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Trajectory Based Flight Phase Identification with Machine Learning for Digital Twins

Arts, Emy and Kamtsiuris, Alexander and Meyer, Hendrik and Raddatz, Florian and Peters, Annika and Wermter, Stefan (2022) Trajectory Based Flight Phase Identification with Machine Learning for Digital Twins. Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V.. DLRK2021, 31. Aug. - 02. Sep. 2021, Bremen. doi: 10.25967/550191.

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Analysis of aircraft trajectory data is used in different applications of aviation research. Areas such as Maintenance, Repair and Overhaul (MRO) and Air Traffic Management (ATM) benefit from a more detailed understanding of the trajectory, thus requiring the trajectory to be divided into the different flight phases. Flight phases are mostly computed from the aircraft’s internal sensor parameters, which are very sensitive and have scarce availability to the public. This is why identification on publicly available data such as Automatic Dependent Surveillance Broadcast (ADS-B) trajectory data is essential. Some of the flight phases required for these applications are not covered by state-of-the-art flight phase identification on ADS-B trajectory data. This paper presents a novel machine learning approach for more detailed flight phase identification. We generate a training dataset with supervised simulation data obtained with the X-plane simulator. The model combines K-means clustering with a Long Short-Term Memory (LSTM) network, the former allows the segmentation to capture transitions between phases more closely, and the latter learns the dynamics of a flight. We are able to identify a larger variety of phases compared to state of the art and adhere to the International Civil Aviation Organisation (ICAO) standard.

Item URL in elib:https://elib.dlr.de/187792/
Document Type:Conference or Workshop Item (Speech)
Title:Trajectory Based Flight Phase Identification with Machine Learning for Digital Twins
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Arts, EmyEmy.Arts (at) dlr.deUNSPECIFIED
Kamtsiuris, AlexanderAlexander.Kamtsiuris (at) dlr.deUNSPECIFIED
Meyer, HendrikHendrik.Meyer (at) dlr.deUNSPECIFIED
Raddatz, FlorianFlorian.Raddatz (at) dlr.dehttps://orcid.org/0000-0002-0660-7650
Peters, Annikaapeters (at) informatik.uni-hamburg.deUNSPECIFIED
Wermter, Stefanwermter (at) informatik.uni-hamburg.deUNSPECIFIED
Date:3 August 2022
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
DOI :10.25967/550191
Publisher:Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V.
Keywords:Machine Learning, Long Short-Term Memory, Digital Twin, Maintenance Repair and Overhaul,
Event Title:DLRK2021
Event Location:Bremen
Event Type:national Conference
Event Dates:31. Aug. - 02. Sep. 2021
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Efficient Vehicle
DLR - Research area:Aeronautics
DLR - Program:L EV - Efficient Vehicle
DLR - Research theme (Project):L - Digital Technologies
Location: Hamburg
Institutes and Institutions:Institute of Maintenance, Repair and Overhaul > Process Optimisation and Digitalisation
Deposited By: Arts, Emy
Deposited On:15 Aug 2022 07:40
Last Modified:15 Aug 2022 07:40

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