Modeling Average Maintenance Behavior of Fleet ofAirplanes using Fleet-MCS

摘要

In aircrafts, fuselage inspections are performed regularly to remove large damages that threaten the safety of the structure. Recently, structural health monitoring techniques have been developed that uses sensors and actuators to detect damages on structures paving way for progressive inspection. The average maintenance hangar trips per airplane and the average number of panels replaced on it have a direct bearing on the cost of progressive inspection. The lifecycle of an airplane was modeled as blocks of damage propagation interspersed with inspection. The Paris model with random parameters is used to model damage growth, and detection probability during inspections is modeled by Palmberg expression. Conventionally. Monte Carlo Simulations delineate the process. In this paper, a fleet-MCS procedure is presented that predict the average behavior of a fleet of airplanes using simple analytical expressions. Fleet-MCS procedures reduce the high computational cost of Monte Carlo simulations in predicting the average fleet behavior while maintaining similar level of accuracy. Monte Carlo simulations involve random sampling and would require multiple simulations to predict the fleet average. Fleet-MCS procedure predicts the fleet average with a single run of the simulation reducing the computational burden. The fleet average from the regular MCS and the fleetMCS has been compared in this paper and has been found to be in accordance with reasonable accuracy.