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Datenanalyse in der intelligenten Fabrik

Part of the Springer Reference Technik book series (VDISR)

Zusammenfassung

Die Mehrheit der Projekte zur Überwachung und Diagnose cyber-physischer Systeme (CPS) beruht auf von (menschlichen) Experten erstellten Modellen. Diese Modelle sind jedoch nur selten verfügbar, sind oft unvollständig, schwer zu überprüfen und zu warten. Datengetriebene Ansätze sind eine viel versprechende Alternative: Diese nutzen die großen Datenmengen die heutzutage in CPS gesammelt werden. Algorithmen verwenden die Daten, um die zur Überwachung notwendigen Modelle automatisch zu lernen. Dabei sind mehrere Herausforderungen zu bewältigen, wie zum Beispiel die Echtzeit-Datenerfassung und Speicherung, Datenanalyse, Mensch-Maschine Schnittstellen, Feedback- und Steuerungsmechanismen. In diesem Beitrag wird eine kognitive Referenzarchitektur vorgeschlagen um diese Herausforderungen in Zukunft einfacher zu lösen. Diese soll durch die Bereitstellung eines Vergleichsschemas sowohl die Wiederverwendung von Algorithmen erleichtern, als auch den wissenschaftlichen Diskurs unterstützen. Anwendungsfälle aus unterschiedlichen Branchen werden schematisch dargestellt und untermauern die Richtigkeit und den Nutzen der Architektur.

Schlüsselwörter

  • Diagnose
  • Datengetrieben
  • Architektur
  • Referenzarchitektur
  • Industrie 4.0
  • Big Data
  • Lernen
  • HMI

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Niggemann, O., Biswas, G., Kinnebrew, J.S., Khorasgani, H., Volgmann, S., Bunte, A. (2017). Datenanalyse in der intelligenten Fabrik. In: Vogel-Heuser, B., Bauernhansl, T., ten Hompel, M. (eds) Handbuch Industrie 4.0 Bd.2. Springer Reference Technik (). Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53248-5_73

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  • DOI: https://doi.org/10.1007/978-3-662-53248-5_73

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