Designing reliable, data-driven maintenance for aircraft systems with applications to the aircraft landing gear brakes
J. Lee (TU Delft - Air Transport & Operations)
M.A. Mitici (TU Delft - Air Transport & Operations)
S. Geng (TU Delft - Safety and Security Science)
M. Yang (TU Delft - Safety and Security Science)
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Abstract
When designing the maintenance of multi-component aircraft systems, we consider parameters such as safety margins (used when component replacements are scheduled), and reliability thresholds (used to define data-driven Remaining-Useful-Life prognostics of components). We propose Gaussian process learning and novel adaptive sampling techniques to efficiently optimize these design parameters. We illustrate our approach for aircraft landing gear bakes. Data-driven, Remaining-Useful-Life prognostics for brakes are obtained using a Bayesian linear regression. Pareto optimal safety margins for scheduling brake replacements are identified, together with Pareto optimal reliability thresholds for prognostics.