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Development of predictive structural maintenance strategies for aircraft using model-based prognostics

Abstract : Aircraft maintenance represents a major economic cost for the aviation industry. Traditionally, the aircraft maintenance is highly regulated based on fixed schedules (thus called scheduled maintenance) in order to ensure safety. The frequency of scheduled maintenance is designed to be very conservative to maintain a desirable level of reliability. Developing efficient maintenance can be an important way for airlines to allow a new profit growth. With the development of sensor technology, structural health monitoring (SHM) system, which employ a sensor network sealing inside aircraft structures to monitor the damage state, are gradually being introduced in the aviation industry. Once it is possible to monitor the structure damage state automatically and continuously by SHM systems, it enables to plan the maintenance activities according to the actual or predicted health state of the aircraft rather than a fixed schedule. This work focus on the fatigue crack propagation in the fuselage panels. The SHM system is assumed to be employed. A model-based prognostics method is developed, which enables to filter the noise of SHM data to estimate the crack size, and to predict the future health state of the panels. This predictive information is integrated into the maintenance decision-making and two types of predictive maintenance are developed. The numerical study shows that the predictive maintenance significantly reduces the maintenance cost by reducing the number of maintenance stop and the repaired panels.
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Submitted on : Thursday, February 22, 2018 - 6:07:05 PM
Last modification on : Wednesday, June 24, 2020 - 4:18:54 PM
Long-term archiving on: : Wednesday, May 23, 2018 - 2:39:31 PM


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  • HAL Id : tel-01715558, version 1


Yiwei Wang. Development of predictive structural maintenance strategies for aircraft using model-based prognostics. Mechanical engineering [physics.class-ph]. INSA de Toulouse, 2017. English. ⟨NNT : 2017ISAT0005⟩. ⟨tel-01715558⟩



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