Designing reliable, data-driven maintenance for aircraft systems with applications to the aircraft landing gear brakes

Conference paper (2022)

Authors

J. Lee Air Transport & Operations - Aerospace Engineering
M.A. Mitici Air Transport & Operations - Aerospace Engineering
S. Geng Safety and Security Science - TPM
Ming Yang Safety and Security Science - TPM
M. Yang Safety and Security Science - TPM
Research Group
Air Transport & Operations (Aerospace Engineering) (TU Delft)
Copyright: © 2022 J. Lee, M.A. Mitici, S. Geng, M. Yang
DOI: https://doi.org/10.3850/978-981-18-5183-4_R01-04-288-cd
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Published Date

2022

Language

English

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Faculty

Aerospace Engineering

Department

Control & Operations

Research Group

Air Transport & Operations

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.

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