命和制定合理的维修计划具有重要意义。