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Prognostics and Health Management (PHM) are emerging approaches to productlife cycle that will maintain system safety and improve reliability, whilereducing operating and maintenance costs. This is particularly relevant foraerospace systems, where high levels of integrity and high performances arerequired at the same time. We propose a novel strategy for the nearly real-timeFault Detection and Identification (FDI) of a dynamical assembly, and for theestimation of Remaining Useful Life (RUL) of the system. The availability of atimely estimate of the health status of the system will allow for an informedadaptive planning of maintenance and a dynamical reconfiguration of the missionprofile, reducing operating costs and improving reliability. This workaddresses the three phases of the prognostic flow - namely (1) signalacquisition, (2) Fault Detection and Identification, and (3) Remaining UsefulLife estimation - and introduces a computationally efficient procedure suitablefor real-time, on-board execution. To achieve this goal, we propose to combineinformation from physical models of different fidelity with machine learningtechniques to obtain efficient representations (surrogate models) suitable fornearly real-time applications. Additionally, we propose an importance samplingstrategy and a novel approach to model damage propagation for dynamicalsystems. The methodology is assessed for the FDI and RUL estimation of anaircraft electromechanical actuator (EMA) for secondary flight controls. Theresults show that the proposed method allows for a high precision in theevaluation of the system RUL, while outperforming common model-based techniquesin terms of computational time.

Computational framework for real-time diagnostics and prognostics of aircraft actuation systems