The environment in the cockpit of large transport aircraft is highly complex due to an increasing number of automation systems. This complexity can cause pilots to become less aware of how systems interact. It becomes a severe issue when sensor or data failures occur, as such failures can contribute to a situation in which it is difficult for a pilot to assess what actually is happening and, possibly, how to resolve the problem. This paper presents a method, based on artificial intelligence, for identifying incorrect critical flight control data in real-time. A novel combination of reinforcement learning and a denoising autoencoder is proposed to identify failures and to provide inputs to the aircraft’s flight control and guidance systems, allowing for the correct maneuver to counter the failure and/or to avoid or recover from flight upsets. Tests in stall conditions with a partially blocked pitot tube show that the proposed method results in successful detection and recovery. The performance of the system without an autoencoder is compared to highlight the significant advantages, how this relates to creating systems with AI to improve situational awareness for pilots, and to execute appropriate automatic maneuvers to successfully counter the effect of sensor failures.

由于自动化系统数量的不断增加，大型运输机驾驶舱内的环境变得非常复杂。这种复杂性可能会导致飞行员不太了解系统如何交互。当传感器或数据发生故障时，这将成为一个严重的问题，因为此类故障可能会导致飞行员难以评估实际发生的情况以及如何解决问题。本文提出了一种基于人工智能的实时识别错误关键飞行控制数据的方法。提出了强化学习和降噪自动编码器的新颖组合来识别故障并向飞机的飞行控制和引导系统提供输入，从而允许正确的机动来应对故障和/或避免飞行失常或从飞行失常中恢复。在失速条件下使用部分堵塞的皮托管进行的测试表明，所提出的方法可以成功检测和恢复。对不带自动编码器的系统的性能进行比较，以突出其显着优势，以及这与创建人工智能系统有何关系，以提高飞行员的态势感知能力，并执行适当的自动操作以成功应对传感器故障的影响。