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Airframe maintenance is traditionally performed at scheduled maintenance stops. The decision to repair a fuselage panel is based on a fixed crack size threshold, which allows to ensure the aircraft safety until the next scheduled maintenance stop. With progress in sensor technology and data processing techniques, structural health monitoring (SHM) systems are increasingly being considered in the aviation industry. SHM systems track the aircraft health state continuously, leading to the possibility of planning maintenance based on an actual state of aircraft rather than on a fixed schedule. This paper builds upon a model-based prognostics framework that the authors developed in their previous work, which couples the Extended Kalman filter (EKF) with a first-order perturbation (FOP) method. By using the information given by this prognostics method, a novel cost driven predictive maintenance (CDPM) policy is proposed, which ensures the aircraft safety while minimizing the maintenance cost. The proposed policy is formally derived based on the trade-off between probabilities of occurrence of scheduled and unscheduled maintenance. A numerical case study simulating the maintenance process of an entire fleet of aircrafts is implemented. Under the condition of assuring the same safety level, the CDPM is compared in terms of cost with two other maintenance policies: scheduled maintenance and threshold based SHM maintenance. The comparison results show CDPM could lead to significant cost savings.
Wang et al. (Tue,) studied this question.