Real-time measurement parameters are crucial for diagnosing faults in aero-engine gas path performance, ensuring engine reliability, and mitigating potential economic losses. Traditional aero-engines performance diagnosis was mainly based on the measurements of steady-state condition and lacked the utilization of data under transient conditions. Gas path diagnosis of aero-engines under transient conditions is crucial for early fault detection and safety of flight within the envelope. The challenge lies in the inconsistent distribution of performance deviations caused by variable operating conditions, especially with complex fault types, which can undermine diagnostic credibility. To improve reliability of gas path diagnosis under transient conditions, an offline reinforcement learning fault diagnosis framework based on a transient aero-engine performance model is proposed. To address the issue of variable operating conditions during transient states, a domain adaptive approach is utilized to reconstruct the measurement baseline and facilitate the transfer of different performance deviation distributions. Additionally, by adding spool acceleration as a measurement parameter, the multi-component fault coupling is solved. Finally, validation with actual operating data simulates fault cases, demonstrating the proposed method's efficacy in quantitatively detecting gradual, sudden, and multiple component faults under transient conditions with high accuracy and efficiency. The method proposed in this study achieves a computational speed improvement by 64% compared to the conventional method, achieving a time of 0.13 seconds, with an average error of less than 0.00389%. Additionally, it demonstrates strong robustness in the presence of noise, with an average error of less than 0.03125%. This proposed method improves real-time fault detection under transient conditions for its higher accuracy and efficiency, and therefore significantly enhance gas path health monitoring and diagnosis capability.

中文翻译：

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基于域自适应离线强化学习的航空发动机瞬态气路故障诊断


实时测量参数对于诊断航空发动机气路性能故障、确保发动机可靠性和减少潜在的经济损失至关重要。传统的航空发动机性能诊断主要基于稳态条件的测量，缺乏瞬态条件下的数据利用。在瞬态条件下对航空发动机进行气路诊断对于早期故障检测和包络内飞行安全至关重要。挑战在于可变操作条件导致的性能偏差分布不一致，尤其是对于复杂的故障类型，这可能会破坏诊断的可信度。为了提高瞬态条件下气路诊断的可靠性，该文提出一种基于瞬态航空发动机性能模型的离线强化学习故障诊断框架。为了解决瞬态期间工作条件可变的问题，采用域自适应方法来重建测量基线并促进不同性能偏差分布的传递。此外，通过将阀芯加速度添加为测量参数，解决了多分量故障耦合问题。最后，使用实际运行数据进行验证，模拟故障情况，证明了所提方法在瞬态条件下以高精度和高效定量检测渐进、突发和多组件故障的有效性。本文提出的方法相比传统方法，计算速度提高了 64%，时间达到 0.13 秒，平均误差小于 0.00389%。 此外，它在存在噪声的情况下表现出很强的鲁棒性，平均误差小于 0.03125%。该方法提高了瞬态条件下的实时故障检测，具有更高的精度和效率，从而显著提高了气路健康监测和诊断能力。