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Automatisierte Betriebszustandsdefinition auf Basis von Maschinellem Lernen und Pseudoschädigungsrechnung

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Description

Präzise Lastannahmen kombinieren die Lastprofile verschiedener Betriebszustände und bilden die Grundlage für die Auslegung zuverlässiger und gleichzeitig wirtschaftlicher mechanischer Systeme. Aktuell erfolgt die Definition von Betriebszuständen maßgeblich auf Basis von Expertenwissen. Bekannte Betriebszustände werden manuell oder anhand von Sensordaten automatisiert zugewiesen. Die Identifikation der für die Lastannahmen relevanten Betriebszustände ist bisher nicht automatisierbar und stellt häufig eine zeitintensive Aufgabe dar.
Auf Basis der Scattering Transformation und Methoden des Maschinellen Lernens lassen sich zuvor undefinierte Betriebszustände aus Last- oder Beanspruchungs-Zeit-Verläufen ableiten. Dabei werden Signal- und Schädigungscharakteristiken aus dem Zeitverlauf analysiert und ähnliche Bereiche zu Betriebszuständen mit ihrem jeweiligen Lastprofil gruppiert. Die Betriebszustände können anhand eines Nutzungsprofils extrapoliert und akkumuliert werden, um ein gesamtes Lastprofil abzuschätzen. Durch den Vergleich von vorhergesagtem mit tatsächlichen Lastprofil mittels Pseudoschädigungsrechnung lässt sich der Algorithmus automatisiert und ohne tiefgreifendes Expertenwissen parametrieren.
Das Vorgehen wird an einem öffentlich zugänglichen eBike-Datensatz demonstiert. Die automatisch generierten Betriebszustände kommen für die Vorhersage eines Lastprofils an unabhängigen Testdaten zum Einsatz. Die Güte der Schädigungsvorhersage auf Basis des betriebszustandsbasierten Lastprofils wird mit einem bestehenden Modell zur Schädigungsregression für denselben Datensatz verglichen. Da der Algorithmus nur mit Last- oder Beanspruchungsdaten parametriert wird, ist von einer breiten Anwendbarkeit, insbesondere in der Automobil- oder Landtechnik, auszugehen.

Reference: BF-2024-395

Title: Automatisierte Betriebszustandsdefinition auf Basis von Maschinellem Lernen und Pseudoschädigungsrechnung
Author(s): J. Riebe, P. Hantschke, A. Griesing, M. Kästner
DOI: 10.48447/BF-2024-395
Event: 50. Tagung DVM-Arbeitskreis Betriebsfestigkeit – Tagung 2024
Year of publication: 2024
Publication type: conference paper (PDF)
Language: German
Keywords: Lastannahme,Unsupervised Learning,Betriebszustand,Schädigung,Künstliche Intelligenz

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Zertifizierungskonformer Strukturnachweis von zyklisch-hochbelasteten Großwälzlagerringen auf Basis der FKM Richtlinie

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€25.00

Online only

Editor(s): Dr.-Ing. M. Decker

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50. Tagung des DVM-Arbeitskreises Betriebsfestigkeit

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Author(s): M. Hupka, R. Rennert, M. Wächter, C. Müller, A. Esderts

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Berücksichtigung von Bauteileinflüssen auf die Festigkeit von Kupferwerkstoffen

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1

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1

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Author(s): J. Hug, S. Hug-Lohmüller

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Thema 1: Dauerfestigkeit - …und es gibt sie doch!?!

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€25.00

Online only

Author(s): V. Landersheim, J. Hansmann, M. Wallmichrath, S. Chéreau, T. Bruder, F. Bilger

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Methode zur Betriebsfestigkeitsprüfung strukturintegrierter Hochvoltspeicher mittels eines multiaxialen Schwingtischs

Publication type: conference paper (PDF)

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€25.00

Online only

Author(s): M. Burger, S. Beckmann, S. Chéreau, F. Bilger, D. Meckl

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KI-basierte Ermittlung der mechanischen Beanspruchung am Hochvoltspeicher

Publication type: conference paper (PDF)

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1

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€25.00

Online only

Author(s): S. Krause, T. Lohmüller

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Thema 2: Schäden durch hochfrequente Torsionsschwingungen mit extremen Beschleunigungen

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1

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€25.00

Online only

Author(s): E. Stolz, R. Masendorf, B. Tonn, A. Esderts, P. Lachart

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Online only