Predictive airframe maintenance strategies using model-based prognostics

Yiwei Wang (1, 2) , Christian Gogu (1) , Nicolas Binaud (1) , Christian Bes (1) , Raphaël Haftka (3) , Nam-Ho Kim (3)

1 ICA - Institut Clément Ader
2 NJU - Nanjing University
3 UF|MAE - Department of Mechanical and Aerospace Engineering [Gainesville]

Aircraft panel maintenance is typically based on scheduled inspections during which the panel damage size is compared to a repair threshold value, set to ensure a desirable reliability for the entire fleet. This policy is very conservative since it does not consider that damage size evolution can be very different on different panels, due to material variability and other factors. With the progress of sensor technology, data acquisition and storage techniques, and data processing algorithms, structural health monitoring systems are increasingly being considered by the aviation industry. Aiming at reducing the conservativeness of the current maintenance approaches, and, thus, at reducing the maintenance cost, we employ a model-based prognostics method developed in a previous work to predict the future damage growth of each aircraft panel. This allows deciding whether a given panel should be repaired considering the prediction of the future evolution of its damage, rather than its current health state. Two predictive maintenance strategies based on the developed prognostic model are proposed in this work and applied to fatigue damage propagation in fuselage panels. The parameters of the damage growth model are assumed to be unknown and the information on damage evolution is provided by noisy structural health monitoring measurements. We propose a numerical case study where the maintenance process of an entire fleet of aircraft is simulated, considering the variability of damage model parameters among the panel population as well as the uncertainty of pressure differential during the damage propagation process. The proposed predictive maintenance strategies are compared to other maintenance strategies using a cost model. The results show that the proposed predictive maintenance strategies significantly reduce the unnecessary repair interventions, and, thus, they lead to major cost savings.

Mots clés: Structural airframe maintenance; model-based prognostics; predictive maintenance; Extended Kalman filter; first-order perturbation method

Domaines: Autre

Yiwei Wang, Christian Gogu, Nicolas Binaud, Christian Bes, Raphaël Haftka, et al.. Predictive airframe maintenance strategies using model-based prognostics. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2018, 232 (6), pp.690-709. ⟨10.1177/1748006X18757084⟩. ⟨hal-01940475⟩