Print Email Facebook Twitter Multi-objective design of aircraft maintenance using Gaussian process learning and adaptive sampling Title Multi-objective design of aircraft maintenance using Gaussian process learning and adaptive sampling Author Lee, J. (TU Delft Air Transport & Operations) Mitici, M.A. (TU Delft Air Transport & Operations) Date 2022 Abstract Aircraft maintenance design aims to identify strategies that render the aircraft reliable for flight in a cost-efficient manner. These are often conflicting objectives. Moreover, existing studies on maintenance design often limit themselves to only one type of maintenance strategy, overlooking other potentially dominating designs. We propose a framework for aircraft maintenance design with explicit reliability and cost-efficiency objectives. We explore the design space of a variety of maintenance strategies ranging from traditional time-based maintenance to predictive maintenance. To explore this design space, we propose an adaptive algorithm using Gaussian process learning and a novel adaptive sampling method. Gaussian process learning models rapidly pre-evaluate new maintenance designs, while adaptive sampling selects for further exploration only those designs that are expected to improve the available Pareto front of maintenance designs. This framework is illustrated for the maintenance of multi-component aircraft systems with k-out-of-n redundancy. The results show that novel predictive maintenance designs based on Remaining-Useful-Life prognostics dominate other maintenance designs, especially in the knee region of the obtained Pareto front, where the most beneficial balance between conflicting objectives is achieved. Our proposed exploration algorithm also outperforms other state-of-the-art exploration algorithms with respect to the quality of the Pareto front obtained. Subject Aircraft maintenanceDesign space explorationEfficiencyGaussian process learningMulti-objective designPredictive maintenanceReliability To reference this document use: http://resolver.tudelft.nl/uuid:78c7aef8-8e0b-4049-8aa6-64d5b61be3a4 DOI https://doi.org/10.1016/j.ress.2021.108123 ISSN 0951-8320 Source Reliability Engineering & System Safety, 218 Part of collection Institutional Repository Document type journal article Rights © 2022 J. Lee, M.A. Mitici Files PDF 1_s2.0_S0951832021006189_main.pdf 2.6 MB Close viewer /islandora/object/uuid:78c7aef8-8e0b-4049-8aa6-64d5b61be3a4/datastream/OBJ/view