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.

As aircraft maintenance is transitioning towards data- driven condition-based maintenance (CBM), its cost and performance objectives need to be re-evaluated: how are these objectives related under various CBM strategies?; which objectives are conflicting?; what are the trade-offs between the conflicting objectives?; what is the impact of this transition on aircraft maintenance? We propose a methodology based on discrete-event simulation to analyze CBM of aircraft from the perspective of multiple objectives. The simulation considers an aircraft operations model, systems of multiple, redundant aircraft components, stochastic degradation models for components, and specific CBM strategies. In particular, we analyze two CBM strategies for component replacement, which are based on sensor monitoring and remaining-useful-life prognostics. As objectives for these strategies, we consider the minimization of the number of component replacements, the number of unscheduled replacements, the number of degradation incidents, the delay caused by maintenance, and the mean number of flight cycles to replacements (MCTR). We identify the main conflicting objectives and generate Pareto fronts. We show non-trivial trade-offs between the performance-oriented objectives (the number of degradation incidents and the delay due to maintenance) and cost-oriented objectives (MCTR). In fact, the CBM strategy based on remaining-useful-life prognostics dominates the other strategies in the knee region of the Pareto fronts. This implies that the transition towards data-driven CBM strategies can reduce the cost while maintaining the performance. Moreover, the proposed methodology is readily applicable to analyze general aircraft systems and other maintenance strategies.

Aircraft maintenance is key for safe and efficient aircraft operations. While most studies propose cost-efficient maintenance strategies, the safety and efficiency of these strategies need to be quantified. This paper proposes a formal framework to assess the safety and efficiency of maintenance strategies by means of agent-based modelling, stochastically and dynamically coloured Petri nets, and Monte Carlo simulation. We model an end-to-end aircraft maintenance process, considering several maintenance stakeholders. We apply our framework for aircraft landing gear brakes, and use a Gamma process to model the degradation trends of the brakes. The numerical results show that applying data-driven strategies reduces the number of inspections by 36%, while maintaining the same level of safety as in the case of traditional time-based maintenance strategies. Furthermore, in order to discuss the possibility to substitute all inspections by sensor monitoring, an advanced data-driven strategy using prognostics is considered. Overall, our proposed framework is generic and can readily be applied to assess the safety and efficiency of the maintenance of other aircraft components and maintenance strategies.