Aircraft Maintenance Scheduling Using Engine Sensor Data
An aircraft maintenance scheduling optimization model using data-driven turbofan engine RUL prognostics
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Abstract
This research is relevant to any airline that wants to innovate its maintenance scheduling process bymaking use of data-driven techniques. Shifting towards a data-driven organization holds the potential benefits of increased scheduling efficiency and profits, while decreasing the time required to schedule maintenance on a daily basis. Optimizing the airline maintenance process will lead to lower aircraft downtime and less wasted life of aircraft components. This will result in a higher passenger satisfactory, a more sustainable way of operation and a higher profit for the airline. All of these aspects stress the importance of optimizing the airline maintenance scheduling process, both from a economic and societal point of view.
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Final_thesis_Arthur_Reijns.pdf
(.pdf | 10.9 Mb)
- Embargo expired in 03-07-2023