Accessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions

TL;DR

This paper presents a method to improve the analysis of production system event data by integrating semantic models, process descriptions, and OPC UA information models to provide context-aware event trace access.

Contribution

It introduces a novel approach combining semantic modeling, formalized process descriptions, and OPC UA models for context-based event data retrieval in Industry 4.0.

Findings

Effective extraction of contextually filtered event logs

Enhanced interpretation of heterogeneous event data

Demonstrated feasibility with OPC UA-based sample server

Abstract

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.