飞机作动系统实时的诊断和故障诊断的计算框架
Computational framework for real-time diagnostics and prognostics of aircraft actuation systems
摘要：
预测与健康管理（PHM）是一种新兴的产品生存周期管理方法，它将维护系统的安全性，提高可靠性，同时降低运行和维护成本。这对于航空航天系统尤其重要，因为航空航天系统同时需要高水平的完整性和高性能。提出了一种新的动态装配体故障实时的【形】检测与识别（FDI）以及系统剩余使用寿命（RUL）估计策略。如果能够及时估计系统的健康状况，就可以在知情的情况下对维护进行适应性规划，并对任务剖面进行动态重新配置，从而降低运营成本并提高可靠性。这项工作解决了预测流程的三个阶段-即（1）信号采集，（2）故障检测和识别，以及（3）剩余使用寿命估计-并介绍了一个计算效率高的程序，适用于实时的【形】，板载执行。为了实现这一目标，我们建议将来自不同保真度的物理模型的信息与机器学习技术相结合，以获得适用于几乎实时的【形】应用的有效表示（代理模型）。此外，我们提出了一种重要性采样策略和一种新的动力系统损伤传播建模方法。该方法进行评估的FDI和RUL估计的飞机机电作动器（EMA）的二次飞行控制。结果表明，所提出的方法允许在系统的RUL的评估精度高，同时优于常见的基于模型的技术在计算时间。
英文摘要：
Prognostics and Health Management (PHM) are emerging approaches to product life cycle that will maintain system safety and improve reliability, while reducing operating and maintenance costs. This is particularly relevant for aerospace systems, where high levels of integrity and high performances are required at the same time. We propose a novel strategy for the nearly real-time Fault Detection and Identification (FDI) of a dynamical assembly, and for the estimation of Remaining Useful Life (RUL) of the system. The availability of a timely estimate of the health status of the system will allow for an informed adaptive planning of maintenance and a dynamical reconfiguration of the mission profile, reducing operating costs and improving reliability. This work addresses the three phases of the prognostic flow - namely (1) signal acquisition, (2) Fault Detection and Identification, and (3) Remaining Useful Life estimation - and introduces a computationally efficient procedure suitable for real-time, on-board execution. To achieve this goal, we propose to combine information from physical models of different fidelity with machine learning techniques to obtain efficient representations (surrogate models) suitable for nearly real-time applications. Additionally, we propose an importance sampling strategy and a novel approach to model damage propagation for dynamical systems. The methodology is assessed for the FDI and RUL estimation of an aircraft electromechanical actuator (EMA) for secondary flight controls. The results show that the proposed method allows for a high precision in the evaluation of the system RUL, while outperforming common model-based techniques in terms of computational time.
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