Abstract
Production system operators need support for collecting and pre-processing data on production systems consisting of several system components, as foundation for optimization and defect detection. Traditional approaches based on hard-coded programming of such run-time data collection systems take time and effort, and require both domain and technology knowledge. In this article, we introduce the AML-RTDC approach, which combines the strengths of AutomationML (AML) data modeling and model-driven engineering, to reduce the manual effort for realizing the run-time data collection (RTDC) system. We evaluate the feasibility of the AML-RTDC approach with a demonstration case about a lab-sized production system and a use case based on real-world requirements.
Compile- and run-time approaches for the selection of efficient data structures for dynamic graph analysis