1 research outputs found
Computational framework for real-time diagnostics and prognostics of aircraft actuation systems
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.Comment: 57 page