Multi-objective design of aircraft maintenance using Gaussian process learning and adaptive sampling

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.Aerospace Transport & Operation

Predictive Aircraft Maintenance: Modeling and Analysis Using Stochastic Petri Nets

Predictive aircraft maintenance is a complex process, which requires the modeling of the stochastic degradation of aircraft systems, as well as the dynamic interactions between the stakeholders involved. In this paper, we show that the stochastically and dynamically colored Petri nets (SDCPNs) are able to formalize the predictive aircraft maintenance process. We model the aircraft maintenance stakeholders and their interactions using local SDCPNs. The degradation of the aircraft systems is also modeled using local SDCPNs where tokens change their colors according to a stochastic process. These SDCPN models are integrated into a unifying SDCPN model of the entire aircraft maintenance process. We illustrate our approach for the maintenance of multi-component systems with k-out-of-n redundancy. Using SDCPNs and Monte Carlo simulation, we analyze the number of maintenance tasks and potential degradation incidents that the system is expected to undergo when using a remaining useful life(RUL)-based predictive maintenance strategy. We compare the performance of this predictive maintenance strategy against other maintenance strategies that rely on fixed-interval inspection tasks to schedule component replacements. The results show that by conducting RUL-based predictive maintenance, the number of unscheduled maintenance tasks and degradation incidents is significantly reduced.Air Transport & Operation

Probabilistic Flight Delay Predictions Using Machine Learning and Applications to the Flight-to-Gate Assignment Problem

The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay predictions. Therefore, in this study, two probabilistic forecasting algorithms, Mixture Density Networks and Random Forest regression, are applied to predict flight delays at a European airport. The algorithms estimate well the distribution of arrival and departure flight delays with a Mean Absolute Error of less than 15 min. To illustrate the utility of the estimated delay distributions, we integrate these probabilistic predictions into a probabilistic flight-to-gate assignment problem. The objective of this problem is to increase the robustness of flight-to-gate assignments. Considering probabilistic delay predictions, our proposed flight-to-gate assignment model reduces the number of conflicted aircraft by up to 74% when compared to a deterministic flight-to-gate assignment model. In general, the results illustrate the utility of considering probabilistic forecasting for robust airport operations’ optimization.Aerospace Transport & Operation

Mathematical Models for Air Traffic Conflict and Collision Probability Estimation

Increasing traffic demands and technological developments provide novel design opportunities for future air traffic management (ATM). In order to evaluate current air traffic operations and future designs, over the past decades, several mathematical models have been proposed for air traffic conflict and collision probability estimation. However, few comparative evaluations of these models with respect to their mathematical core exist. Such comparative evaluations are particularly difficult since different authors employ different model definitions, notations, and assumptions, even when using the same modeling techniques. The aim of this paper is: 1) to present the mathematical core of the existing approaches for air traffic conflict and collision probability estimation using the same body of notations and definitions; 2) to outline the advances in estimating the probability of air traffic conflict and collision using a unified mathematical framework; 3) to various air traffic applications and their use of directed mathematical models for air traffic conflict and collision probability estimation; and 4) to provide insight into the capabilities and restrictions of the mathematical models in the evaluation of future ATM designs.Aerospace Transport & Operation

Analyzing tactical control strategies for aircraft arrivals at an airport using a queuing model

This paper proposes to analyze control strategies for arrival air traffic at an airport using a classical queuing model. The parameters of our model are estimated by means of a data-driven analysis of two years of radar tracks and flight plans for arrival flights at Tokyo International Airport from 2016 to 2017. Our results show that increasing the capacity with one or two more aircraft in the airspace up to 60 NM around the airport significantly mitigates arrival delays, even when assuming future, increased arrival traffic volumes. The outcomes of this study provide insights into the effectiveness of arrival control strategies and are seen as a means to recommend scenarios to be further analyzed with human-in-the-loop simulations.Aerospace Transport & Operation

Queue-Based Modeling of the Aircraft Arrival Process at a Single Airport

This paper proposes data-driven queuing models and solutions to reduce arrival time delays originating from aircraft arrival processing bottlenecks at Tokyo International Airport. A data-driven analysis was conducted using two years of radar tracks and flight plans from 2016 and 2017. This analysis helps not only to understand the bottlenecks and operational strategies of air traffic controllers, but also to develop mathematical models to predict arrival delays resulting from increased, future aircraft traffic. The queue-based modeling approach suggests that one potential solution is to expand the realization of time-based operations, efficiently shifting from traffic flow control to time-based arrival management. Furthermore, the proposed approach estimates the most effective range of transition points, which is a key requirement for designing extended arrival management systems while offering automation support to air traffic controllers.Aerospace Transport & Operation

Fleet scheduling for electric towing of aircraft under limited airport energy capacity

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.Control & SimulationAir Transport & Operation

Evaluating the impact of new aircraft separation minima on available airspace capacity and arrival time delay

Although the application of new, reduced aircraft separation minima can directly increase runway throughput, the impact thereof on the traffic flow of aircraft arriving at the destination airport has not been discussed yet. This paper proposes a data-driven and queue-based modeling approach and presents an analysis of the impact on the delay time of arriving aircraft in the airspace within a radius of 100 nautical miles around an airport. The parameters of our queuing model were estimated by analysing the data contained in the radar tracks and flight plans for flights that arrived at Tokyo International Airport during the 2 years of 2016 and 2017. The results clarified the best arrival strategy according to the distance from the arrival airport: The combination of airspace capacity control and reduction of the flight time and separation variance is the most powerful solution to mitigate delays experienced by arriving traffic while also allowing an increase in the amount of arrival traffic. The application of new wake vortex categories would enable us to increase the arrival traffic to 120%. In addition, the arrival delay time could be minimised by implementing the proposed arrival traffic strategies together with automation support for air traffic controllers.Aerospace Transport & Operation

A mathematical framework for improved weight initialization of neural networks using Lagrange multipliers

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.Air Transport & Operation

Multi-objective Analysis of Condition-based Aircraft Maintenance Strategies using Discrete Event Simulation

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.Aerospace Transport & Operation