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Method for determining operating loads on rail vehicles using artificial intelligence tools

Laporte, Mathilde und Winkler-Höhn, Robert und Buhr, Alexander und Bell, James und Köppel, Martin (2025) Method for determining operating loads on rail vehicles using artificial intelligence tools. NAFEMS, 2025-05-19 - 2025-05-22, Salzburg, Austria.

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Kurzfassung

The method described below is part of the research project KI-MeZIS ('œAI Methods in Condition Monitoring and Demand-Adapted Maintenance of Rail Vehicle Structures' (funding code 19|21024D)). The aim of this project is to develop and apply the potential of artificial intelligence (AI) methods for monitoring rail traffic. With the help of sensors placed on the front, on the supporting structure and on the bogie of the train, AI methods should be able to evaluate and interpret the data. Nowadays, aerodynamics and lightweight constructions are the most important requirements for rail vehicles. However, standards are currently used for the design of rail components and most of them are not actual anymore and the origin has been lost during the years. Moreover, it often leads to an oversizing of the components and thus to higher costs in production and operation due to a higher mass. In order to respect our lightweight requirements, new materials can be used or the components can be designed according to the loads applied during operation. This has the advantage that the components can be designed according to the real load and thus lead to a mass reduction overall. In our case, sensors, such as accelerometers and strain gauges are used to determine the acting loads. The main goal is to use then these loads for Finite-Element-Method(FEM) simulations or fatigue analysis. Forces are the easiest loads to define on a FEM model. But the force cannot be so easily determined from accelerometers or strain gauge because of non-linear problems. That is why, Machine learning method is used to solve the inverse problem. Sensors data, named acceleration and strain are given to the neuronal network (NN) and as output, the force is given. In order to train the NN, a large amount of couple acceleration/force or strain/force are needed. To that end, a FEM model was created to generate these training data. A code has been developed to create and run automatically FEM simulations, changing the input force. Finally, the output force results from the NN model can be used for FEM simulations, design optimization, like topology optimization for example or/and fatigue analysis.

elib-URL des Eintrags:https://elib.dlr.de/216732/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Method for determining operating loads on rail vehicles using artificial intelligence tools
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Laporte, MathildeMathilde.Laporte (at) dlr.dehttps://orcid.org/0000-0002-4620-3634NICHT SPEZIFIZIERT
Winkler-Höhn, RobertRobert.Hoehn (at) dlr.dehttps://orcid.org/0009-0003-1116-6786NICHT SPEZIFIZIERT
Buhr, AlexanderAlexander.Buhr (at) dlr.dehttps://orcid.org/0000-0002-6805-2531NICHT SPEZIFIZIERT
Bell, JamesJames.Bell (at) dlr.dehttps://orcid.org/0000-0001-8319-9817NICHT SPEZIFIZIERT
Köppel, MartinDB InfraGO AGNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:31 Januar 2025
Referierte Publikation:Ja
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:Machine Learning, Operating Loads, Load Spectra, Optimized design
Veranstaltungstitel:NAFEMS
Veranstaltungsort:Salzburg, Austria
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:19 Mai 2025
Veranstaltungsende:22 Mai 2025
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Verkehr
HGF - Programmthema:Schienenverkehr
DLR - Schwerpunkt:Verkehr
DLR - Forschungsgebiet:V SC Schienenverkehr
DLR - Teilgebiet (Projekt, Vorhaben):V - RoSto - Rolling Stock
Standort: Stuttgart
Institute & Einrichtungen:Institut für Aerodynamik und Strömungstechnik > Bodengebundene Fahrzeuge
Institut für Fahrzeugkonzepte > Fahrzeugarchitekturen und Leichtbaukonzepte
Hinterlegt von: Laporte, Mathilde
Hinterlegt am:24 Sep 2025 10:17
Letzte Änderung:24 Sep 2025 10:17

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