Time-series based simulations of industrial processes are instrumental to optimizing a variety of industrial settings. For example, time-series data can be used for the modeling of manufacturing processes, which in turn are the foundation for simulation-based optimization approaches. In this paper, we describe a use case, developed together with Infineon Technologies Austria AG. In this use case, monitoring data stored in relational databases was used to build process models of industrial chillers through black-box modeling techniques. Optimization algorithms were then applied to find optimal strategies for operating the chillers. Even though the results from this approach were convincing, the access to the necessary data was a labor-intensive and error-prone task. Therefore, in this paper, we investigate how Semantic Web technologies can help to improve data access for time-series data and under which circumstances they would be helpful for the domain experts performing the simulation.
MSc.
TU Wien
https://ifs.tuwien.ac.at
After spending the last six months of his studies in Bolzano, Benjamin will finish his PhD on knowledge graphs in the manufacturing domain at TU Wiens Institute for Production Engineering and Photonic Technologies in July 2019. He successfully carried out numerous Industry projects in the context of "Industry 4.0".
TU Wien
https://ifs.tuwien.ac.at
Fajar J. Ekaputra M.T. is a PhD student at TU Vienna. His current research topic focuses on Knowledge Change Management in Multi-Disciplinary Engineering Environments using Semantic Web Technologies.
