This study provides new insights in the problem of lumpy aircraft spare parts demands by incorporating new drivers that have an impact on the failure patterns of aircraft components. The study introduces a model and presents corresponding results that obtains component failure characteristics based on data from an aircraft manufacturer. A Monte Carlo simulation technique is used to take dierent repair qualities, fleet sizes, environmental conditions and shared component pool strategies into account. The outcome is evaluated to capture the impact of these parameters. Based on the occurring patterns of the failures, the demand patterns can be inferred. The study confirms the conclusion from previous research that the fleet size is the main contributor to the unpredictability of the demands of spare parts, but notes that this conclusion is not always usable in practice, as practical limitations regarding the extension of fleets are in play. The study concludes that an improvement of the repair quality is beneficial for the variance of the demand and the total amount of failures over time.

As the profit margins of the airline industry are relatively low, it is of utmost importance to keep costs low in order for airlines to stay competitive. An important cost factor is maintenance costs, as it can take up around 10-20 % of the total direct operational costs. Currently, much development is taking place in developing condition-based maintenance (CBM) strategies. These strategies on the one hand leverage remaining useful life (RUL) predictions of components to enable better planning and lower repair costs while on the other hand less preventive maintenance tasks are required due to the increase of useful sensor data available. This paper develops insights in the potential benefits that CBM can have as a function of different prognostic performance levels. This is done by developing cost- benefit models which accept a wide range of parameters being able to simulate prognostic effectiveness on different aircraft fleets. Results are obtained by using real MRO and operator input data. The results show that CBM can be beneficial, given that the model has a sufficient specificity and the component supply chain scales accordingly.

Estimating the RUL (Remaining Useful Life) of machinery is a useful tool for maintenance and performance operations. This results in lower costs, improved safety and operational improvements.
This paper proposes two adaptations to the CNN-LSTM network provided by Li et al. \cite{Li2019APrediction}, as well as exploring reproducibility, accuracy and sensitivity of the original DAG (Directed Acyclic Graph) network. The network at hand is an ensemble network combining LSTM and CNN neural networks to provide an accurate regression RUL prediction using the NASA CMAPSS dataset \cite{NasaNasaReprository}.
The Adaptable Time Window (ATW) adaptation increases the amount of time cycles that can be predicted and increases the accuracy, allowing for earlier predictions and better RUL predictions. Allowing state-of-the-art predictions accuracy for complex datasets. The Sub-network training adaptions did not surpass the accuracy of the original network with the current implementation settings, however is promising for further research.

Some aircraft damages require immediate attention. Typically, there are several alternative actions, resulting in varying outcomes. Application of MCDM was investigated to quickly choose the best scenario, as this is not always immediately apparent. In doing so, this research contributes to the state-of-the-art by a) considering alternative evaluation for multiple simultaneous operational occurrences and b) performing a comparative analysis of several MCDM methods in terms of applicability, suitability and robustness. WSM, TOPSIS and VIKOR have been identified as potentially appropriate. Using the criteria survivability, time and cost, different scenarios have been evaluated. It resulted that for single occurrences the outcome only varies for very extreme weight settings. In a multiple damage occurrences scenario, i.e. several aircraft competing for limited resources, differences have been found. While TOPSIS and VIKOR generate similar recommendations, WSM yields different results. Furthermore, it was found that VIKOR is not suited for the used heuristic approach, as it results in an ambiguous performance score of several solutions relative to each other. Its sensitivity makes its use in an operational environment questionable, as small changes easily lead to a different recommendation. Looking at the frequent use of a DSS in an operational environment as opposed to a strategic one, WSM is the best choice. This is due to its ease of implementation, its robustness and its simplicity.

Unplanned maintenance is a costly factor in aircraft operations. Predictive Maintenance aims at reducing the surprise effect of unplanned maintenance and thereby its associated cost. A variety of statistical models are used to estimate the remaining life, as well as sensors to gauge component condition. The application to statistical models of sensory information coming from the pilot, in the form of pilot complaints, appears to be an overlooked option worth investigating. In other words: What is the effect of pilot complaints on the predictability of component removals? This question is answered by determining relevant words in the pilot complaints using a TF-IDF analysis and use the presence of these words as covariate in the well known Proportional Hazards Model. Left truncation and right censoring is applied to limit the time-invariant nature of these covariates. The results in the form of hazard ratios indicate a hazard increase of several orders of magnitude with respect to baseline hazard. These results are put into perspective when compared when compared to the known outcome of the pilot complaints, making their added predictability seem marginal. Another adverse indication is the violation of the proportionality assumption. The magnitude of the hazard ratios do suggest that additional
measures in the from of a more in depth natural language processing and the application of time-varying covariates could bring the concept closer to practical application.

In recent years, airlines have increasingly developed the ability to monitor the condition of aircraft components by means of sensors. In turn, aircraft maintenance aims to use this sensor data to predict component failures. However, the challenge remains to make use of these prognostics to generate appropriate maintenance schedules. In this paper, we develop a Monte-Carlo tree search to schedule maintenance tasks based on component prognostics and available maintenance slots. This approach is used to create a maintenance policy for multiple aircraft which specifies which aircraft are allocated for maintenance and on which days. The results show that the scheduling of the maintenance tasks is robust and able to accommodate the maintenance scheduling of smaller airline fleet sizes. Overall, our results support the integration of aircraft component prognostics in aircraft maintenance scheduling.

Global and local aviation traffic is growing while economic and performance pressures on the industry are increasing. As a consequence, airlines try to maximise their fleet utilization. Airline operators and Maintenance, Repair and Overhaul (MRO) providers therefore require as much insight as possible in factors affecting component reliability and availability. Reliability analysis in literature rarely considers the existence of a relation between explanatory variables and component reliability, and includes strict assumptions on independence of events and underlying distributions. This disregards the complex nature of aircraft operations, where the probability of an event may be influenced by various operational and maintenance factors. This research develops new insights from operational and maintenance data about the impact of operating environment and ageing of components and fleet on reliability of the components by incorporating these factors in an extension of the Cox regression model. The research was performed in cooperation with KLM Engineering & Maintenance, a Dutch MRO company. Examination of results obtained from analysing historical data of a set of three components with respect to installations and removals indicate that the natural environment at the hub airport, maintenance history of components, the age of the aircraft on which the component is installed and different modification designs are useful significant predictors of the time-on-wing duration of the component.

Air forces demand aircraft availability for their operations. In order to increase this availability, there is a need for insight in the effect of environmental conditions on the usage profiles of components and systems. Existing frameworks and standards only provide for static environmental effects on component degradation. The research presented in this thesis aims to identify primary environmental drivers of deterioration for selected systems and components by modeling, implementing and subsequently verifying and validating these drivers on maintenance requirements.

In this research, a multi-level prognostic framework is proposed. The framework is tested and applied to a case-study within the Royal Netherlands Air Force at the NH90 program. Using Principle Component Analysis, coefficients for the first two components show that Relative Humidity, Sea Level Pressure and Temperature-related features are environmental drivers to the corrosion degradation characteristics of the NH90. These results are verified on the maintenance requirements of the RNLAF, which show an increase in system availability and less maintenance costs such as spare parts demand and required personnel.

Aircraft base maintenance consists of hundreds of tasks including preventive, corrective and modification tasks which have to be scheduled to a few check moments per year. The allocation of tasks to checks represents a complex scheduling problem due to the constraints involved. A mixed integer linear programming model was developed to optimize the allocation of the complete base maintenance workload and maintenance checks over a four year period. The model optimizes for resource capacity and interval efficiency. CPLEX has been used as solver. An iterative solution technique loops around the model which yields an approximate optimal solution. The model was developed for a large MRO service provider. Its outcome provides the MRO with an integral insight into the complete workload distribution over a tactical planning horizon for all checks. This insight can help to optimize the allocation of maintenance tasks and resource capacity which will contribute to a higher fleet availability.

Scheduled aircraft maintenance consists of a set of work tasks to be executed. This thesis focuses on improving the order in which these work tasks are executed. Because finding an optimum for these kind of problems is computationally intensive, the chosen approach is of heuristic nature, which is a practical method resulting in close-to-optimal solutions. In short, the model builds a schedule based on a set of rules and constraints, in which the work tasks with the highest priority are planned as early as possible in the maintenance check. Here, priority is the heuristic and based on minimizing the total length of the maintenance check. Because of the rule-based approach, the output of the model will only slightly change with similar sets of work tasks, leading to standardization of the maintenance. Lastly, as the model is semi-automatized, the model can both proactively as well as reactively generate schedules.

In aircraft maintenance, performance of routine maintenance tasks is prone to generate additional non-routine tasks. These non- routine tasks relate to solving an unexpected issue and have specific requirements in terms of materiel, tools and manpower. In existing research regarding optimization of aircraft maintenance environments and schedules, these requirements are assumed to be in place or not considered at all. In real-life, the required resources may not be in place and retrieving them is subject to human interactions and decision-making, potentially resulting in delays in the maintenance schedule. This research proposes a novel methodology to address this problem by developing a multi-agent automated negotiation model capable of simulating negotiations related to the procurement of required aircraft materials in order to perform non-routine tasks. The proposed model explores its applicability by incorporating several negotiation strategies, adaptive parameters and negotiation circumstances, e.g. urgency. The main research question is: In the context of non-routine aircraft maintenance materiel availability, to what extent can multi- agent automated negotiation be applied to explore effective agent strategies for various negotiation circumstances regarding the procurement of aircraft components and expendables? The proposed methodology has shown to be very suitable for modelling negotiation circumstances as evaluation of the model’s performance has resulted in understanding and recommendations to policy makers concerning what strategy to apply in which circumstance. Variations of circumstances have lead to expected findings, e.g. increasing urgency results in a sooner delivery of materials, whereas in other situations the model’s KPIs are unaffected by parameter variations, unexpectedly.

There is a growth in the use of composites for the new generation of wide-body aircraft such as the Boeing 787 and Airbus A350. This shift from using aluminium as the primary material is motivated by the benefits of using composites in design, manufacturing and operations. Composites offer the aircraft manufacturer the ability to create more complex shapes and optimise the design such that it is light-weight. This, in tandem with other design improvements, leads to lower fuel burn. Consequently, airlines see the advantage of these new aircraft to reduce their operational cost. Therefore, as airlines continue to renew their ageing fleets of aluminium aircraft, there is going to be an increased need for composite maintenance. However, fulfilling the increased demand for composite repairs is impeded by limited availability of historical damage data, due to the young operational age of these aircraft. Composites are particularly sensitive to impact damage, and understanding the likelihood and the consequence of this type of damage is valuable for maintenance processes such as repair decision-making. The purpose of this dissertation is to predict the risk of impact damage for future composite aircraft and use it to substantiate maintenance decision-making in an operational setting...@en

Delivering air power is the main goal of an Air Force. Military aircraft used to deliver this air power are required to be maintained in accordance with a so-called Aircraft Maintenance Program (AMP). Utilization of military aircraft is assumed to be constant when constructing an AMP. In reality, usage can drastically change over both short- and long-term periods as a result of political choices, as well as sudden deployments and changing mission types. These changes in usage will have its effect on system condition and by extension on required maintenance. In order to guarantee optimal efficiency and effectiveness of the AMP, these changes in usage should be taken into account while reviewing the AMP. Health and Usage Monitoring Systems (HUMS) can be used to support in analysing usage on a detailed level. A tool which is able to incorporate both historical and (expected) future usage will be of great help to a maintenance engineer responsible for reviewing the AMP on a yearly basis.
In this research, a tool has been developed able to support in maintenance decisions on interval adjustments and incorporating system usage. Various methods and algorithms are incorporated of which some are novel contributions in their own right. The developed tool uses HUMS data, flight planning data, and design data to provide a possible adjustment on maintenance intervals. The tool takes both historical usage and expected future usage into account. The model is able to translate usage profiles (as defined by the operator) into usage on a very detailed level. This translation creates possibilities to determine usage effects on system condition. Translation to this detailed level of usage is achieved by processing HUMS data through rule-based flight regime recognition algorithms. Finally, the model includes a method to carry out a simplified business case to show possible gains resulting from the proposed interval adjustment.
The model is tested by applying case study data originated from the Apache helicopter of the Royal Netherlands Air Force (RNLAF). The used data to train and test the model covers three years of operation: 2014-2016. Concerning accrued fatigue damage, case study results show an average severity factor of 0.37. This factor is based on the accrued fatigue damage resulting from usage conform the design spectra as defined by the Original Equipment Manufacturer (OEM). The maintenance intervals, currently used by the RNLAF and defined in the AMP, are based on these design spectra. Validation of the model is carried out by making use of a dedicated validation dataset (covering the year 2017). Both historical and expected (extreme severe) future usage are incorporated to set up business cases for three Fatigue Life Limited (FLL) Critical Safety Items (CSIs). These business cases revealed potential gains in component and maintenance cost when adapting the proposed interval adjustment. For these three components, the model proposed interval escalations of 42%, 44%, and 94% where both historical and expected future usage is taken into account.

Aircraft maintenance is inherently unpredictable due to differences in failure rates between components. This results in high variance inflow in repair shops, which in case of insufficient capacity yields long turnaround times and thus the need for additional components in stock. The aim of this research project is to determine the effect of service level optimisation in the presence of highly variable demand from an aircraft maintenance supply chain perspective. The first approach focuses on determining the optimal capacity in the repair shop using a greedy algorithm, while the second aims at controlling demand by assisting in in- or outsourcing decision-making by means of a multi-criteria decision-making model. A case study at KLM Engineering & Maintenance is performed to test the applicability of the models in the aircraft maintenance industry.

The newest generation of aircraft has seen a strong increase in sensor data generated on-board. The available data has the potential to indicate the health state of individual components based on which their maintenance requirements can be determined, a maintenance strategy called Condition Based Maintenance. Predictive Maintenance is a specific condition based maintenance strategy that aims to determine these requirements in advance by predicting failures from the sensor data. It has the potential to reduce costly unanticipated maintenance or unnecessarily conservative maintenance. In the short term it could add significant value for components that are currently subject to a reactive maintenance policy. In the long term it could potentially disrupt the traditional maintenance practice of periodic inspection. One of the main challenges in applying Predictive Maintenance in the aviation industry is translating the large amounts of sensor data into a reliable failure prediction, a process called prognostics.

In this study, state-of-the-art machine learning, and specifically deep learning models, have been investigated for their potential for prognostics. A case study has been performed at KLM Royal Dutch Airlines on the Boeing 747 Bleed Air Valves, traditionally some of the most challenging components from a maintenance perspective. It has been shown that fully self-learning algorithms can be used for prognostics, enabling the implementation of one of the first real-life predictive maintenance implementations.

KLM Engineering and Maintenance provides component availability for numerous airline customers around the world. To ensure on-time performance, inventory is an indelible part of operations. Inventory models allow supply chain engineers to determine the required size of the component pool.Over the course of the project, a multi-echelon inventory model is developed tailored to the operations of KLM. Many different inventory models have been developed in current literature. The colloquium will discuss the challenges in applying and adapting such a model to a supply chain of aircraft rotables, while simultaneously discussing the effect on the investment of the supply chain to ensure on-time performance.

Multiple concepts for hybrid vehicles capable of both road and flight transport are becoming a reality. However, through incorporation of both flight and road hardware into one vehicle these designs become inefficient. The aim of this project is to design an alternative strategy for realising optimised hybrid vehicles. The objective of this project is to "Develop a personal transport vehicle suitable for commuter use on which flight hardware can be attached within 5 minutes, by 10 students within 10 weeks". This was derived from the mission statement which was to "Develop a faster and cheaper personal transport vehicle for the European market with detachable flight/road hardware that combines the advantages of road transport with flight capability". From this objective, the three most key requirements are that the vehicle shall have a road and flight configuration, that the flight hardware shall be separable from the road hardware and that the flight hardware shall be attached/detached from the road hardware within 5 minutes. When analysing the performance of the vehicle, typical flight stages and stalling configurations were investigated in terms of speed and power setting. At cruise speed, the 12 DEP (Distributed Electric Propulsion) propellers in the midsection of the wing are turned off and folded . However, for take-off and landing configuration, all the propellers are switched on. For fuel use two options are present. E10 gasoline for maximum range, or E85 for decent range, but a 70% lower eco-impact. 122 kW of power is supplied to the 6 outboard motors during cruise. The take-off distance is 450 m and the landing distance is 462 m. Two different propellers were designed and optimized for DEP and cruise conditions. To verify the noise requirements, a propeller noise analysis of the vehicle was carried out, since that is the largest contributer to overall vehicle noise. From that analysis it was concluded that the propeller noise level of the vehicle is 52.3 dBA at 1000 ft, which is low compared to other general aviation aircraft. The airfoil of the wing was selected using the design lift coefficient of 0.56. The airfoils chosen for the wing and tail are, respectively, theNACA4418 andNACA0012. Thewingwas designed to have an aspect ratio of 17, a surface area of 8.414 m2 and a span of 11.96 m. Using these values, the induced drag and pitching moment coefficients were determined. To investigate the overall efficiency of the aerodynamics of the wing, the lift over drag ratio (L/D) was calculated for each configuration. For cruise, landing and take-off, the L/D is 12.93, 7.74 and 9.48, respectively. The packaging of the vehicle resulted in the centre of gravity of the operative empty weight of the full configuration to act at 37.4% of the fuselage length at an empty mass of 1174.7 kg. The longitudinal and lateral stability and control of the vehicle were assessed. Due to the large downwash caused by the DEP propellers of the wing, a T-tail configuration was designed to move the tail away from the downwash and make it more effective. A fully-movable horizontal tail was necessary to counter the large lift coefficient and, consequently, the moment created by distributed electrical propulsion. The horizontal and vertical stabiliser were designed with a surface area of 2.42m2 and 1.1m2 respectively. After analysing the strengths and manufacturing methods of several materials it was found that the wingbox would be made using aluminium (AL7075-T6) with taper in the sheet thickness. Due to the slender wing, the weight of the wing became relatively high at 200 kg. For the skin, the most suitable material was polyester with glass fibres for its specific strength, price and compatibility with a foam or balsa core. For the linkage system, the location where the linkage is established needed to be considered. After investigating this, it was determined that the best place for the linkage would be the rear of the road hardware and the front of the flight hardware. For the linkage the Scharfenberg train coupling was used together with safety pins. The Scharfenberg connection was scaled down to suit the vehicle as it was originally designed for trains. After the final design is concluded, it is necessary to plan for future research in order to make this concept a reality. There are still uncertainties in how DEP affects the performance and aerodynamics of the concept. Therefore, it is recommended to conduct more lowspeed tests to properly establish those effects. Computational FluidDynamics (CFD) and wing tunnel testing is also recommended to visualise and analyse the fluid flow of the wing. A structural analysis using the finite elementmethod and full scale tests of themajor components are also recommended. As the coupling mechanism has never been used in aerospace application, further research into the adaptability and the integration of this mechanism should be performed. As a single-engine Private Pilot License is preferred, recommendations were also made on tests that need to be performed in order to make a case for the airworthiness authorities.

The main objective of the Royal Netherlands Air Force (RNLAF) organization is to produce airpower, by delivering available aircraft and air crew (also known as: Operational Readiness). It is found that flight cancellations occur due to various reasons, from which operational- and maintenance influences are the majority.

This thesis focuses on the improvement of aircraft maintenance operations (AMO), in order to increase the amount of available aircraft in a fleet. The improvement answers the question;

‘Which components should be maintained when, and using which method (how)?

The improvement is done by correlating (statistical) Reliability Analysis with Health and Usage Monitoring System data, in order to determine statistical failure behavior and failure predicting using component condition.

Furthermore using the correlation of Maintenance Engineering data and Component Condition data, a relation is laid which influence maintenance actions have on component health. Using these relations, the improvement of aircraft maintenance can be continued.

Within the first (out of three) parts, statistical reliability data is compared to theoretical failure behavior, based on the combination of individual lifetime conditional regressions, in order to gain population characteristics, like: Mean Time Between Failures. The second part focusses on determination of Remaining Useful Life of individual serial numbers, based on linear regression of last known condition data. The third part investigates the relation of maintenance action versus component condition

Finally, a Cox Proportional Hazard Model is applied, using both statistical reliability analysis and condition data. Followed by the improvement of aircraft maintenance operations.

Previous researches found to be positive when models were introduced in an aviation organization. This research comes with in Integrated Aviation Organization Maintenance and Planning Concept, which describes the internal relationships of the different optimization and improvement models and conceptualizes them. This conceptualization is done for enhancement of the implementation within aviation organizations.