Towards the Representation of Cross-Domain Quality Knowledge for Efficient Data Analytics

Sebastian Kropatschek, Thorsten Steuer, Elmar Kiesling, Kristof Meixner, Thomas Fruhwirth, Patrik Sommer, Daniel Schachinger, Stefan Biffl

Publication: Chapter in book/Conference proceedingContribution to conference proceedings

Abstract

In Cyber-physical Production System (CPPS) engineering, data analysts and domain experts collaborate to identify likely causes for quality issues. Industry 4.0 production assets can provide a wealth of data for analysis, making it difficult to identify the most relevant data. Because data analysts typically do not posses detailed knowledge of the production process, a key challenge is to discover potential causes that impact product quality with various experts, as knowledge about production processes is typically distributed across various domains. To address this, we highlight the need for cross-domain modelling and outline an approach for effective and efficient quality analysis. Specifically, we introduce the Quality Dependency Graph (QDG) to represent cross-domain knowledge dependencies for efficiently prioritizing data sources. We evaluate the QDG in a feasibility study based on a real-world use case in the automotive industry.

Original languageEnglish
Title of host publicationProceedings - 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728129891
DOIs
Publication statusPublished - 2021
Event26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021 - Virtual, Vasteras, Sweden
Duration: 7 Sept 202110 Sept 2021

Publication series

SeriesIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Volume2021-September
ISSN1946-0740

Conference

Conference26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021
Country/TerritorySweden
CityVirtual, Vasteras
Period7/09/2110/09/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Cause-Effect Graph
  • Cross-Domain Engineering
  • Data Analytics
  • Knowledge Engineering

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