Taxiing aircraft using electric vehicles is seen as an effective solution to meet aviation targets of climate neutrality. However, making the transition to electric taxiing operations is expected to significantly increase the electricity demand at airports. In this paper we propose a mixed-integer linear program to schedule electric vehicles for aircraft towing and battery charging, while considering a limit for the supply of energy. The objective of the schedule is to maximize emissions savings. For computational tractability, we develop an Adaptive Large Neighbourhood Search which makes use of multiple local search heuristics to identify scheduling solutions. For daily scheduling with a small fleet size, the developed heuristic achieves solutions with an average 4% gap to the best linear programming solution. The results show that charging the vehicles during daytime is essential to maximize saved emissions: removing charging opportunities for a few hours during the day reduces the performance by an average of 6.4%. In addition, it is found that fast charging leads to low vehicle downtime, unless the battery size exceeds 750kWh, when charging rates over 150kW become unnecessary. Overall, our model provides support for infrastructure planning of airports during the transition to aircraft electric taxiing.

@en

The health management of batteries is a key enabler for the adoption of Electric Vertical Take-off and Landing vehicles (eVTOLs). Currently, few studies consider the health management of eVTOL batteries. One distinct characteristic of batteries for eVTOLs is that the discharge rates are significantly larger during take-off and landing, compared with the battery discharge rates needed for automotives. Such discharge protocols are expected to impact the long-run health of batteries. This paper proposes a data-driven machine learning framework to estimate the state-of-health and remaining-useful-lifetime of eVTOL batteries under varying flight conditions and taking into account the entire flight profile of the eVTOLs. Three main features are considered for the assessment of the health of the batteries: charge, discharge and temperature. The importance of these features is also quantified. Considering battery charging before flight, a selection of missions for state-of-health and remaining-useful-lifetime prediction is performed. The results show that indeed, discharge-related features have the highest importance when predicting battery state-of-health and remaining-useful-lifetime. Using several machine learning algorithms, it is shown that the battery state-of-health and remaining-useful-life are well estimated using Random Forest regression and Extreme Gradient Boosting, respectively.@en

Following the Paris Accords, the aviation industry aims to become climate neutral by 2050. In this line, electric vehicles that tow aircraft during taxiing are a promising emerging technology to reduce emissions at airports. This paper proposes an end-to-end optimization framework for electric towing vehicles (ETVs) dispatchment at large airports. We integrate the routing of the ETVs in the taxiway system where minimum separation distances are ensured at all times, with the assignment of these ETVs to aircraft towing tasks and scheduling ETV battery recharging. For ETV recharging, we consider a preemptive charging policy where the charging times depend on the residual state-of-charge of the battery. We illustrate our model for one day of operations at a large European airport. The results show that the 913 arriving and departing flights can be towed with 38 ETVs, with battery charging distributed throughout the day. The fleet size is shown to increase approximately linear with the number of flights in the schedule. We also propose a greedy dispatchment of the ETVs, which is shown to achieve an optimality gap of 6% with respect to the number of required vehicles and with 22% with respect to the maximum delay during towing. We also show that both algorithms can be leveraged to account for flight delays using a rolling horizon approach, and that over 95% of the flights can be reallocated if delays occur. Overall, we propose a roadmap for ETV management at large airports, considering realistic ETV specifications (battery capabilities, kinematic properties) and requirements for aircraft collision avoidance during towing.

@en

The increasing use of on-board sensor monitoring and data-driven algorithms has stimulated the recent shift to data-driven predictive maintenance for aircraft. This paper discusses emerging challenges for data-driven predictive aircraft maintenance. We identify new hazards associated with the introduction of data-driven technologies into aircraft maintenance using a structured brainstorming conducted with a panel of maintenance experts. This brainstorming is facilitated by a prior modeling of the aircraft maintenance process as an agent-based model. As a result, we identify 20 hazards associated with data-driven predictive aircraft maintenance. We validate these hazards in the context of maintenance-related aircraft incidents that occurred between 2008 and 2013. Based on our findings, the main challenges identified for data-driven predictive maintenance are: (i) improving the reliability of the condition monitoring systems and diagnostics/prognostics algorithms, (ii) ensuring timely and accurate communication between the agents, and (iii) building the stakeholders’ trust in the new data-driven technologies.
@en

Reducing aircraft taxiing emissions will deliver a significant contribution to the worldwide goal of net-zero greenhouse gas emissions in the aviation industry. Replacing jet-engine taxiing by towing aircraft with electric towing vehicles is expected to reduce taxiing emissions by roughly 80%. Introducing a fleet of towing vehicles introduces operational challenges to an airport. Although there has been research focused on optimizing the assignment of vehicles to aircraft, such an assignment will require changes during a day of operations, when disruptions such as flight delays occur. This paper proposes two models, a strategic and a disrupted model, with which an adaptive vehicle-to-aircraft assignment is created. The models are formulated as Mixed Integer Linear Problems, and both maximize the number of towed aircraft and minimize the schedule changes for vehicle operators. The approach illustrated includes vehicle and aircraft routing, conflict avoidance, and a model for energy usage. We apply the models to Amsterdam Airport Schiphol, where the disrupted model is able to create assignments that remain the same in subsequent time steps for an average of 55% of the vehicles, on a busy day, when towing all aircraft. Furthermore, the results show that minimizing schedule changes does not come at the expense of fewer towed aircraft, i.e. of smaller emission savings. Lastly, we investigate the impact of fleet size and general on-time performance on the assignments created by the model.@en

Recent advances in battery technology have opened the possibility for short-haul electric flight. This is particularly attractive for commuter airlines that operate in remote regions such as archipelagos or Nordic fjords where the geography impedes other means of transportation. In this paper we address the question of how to optimize the charging infrastructure (charging power, spare batteries) for an airline when considering a battery swapping system. Our analysis considers the expenditures needed for (i) the significant charging power requirements, (ii) spare aircraft batteries, (iii) the used electricity, and (iv) delay costs, should the infrastructure not be sufficient to accommodate the flight schedule. The main result of this paper is the formulation of this problem as a two-phase recourse model. This is required to account for the variation of the flight schedule throughout a year of operations. With this, both the strategic (infrastructure sizing) and tactical (battery recharge scheduling) planning are addressed The model is applied for Widerøe Airlines, with a network of 7 hub airports and 36 regional airports in Norway. The results show that a total investment of 4412 kW in electricity power supply and 25 spare batteries is needed for the considered network, resulting in a daily investment of €11700. We also quantify the benefits of considering an entire year of operations for our analysis, instead of just one congested day (7% cost reduction) or one average day of operations (31% reduction) at the most congested airport.

@en

The increasing availability of condition-monitoring data for components/systems has incentivized the development of data-driven Remaining Useful Life (RUL) prognostics in the past years. However, most studies focus on point RUL prognostics, with limited insights into the uncertainty associated with these estimates. This limits the applicability of such RUL prognostics to maintenance planning, which is per definition a stochastic problem. In this paper, we therefore develop probabilistic RUL prognostics using Convolutional Neural Networks. These prognostics are further integrated into maintenance planning, both for single and multiple components. We illustrate our approach for aircraft turbofan engines. The results show that the optimal replacement time for the engines is close to the lower bound of the 99% confidence interval of the RUL estimates. We also show that our proposed maintenance approach leads to a cost reduction of 53% compared to a traditional Time-based maintenance strategy. Moreover, compared with the ideal case when the true RUL is known in advance (perfect RUL prognostics), our approach leads to a limited number of failures. Overall, this paper proposes an end-to-end framework for data-driven predictive maintenance for multiple components, and showcases the potential benefits of data-driven predictive maintenance on cost and reliability.

@en

Taxiing aircraft using electric towing vehicles (ETVs) is expected to significantly contribute to the objective of climate-neutral aviation by 2050. This study reviews existing work on operational aspects of electric towing of aircraft, and discusses management solutions. We first discuss the varying electric taxi systems currently under development, and their implementation progress at airports. We outline the current specifications of ETVs and the procedures needed to perform electric taxiing movements. We next discuss the management needs for implementing ETVs at an airport, by reviewing existing mathematical models for ETV fleet management: dedicated vehicle routing models, ETV to flight assignment models, fleet sizing models and battery charging optimisation models. Last, we identify remaining research challenges. For instance, a main challenge is to increase the robustness of ETV routing and towing scheduling against disruptions due to flight delay. This paper summarizes the main research directions needed to support large-scale ETV implementation in the next few decades.@en

Most Remaining Useful Life (RUL) prognostics are obtained using supervised learning models trained with many labelled data samples (i.e., the true RUL is known). In aviation, however, aircraft systems are often preventively replaced before failure. There are thus very few labelled data samples available. We therefore propose a Long Short-Term Memory (LSTM) autoencoder with attention to develop health indicators for an aircraft system instead. This autoencoder is trained with unlabelled data samples (i.e., the true RUL is unknown). Since aircraft fly under various operating conditions (varying altitude, speed, etc.), these conditions are also integrated in the autoencoder. We show that the consideration of the operating conditions leads to robust health indicators and improves significantly the monotonicity, trendability and prognosability of these indicators. These health indicators are further used to predict the RUL of the aircraft system using a similarity-based matching approach. We illustrate our approach for turbofan engines. We show that the consideration of the operating conditions improves the monotonicity of the health indicators by 97%. Also, our approach leads to accurate RUL estimates with a Root Mean Square Error (RMSE) of 2.67 flights only. Moreover, a 19% reduction in the RMSE is obtained using our approach in comparison to existing supervised learning models.

@en

A good weight initialization is crucial to accelerate the convergence of the weights in a neural network. However, training a neural network is still time-consuming, despite recent advances in weight initialization approaches. In this paper, we propose a mathematical framework for the weight initialization in the last layer of a neural network. We first derive analytically a tight constraint on the weights that accelerates the convergence of the weights during the back-propagation algorithm. We then use linear regression and Lagrange multipliers to analytically derive the optimal initial weights and initial bias of the last layer, that minimize the initial training loss given the derived tight constraint. We also show that the restrictive assumption of traditional weight initialization algorithms that the expected value of the weights is zero is redundant for our approach. We first apply our proposed weight initialization approach to a Convolutional Neural Network that predicts the Remaining Useful Life of aircraft engines. The initial training and validation loss are relatively small, the weights do not get stuck in a local optimum, and the convergence of the weights is accelerated. We compare our approach with several benchmark strategies. Compared to the best performing state-of-the-art initialization strategy (Kaiming initialization), our approach needs 34% less epochs to reach the same validation loss. We also apply our approach to ResNets for the CIFAR-100 dataset, combined with transfer learning. Here, the initial accuracy is already at least 53%. This gives a faster weight convergence and a higher test accuracy than the benchmark strategies.

@en

Reducing the length of departure queues at runway entry points is one of the most important requirements for reducing aircraft traffic congestion and fuel consumption at airports. This study designs an aircraft departure model at a runway using a time-varying fluid queue. The proposed model enables us to determine the aircraft waiting time in the departure queue and to evaluate effective control approaches for assigning suitable holds at gates rather than runway entry points. As a case study, this study modeled the departure queue at runway 05 of Tokyo International Airport for an entire day of operations. Using actual traffic data of departures at the airport, the model estimates that aircraft spend a total of 2.5 h departure waiting time in a day at runway 05. Considering the stochastic nature of actual departure traffic, the relevance of the proposed model is discussed using validation criteria. The model estimation shows a reasonable, expected order of magnitude compared with the departure queue recorded in the actual traffic data. Furthermore, ecological and economic benefits are quantitatively evaluated assuming a reduction in the departure queue length. Our results show that about one kiloton of fuel oil per year is wasted due to aircraft waiting to depart from a single departure runway.

@en

The increasing availability of condition monitoring data for aircraft components has incentivized the development of Remaining Useful Life (RUL) prognostics in the past years. However, only few studies consider the integration of such prognostics into maintenance planning. In this paper we propose a dynamic, predictive maintenance scheduling framework for a fleet of aircraft taking into account imperfect RUL prognostics. These prognostics are periodically updated. Based on the evolution of the prognostics over time, alarms are triggered. The scheduling of maintenance tasks is initiated only after these alarms are triggered. Alarms ensure that maintenance tasks are not rescheduled multiple times. A maintenance task is scheduled using a safety factor, to account for potential errors in the RUL prognostics and thus avoid component failures. We illustrate our approach for a fleet of 20 aircraft, each equipped with 2 turbofan engines. A Convolution Neural Network is proposed to obtain RUL prognostics. An integer linear program is used to schedule aircraft for maintenance. With our alarm-based maintenance framework, the costs with engine failures account for only 7.4% of the total maintenance costs. In general, we provide a roadmap to integrate imperfect RUL prognostics into the maintenance planning of a fleet of vehicles.

@en

The increasing availability of sensor monitoring data has stimulated the development of Remaining-Useful-Life (RUL) prognostics and maintenance planning models. However, existing studies focus either on RUL prognostics only, or propose maintenance planning based on simple assumptions about degradation trends. We propose a framework to integrate data-driven probabilistic RUL prognostics into predictive maintenance planning. We estimate the distribution of RUL using Convolutional Neural Networks with Monte Carlo dropout. These prognostics are updated over time, as more measurements become available. We further pose the maintenance planning problem as a Deep Reinforcement Learning (DRL) problem where maintenance actions are triggered based on the estimates of the RUL distribution. We illustrate our framework for the maintenance of aircraft turbofan engines. Using our DRL approach, the total maintenance cost is reduced by 29.3% compared to the case when engines are replaced at the mean-estimated-RUL. In addition, 95.6% of unscheduled maintenance is prevented, and the wasted life of the engines is limited to only 12.81 cycles. Overall, we propose a roadmap for predictive maintenance from sensor measurements to data-driven probabilistic RUL prognostics, to maintenance planning.@en

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.@en

Several studies have proposed Remaining-Useful-Life (RUL) prognostics for aircraft components in the last years. However, few studies focus on integrating these RUL prognostics into maintenance planning frameworks. This paper proposes an optimization model for opportunistic maintenance scheduling of aircraft components that integrates RUL prognostics and that groups the maintenance of these components to reduce costs. We illustrate our approach for the maintenance of a fleet of aircraft, each equipped with multiple landing gear brakes. RUL prognostics for the landing gear brakes are obtained using a Bayesian regression model. Based on these RUL prognostics, we group the replacement of brakes using an integer linear program. As a result, we obtain a cost-optimal RUL-driven opportunistic-maintenance schedule for the brakes of a fleet of aircraft. Compared with traditional maintenance strategies, our approach leads to a reduction of up to 20% of the total maintenance costs.@en

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.

@en

With the current advances in aircraft design and Lithium-Ion batteries, electric aircraft are expected to serve as a replacement for conventional, short-range aircraft. This paper addresses the main operational challenges for short-range flights operated with electric aircraft: determining the investment needs for a fleet of electric aircraft, and the logistics of charging stations and swap batteries required to support these flights. A mixed-integer linear program with two phases is proposed. In the first phase, a schedule for flight and battery recharge is developed for a fleet of electric aircraft. In the second phase, optimal times for battery charging are determined, together with an optimal sizing of the number of charging stations and swap batteries. We illustrate our model for short-range flights to and from an European airport and for an electric aircraft designed based on the operational characteristics of a conventional, narrow-body aircraft.@en

Well-established metrics such as the Root Mean Square Error or the Mean Absolute Error are not suitable to evaluate estimated distributions of the Remaining Useful Life (i.e., probabilistic prognostics). We therefore propose novel metrics to evaluate the quality of probabilistic Remaining Useful Life prognostics. We estimate the distribution of the Remaining Useful Life of turbofan engines using a Convolutional Neural Network with Monte Carlo dropout. The accuracy and sharpness of the obtained probabilistic prognostics are evaluated using the Continuous Ranked Probability Score (CRPS) and weighted CRPS. The reliability of the obtained probabilistic prognostics is evaluated using the α-Coverage and the Reliability Score. The results show that the estimated distributions of the Remaining Useful Life of turbofan engines are accurate, reliable and sharp when using a Convolutional Neural Network with Monte Carlo dropout. In general, the proposed metrics are suitable to evaluate the accuracy, sharpness and reliability of probabilistic Remaining Useful Life prognostics.@en

The aviation industry aims for net-zero emissions by 2050. In this line, achieving climate-neutral ground operations is one of the first objectives. Electric vehicles that tow aircraft during taxiing are a promising technology to achieve climate-neutral ground operations. In this paper, we consider the dispatching of electric towing vehicles at an airport. We study the impact of the maximum battery capacity of these vehicles and the battery recharging protocols, on the total number of electric towing vehicles required at an airport. We propose a mixed-integer linear program to determine the size of the fleet of electric towing vehicles under various battery capacities and various battery recharging protocols. We illustrate our model for one day of operations at Amsterdam Airport Schiphol. The results show that 41, 29, and 24 ETVs are required to tow all aircraft when batteries capacities of 100 kWh, 320 kWh, and 500 kWh are considered, respectively. Compared with the best performing approach existing in literature, our model reduces the required size of the fleet of electric towing vehicles by 27% when considering a nominal battery size of 320 kWh.@en

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.

@en