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

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.