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VIRTUELLE SENSOREN FÜR BAUTEILBELASTUNGEN

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Description

Betriebsfestigkeit beschränkt sich im Bereich der Fahrzeugentwicklung schon lange nicht mehr nur auf die Anforderungsdefinition und endet mit der Produktion des Fahrzeugs. Die Analyse von Kundenverhalten und weltweiten Streckenprofilen machen den Weg frei für eine moderne kundengerechte Bauteil- und Gesamtfahrzeugauslegung. Die Kenntnis über Belastungsgrößen direkt im oder am Bauteil sind hierbei essentiell. Herkömmliche physische Sensoren sind aus verschiedenen Gründen ungeeignet, angefangen mit der fehlenden Robustheit gegen Umwelteinflüsse und viel zu hoher Kosten für die Ausstattung von Serienfahrzeugen.
Virtuelle Sensoren oder Softsensoren bilden ein mathematisches Modell ab, welches vorhandene Standardsensoren der Serienfahrzeuge nutzt, um nicht messbare Signale zu prädizieren. Anwendungen finden sich z.B. in der Schätzung der Außen- oder Kühlwassertemperatur, des State of Health (SoH) der Batterie, oder der Vorhersage von Belastungen im Fahrwerk. Letztere sind Gegenstand dieser Arbeit und umfassen z.B. Federwege, Gelenkwellenmomente oder Radkräfte. Die hochfrequente Dynamik und Nichtlinearität dieser Größen machen die Modellierung virtueller Sensoren besonders komplex. Als Modelle werden daher vor allem Algorithmen des Maschinellen Lernens angewendet, die in Kombination mit physikalischen Grundmodellen eine hohe Güte erreichen können. Fokus liegt auf einem performanten und gleichzeitig echtzeitfähigen Sensor, der auf Steuergeräten zum Einsatz kommen kann.
Im Beitrag wird eingegangen auf Signalverarbeitung von Fahrzeugdaten, Modellierung der Algorithmen und Anwendung klassischer Zählverfahren zur Ermittlung einer Pseudoschädigung aus prädizierten Belastungsgrößen, sowie einer Optimierung hinsichtlich betriebsfestigkeitsrelevanter Fehlermetriken.

Reference: BF-2023-102

Title: VIRTUELLE SENSOREN FÜR BAUTEILBELASTUNGEN
Author(s): T. Arnold, S. Glane, F. Grober, L. Muth, W. Sextro
DOI: 10.48447/BF-2023-102
Event: DVM-Arbeitskreis Betriebsfestigkeit 49. Tagung 2023
Year of publication: 2023
Publication type: conference paper (PDF)
Language: German
Keywords: Machine Learning,hybride Modellierung,Automotive Time Series,Echtzeitanwendung,Hochfrequente Signale und CAN

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Author(s): T. Bruder, G. Toplack, G. Jung, S. Chéreau

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Einsatz eines virtuellen Modells zur Ermittlung der Prüffähigkeit auf einem Gesamtfahrzeugprüfstand.

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Author(s): L. Masendorf, A. Esderts

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Einführung eines elektronischen Laborbuches – Fortschritte in der Digitalisierung im Rahmen des Forschungsdatenmanagements...

Publication type: conference paper (PDF)

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Author(s): F. Bilger, S. Chéreau, T. Bruder

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KI-BASIERTE BEANSPRUCHUNGSERMITTLUNG VON BAUTEILEN IN KRAFTFAHRZEUGEN BEI STRUKTUR- UND MODALANREGUNG

Publication type: conference paper (PDF)

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€25.00

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Editor(s): M. Decker

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49. Tagung des DVM-Arbeitskreises Betriebsfestigkeit

Publication type: conference proceedings (PDF)

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€90.00

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Author(s): K. Bauer-Troßmann, S. Bucher, S. Laue, S. Schönborn

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Werkstoffqualifizierung für die zuverlässige Auslegung druckwasserstoffexponierter Komponenten

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Experimentelle und simulative Schwingfestigkeitsuntersuchungen von geschweißten Kupferverbindungen basierend auf der...

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Author(s): R. Rennert, T. Schiemann

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