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 language | English |
---|---|
Title of host publication | Proceedings - 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728129891 |
DOIs | |
Publication status | Published - 2021 |
Event | 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021 - Virtual, Vasteras, Sweden Duration: 7 Sept 2021 → 10 Sept 2021 |
Publication series
Series | IEEE International Conference on Emerging Technologies and Factory Automation, ETFA |
---|---|
Volume | 2021-September |
ISSN | 1946-0740 |
Conference
Conference | 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021 |
---|---|
Country/Territory | Sweden |
City | Virtual, Vasteras |
Period | 7/09/21 → 10/09/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- Cause-Effect Graph
- Cross-Domain Engineering
- Data Analytics
- Knowledge Engineering