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SemML: Reusable ML for condition monitoring in discrete manufacturing

Svetashova, Y.; Zhou, B.; Schmid, S.; Pychinsky, T.; Kharlamov, E.

Abstract:
Machine learning (ML) is gaining much attention for data analysis in manufacturing. Despite the success, there is still a number of challenges in widening the scope of ML adoption. The main challenges include the exhausting effort of data integration and lacking of generalisability of developed ML pipelines to diverse data variants, sources, and domain processes. In this demo we present our SemML system that addresses these challenges by enhancing machine learning with semantic technologies: by capturing domain and ML knowledge in ontologies and ontology templates and automating various ML steps using reasoning. During the demo the attendees will experience three cunningly-designed scenarios based on real industrial applications of manufacturing condition monitoring at Bosch, and witness the power of ontologies and templates in enabling reusable ML pipelines.

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Verlagsausgabe §
DOI: 10.5445/IR/1000127042
Veröffentlicht am 27.12.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1613-0073
KITopen-ID: 1000127042
Erschienen in CEUR workshop proceedings
Verlag CEUR Workshop Proceedings
Band 2721
Seiten 214-218
Nachgewiesen in Scopus
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