WV
W.J.C. Verhagen
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6 records found
1
Condition-based maintenance is an emerging maintenance strategy for aircraft, leveraging the constant collection of sensor information to facilitate diagnostics and prognostics of potential failures. The implementation of this strategy requires a significant initial investment and therefore, the resulting benefits should be quantified in advance. The objective of this research is to investigate the potential benefits of condition- based maintenance (CBM), and more specifically, the use of Prognostic & Health Management (PHM) systems operating with dynamic failure thresholds, with a focus on the required performance levels of the PHM systems. In this paper, a scheduling framework has been developed to schedule preventive maintenance tasks under application of prognostics, using a rolling-horizon scheduling approach to allocate tasks to appropriate maintenance blocks. The resulting maintenance schedule of a fleet of aircraft is subsequently used for the simulation of subsystem failures and the application of prognostics in order to anticipate them. Finally, the possibility of reducing maintenance cost, increasing fleet availability and improving operational reliability is investigated through a cost-benefit analysis. Results show significant improvements in terms of fleet availability and operational reliability, and minor reductions of maintenance cost. Moreover, the achieved benefits are shown to be in relation to the prognostic performance levels, and the scale to which condition-based maintenance is applied.
...
Condition-based maintenance is an emerging maintenance strategy for aircraft, leveraging the constant collection of sensor information to facilitate diagnostics and prognostics of potential failures. The implementation of this strategy requires a significant initial investment and therefore, the resulting benefits should be quantified in advance. The objective of this research is to investigate the potential benefits of condition- based maintenance (CBM), and more specifically, the use of Prognostic & Health Management (PHM) systems operating with dynamic failure thresholds, with a focus on the required performance levels of the PHM systems. In this paper, a scheduling framework has been developed to schedule preventive maintenance tasks under application of prognostics, using a rolling-horizon scheduling approach to allocate tasks to appropriate maintenance blocks. The resulting maintenance schedule of a fleet of aircraft is subsequently used for the simulation of subsystem failures and the application of prognostics in order to anticipate them. Finally, the possibility of reducing maintenance cost, increasing fleet availability and improving operational reliability is investigated through a cost-benefit analysis. Results show significant improvements in terms of fleet availability and operational reliability, and minor reductions of maintenance cost. Moreover, the achieved benefits are shown to be in relation to the prognostic performance levels, and the scale to which condition-based maintenance is applied.
Helicopter pilot performance in degraded visual conditions may be improved through sensor fusion and an augmented reality (AR) display. A simulator experiment with 12 participants was done to test the effect of synthetic terrain grid cell size and helicopter heave dynamics on task performance and control behaviour in a terrain-following hill-climb task. An increase in grid cell size lowered task performance and increased control activity due to reduced optical information. Slower heave dynamics decreased task performance and led to a more prospective control strategy. It was concluded that an effective augmented reality terrain display for altitude control can be designed independently from the vehicle dynamics.
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Helicopter pilot performance in degraded visual conditions may be improved through sensor fusion and an augmented reality (AR) display. A simulator experiment with 12 participants was done to test the effect of synthetic terrain grid cell size and helicopter heave dynamics on task performance and control behaviour in a terrain-following hill-climb task. An increase in grid cell size lowered task performance and increased control activity due to reduced optical information. Slower heave dynamics decreased task performance and led to a more prospective control strategy. It was concluded that an effective augmented reality terrain display for altitude control can be designed independently from the vehicle dynamics.
Aircraft Disruption Management
Increasing Performance with Machine Learning Predictions
Master thesis
(2019)
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Lotfy Hassan, Bruno F. Santos, Wim Verhagen, Dimitrios Zarouchas, Hendriena Ritsema, Jeroen Vink
Airlines experience schedule disruptions on a daily basis. Poor weather conditions, unscheduled aircraft maintenance and congested air spaces are just a few of the causes that prevent airlines from operating their flight schedules as planned. In the third quarter of 2017 over 20% of all scheduled flights in Europe suffered from delays. Operation Research based decision support systems (DSS) help airlines with their disruption management processes and provide suggestions for recovery options.
For large hub-and-spoke carriers, with an extensive network and a large number of aircraft and flights, the computation time required to find the optimal recovery solution after a disruption increases rapidly. As airlines require fast recovery solutions when disruptions occur, there is an ongoing trade-off between computation time and system sophistication. In the majority of disruption cases, no or a limited number of undisrupted aircraft are required to find the optimal recovery solution. By selecting a limited number of aircraft, flights and airports used to find a recovery solution, the computation time is reduced exponentially. The challenge is determining which aircraft and flights should be selected.
This research aims to develop a decision support system for the schedule and aircraft recovery process that is able to present a feasible solution to a disruption in less than 120 seconds. An aircraft recovery model will be developed based on the integer linear programming model that was created by Vink et al. (2019) and Vos et al. (2015). Crew and passenger recovery are not considered. To recover disruptions, the optimization model can delay and cancel flights as well as perform tails swaps, where the flights from two aircraft are switched. The novelty of the work is that machine learning is used to predict which undisrupted aircraft will help recover a disruption. Based on those predictions a sub-network selection algorithm will select the subset of aircraft to be included in the optimization instead of the entire aircraft fleet.
The performance of the system is tested on a case study for the domestic hub-and-spoke network of Delta Airlines. The dataset for the study consists of 2200 daily flights, 147 airports and 827 aircraft in 8 aircraft families. The results of the system are compared with the optimal solution, where no aircraft selections were made. The case study shows that the system is able to make an aircraft selection where 50% of the fleet is discarded, while still finding the optimal solution in 98.9% of the 556 disruptions tested. Furthermore, the system reduces the computation time by 45%, resulting in an average time of 48 seconds. For the disruptions, the computation time varied between 9 and 180 seconds. ...
For large hub-and-spoke carriers, with an extensive network and a large number of aircraft and flights, the computation time required to find the optimal recovery solution after a disruption increases rapidly. As airlines require fast recovery solutions when disruptions occur, there is an ongoing trade-off between computation time and system sophistication. In the majority of disruption cases, no or a limited number of undisrupted aircraft are required to find the optimal recovery solution. By selecting a limited number of aircraft, flights and airports used to find a recovery solution, the computation time is reduced exponentially. The challenge is determining which aircraft and flights should be selected.
This research aims to develop a decision support system for the schedule and aircraft recovery process that is able to present a feasible solution to a disruption in less than 120 seconds. An aircraft recovery model will be developed based on the integer linear programming model that was created by Vink et al. (2019) and Vos et al. (2015). Crew and passenger recovery are not considered. To recover disruptions, the optimization model can delay and cancel flights as well as perform tails swaps, where the flights from two aircraft are switched. The novelty of the work is that machine learning is used to predict which undisrupted aircraft will help recover a disruption. Based on those predictions a sub-network selection algorithm will select the subset of aircraft to be included in the optimization instead of the entire aircraft fleet.
The performance of the system is tested on a case study for the domestic hub-and-spoke network of Delta Airlines. The dataset for the study consists of 2200 daily flights, 147 airports and 827 aircraft in 8 aircraft families. The results of the system are compared with the optimal solution, where no aircraft selections were made. The case study shows that the system is able to make an aircraft selection where 50% of the fleet is discarded, while still finding the optimal solution in 98.9% of the 556 disruptions tested. Furthermore, the system reduces the computation time by 45%, resulting in an average time of 48 seconds. For the disruptions, the computation time varied between 9 and 180 seconds. ...
Airlines experience schedule disruptions on a daily basis. Poor weather conditions, unscheduled aircraft maintenance and congested air spaces are just a few of the causes that prevent airlines from operating their flight schedules as planned. In the third quarter of 2017 over 20% of all scheduled flights in Europe suffered from delays. Operation Research based decision support systems (DSS) help airlines with their disruption management processes and provide suggestions for recovery options.
For large hub-and-spoke carriers, with an extensive network and a large number of aircraft and flights, the computation time required to find the optimal recovery solution after a disruption increases rapidly. As airlines require fast recovery solutions when disruptions occur, there is an ongoing trade-off between computation time and system sophistication. In the majority of disruption cases, no or a limited number of undisrupted aircraft are required to find the optimal recovery solution. By selecting a limited number of aircraft, flights and airports used to find a recovery solution, the computation time is reduced exponentially. The challenge is determining which aircraft and flights should be selected.
This research aims to develop a decision support system for the schedule and aircraft recovery process that is able to present a feasible solution to a disruption in less than 120 seconds. An aircraft recovery model will be developed based on the integer linear programming model that was created by Vink et al. (2019) and Vos et al. (2015). Crew and passenger recovery are not considered. To recover disruptions, the optimization model can delay and cancel flights as well as perform tails swaps, where the flights from two aircraft are switched. The novelty of the work is that machine learning is used to predict which undisrupted aircraft will help recover a disruption. Based on those predictions a sub-network selection algorithm will select the subset of aircraft to be included in the optimization instead of the entire aircraft fleet.
The performance of the system is tested on a case study for the domestic hub-and-spoke network of Delta Airlines. The dataset for the study consists of 2200 daily flights, 147 airports and 827 aircraft in 8 aircraft families. The results of the system are compared with the optimal solution, where no aircraft selections were made. The case study shows that the system is able to make an aircraft selection where 50% of the fleet is discarded, while still finding the optimal solution in 98.9% of the 556 disruptions tested. Furthermore, the system reduces the computation time by 45%, resulting in an average time of 48 seconds. For the disruptions, the computation time varied between 9 and 180 seconds.
For large hub-and-spoke carriers, with an extensive network and a large number of aircraft and flights, the computation time required to find the optimal recovery solution after a disruption increases rapidly. As airlines require fast recovery solutions when disruptions occur, there is an ongoing trade-off between computation time and system sophistication. In the majority of disruption cases, no or a limited number of undisrupted aircraft are required to find the optimal recovery solution. By selecting a limited number of aircraft, flights and airports used to find a recovery solution, the computation time is reduced exponentially. The challenge is determining which aircraft and flights should be selected.
This research aims to develop a decision support system for the schedule and aircraft recovery process that is able to present a feasible solution to a disruption in less than 120 seconds. An aircraft recovery model will be developed based on the integer linear programming model that was created by Vink et al. (2019) and Vos et al. (2015). Crew and passenger recovery are not considered. To recover disruptions, the optimization model can delay and cancel flights as well as perform tails swaps, where the flights from two aircraft are switched. The novelty of the work is that machine learning is used to predict which undisrupted aircraft will help recover a disruption. Based on those predictions a sub-network selection algorithm will select the subset of aircraft to be included in the optimization instead of the entire aircraft fleet.
The performance of the system is tested on a case study for the domestic hub-and-spoke network of Delta Airlines. The dataset for the study consists of 2200 daily flights, 147 airports and 827 aircraft in 8 aircraft families. The results of the system are compared with the optimal solution, where no aircraft selections were made. The case study shows that the system is able to make an aircraft selection where 50% of the fleet is discarded, while still finding the optimal solution in 98.9% of the 556 disruptions tested. Furthermore, the system reduces the computation time by 45%, resulting in an average time of 48 seconds. For the disruptions, the computation time varied between 9 and 180 seconds.
Reducing turnaround time and increasing passenger comfort are requirements that should be tackled for medium range flights. Reducing turnaround time could be achieved by increasing the fuselage aisle width and increasing passenger comfort by increasing the space allocated to carry-on luggage. Because research is now looking at novel aircraft designs, it is necessary to study those geometrical modifications on conventional and novel fuselages. Thus, to what extend can the turnaround time be reduced and passenger comfort be enhanced by conventional and novel fuselages? What would be the opportunities offered by a Prandtl Plane configuration on the reduction of turnaround time and enhancement of passenger comfort? A parametric fuselage model is extended and used to answer this question. Reducing turnaround time comes at a cost in terms of fuselage performances whereas increasing passenger comfort reduces fuselage weight and drag. Combining both also leads to a reduction in fuselage weight and drag.
...
Reducing turnaround time and increasing passenger comfort are requirements that should be tackled for medium range flights. Reducing turnaround time could be achieved by increasing the fuselage aisle width and increasing passenger comfort by increasing the space allocated to carry-on luggage. Because research is now looking at novel aircraft designs, it is necessary to study those geometrical modifications on conventional and novel fuselages. Thus, to what extend can the turnaround time be reduced and passenger comfort be enhanced by conventional and novel fuselages? What would be the opportunities offered by a Prandtl Plane configuration on the reduction of turnaround time and enhancement of passenger comfort? A parametric fuselage model is extended and used to answer this question. Reducing turnaround time comes at a cost in terms of fuselage performances whereas increasing passenger comfort reduces fuselage weight and drag. Combining both also leads to a reduction in fuselage weight and drag.
scoutDROID: Inspection Pocket Drone
Design of a small, quickly deployable unmanned aerial vehicle suitable for indoor inspection: scout Drone for Reconnaisance of Out- and Indoor Danger zones
Bachelor thesis
(2015)
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H.N. Basien, D.S.A. ten Brink, R. Cirera Rocosa, G.I. Földes, S.C. Hungs, E.J. Kroon, V.A. Pereboom, N.D. Potdar, C.H.C. Vlemmix, S.E.C. Voorhoeve, C. de Wagter, J.A. Melkert, V.P. Brügemann, E. Mooij, W.A. Timmer, W.J.C. Verhagen
Major interventions in offshore wind
Predicting the cost of future maintenance
Master thesis
(2015)
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Jarno Veldhoven, Gerard van Bussel, Wim Verhagen, M B Zaayer, M. Spoor, A. Donker
Wind energy is one of the fastest growing types of renewable energy. Today, offshore wind represents 14% of the EU wind energy market, and has a huge potential for further growth [1]. Operations and Maintenance (O&M) accounts for 18-23% of the total lifetime cost in offshore wind farms, compared to 12% onshore [2]. The offshore O&M tasks are more costly, being influenced by distance offshore, harsh offshore conditions, wind farm size, wind turbine reliability and maintenance strategy [2]. Exchanges of main components, also known as major interventions, causes significant costs and has been indicated as the largest uncertaintywhen predicting O&M costs [3]. Major interventions are characterized by the need for a jack-up vessel to perform the exchange. No studies have been found on offshore major interventions. Echavarria has previously performed an analysis on the exchange rates of main components based on the onshore WMEP population, which had an average rated power of 233 kW [4]. Blades and generator were identified as most critical components,
but it is not known how this reflects current exchange rates for larger turbines. The goal of this research is therefore to determine future failure rates and predict the cost of offshore major interventions.
...
Wind energy is one of the fastest growing types of renewable energy. Today, offshore wind represents 14% of the EU wind energy market, and has a huge potential for further growth [1]. Operations and Maintenance (O&M) accounts for 18-23% of the total lifetime cost in offshore wind farms, compared to 12% onshore [2]. The offshore O&M tasks are more costly, being influenced by distance offshore, harsh offshore conditions, wind farm size, wind turbine reliability and maintenance strategy [2]. Exchanges of main components, also known as major interventions, causes significant costs and has been indicated as the largest uncertaintywhen predicting O&M costs [3]. Major interventions are characterized by the need for a jack-up vessel to perform the exchange. No studies have been found on offshore major interventions. Echavarria has previously performed an analysis on the exchange rates of main components based on the onshore WMEP population, which had an average rated power of 233 kW [4]. Blades and generator were identified as most critical components,
but it is not known how this reflects current exchange rates for larger turbines. The goal of this research is therefore to determine future failure rates and predict the cost of offshore major interventions.