Accessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions
This addresses the issue of incomplete or misinterpreted contextual information in Industry 4.0 event analysis, which can lead to suboptimal results, though it appears incremental as it builds on existing semantic models and OPC UA standards.
The paper tackles the problem of analyzing heterogeneous event data in production systems by proposing an approach to access event data based on context, such as product type or process parameters, and demonstrates it using a sample server based on OPC UA for Machinery Companion Specifications.
The analysis of event data from production systems is the basis for many applications associated with Industry 4.0. However, heterogeneous and disjoint data is common in this domain. As a consequence, contextual information of an event might be incomplete or improperly interpreted which results in suboptimal analysis results. This paper proposes an approach to access a production systems' event data based on the event data's context (such as the product type, process type or process parameters). The approach extracts filtered event logs from a database system by combining: 1) a semantic model of a production system's hierarchical structure, 2) a formalized process description and 3) an OPC UA information model. As a proof of concept we demonstrate our approach using a sample server based on OPC UA for Machinery Companion Specifications.