{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T09:40:11Z","timestamp":1732873211601,"version":"3.30.0"},"reference-count":26,"publisher":"Walter de Gruyter GmbH","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"abstract":"<jats:title>Zusammenfassung<\/jats:title><jats:p>Die modellbasierte Identifikation von Anomalien ist ein weit verbreiteter Ansatz, um Abweichungen vom erwarteten Verhalten eines Cyber-Physical Production Systems (CPPS) zu erkennen. Da die manuelle Erstellung dieser Modelle ein zeitaufw\u00e4ndiger Prozess ist, ist es vorteilhaft, sie aus Daten zu lernen und in einem generischen Formalismus wie z.\u2009B. Timed Automata darzustellen. Die Interpretation solcher Modelle, und somit auch der identifizierten Anomalien, wird jedoch aufgrund fehlender Kontextinformationen erschwert. Dieser Beitrag zielt darauf ab, die modellbasierte Anomalieerkennung in CPPS zu verbessern, indem der gelernte Timed Automaton mit einem formalen Wissensgraphen \u00fcber das System kombiniert wird. Hierdurch werden sowohl das Modell als auch die identifizierten Anomalien mit eindeutiger Semantik in einer gemeinsamen Wissensbasis beschrieben, um den Anlagenbetreibern eine einfachere Interpretation des Modells und der identifizierten Anomalien zu erm\u00f6glichen. Dar\u00fcber hinaus pr\u00e4sentieren die Autoren eine Ontologie der erforderlichen Konzepte und Beziehungen. Der Ansatz wurde an einer prozesstechnischen Mischanlage validiert. Es konnte gezeigt werden, dass die Interpretierbarkeit der Anomalien und des Automatenmodells durch den generierten Wissensgraphen gesteigert werden konnte.<\/jats:p>","DOI":"10.1515\/auto-2023-0224","type":"journal-article","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T16:26:57Z","timestamp":1728491217000},"page":"896-905","source":"Crossref","is-referenced-by-count":0,"title":["Timed Automata und zeitliche Anomalien in Wissensgraphen von automatisierten Produktionsanlagen"],"prefix":"10.1515","volume":"72","author":[{"given":"Tom","family":"Westermann","sequence":"first","affiliation":[{"name":"Institut f\u00fcr Automatisierungstechnik , Helmut-Schmidt-Universit\u00e4t , Hamburg , Deutschland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Milapji Singh","family":"Gill","sequence":"additional","affiliation":[{"name":"Institut f\u00fcr Automatisierungstechnik , Helmut-Schmidt-Universit\u00e4t , Hamburg , Deutschland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Fay","sequence":"additional","affiliation":[{"name":"Lehrstuhl f\u00fcr Automatisierungstechnik , Ruhr-Universit\u00e4t Bochum , Bochum , Deutschland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"key":"2024100916475773680_j_auto-2023-0224_ref_001","doi-asserted-by":"crossref","unstructured":"E. 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