Information-Driven Monitoring of Production Process: A Semantic Data Model
Oksana B. Petrina, Sergei Marchenkov, Dmitry Georzhevich Korzun · Zenodo (CERN European Organization for Nuclear Research) · 2021
Recent technologies of industrial monitoring provide highly fragmented information. Large amounts of multiparameter sensed data on production process and equipment operation are stored in disparate structures (databases). Effective use of the collected information requires data fusion within an information-driven monitoring system. In this short paper, we design a semantic data model to create a unified information space that fuses events derived from real-time sensed data streams. Our semantic data model considers a hierarchy of production equipment nodes and supports rules for identifying and composing events in the production process under monitoring.