Using Prediktor APIS in OPC UA data gathering solutions

Abstract

Open Platform Communications Unified Architecture (OPC UA) is a machine to machine communication protocol and a crucial component in the push towards smart factories. It offers a robust information model that allows bringing context alongside with actual data, crucial as industries become increasingly interconnected. Currently, irregular semantics are present in the real world, as assets are integrated over a long period, introducing multiple different ways the same data can be exposed. This heterogeneity between systems costs additional engineering overhead for integration tasks. The goal of this thesis is to explore Prediktor APIS software and how it can be used to perform semantic unification.

APIS is a software suite developed by Prediktor that, among many other things, offers the possibility to perform contextualization on underlying, external server data, effectively providing a way to expose the data in a desired semantic model for higher-level systems. Contextualization allows for a more logical way to output data instead of having identical measurements displayed in different formats. APIS can also be used to act as an aggregating server, where several different output formats can be gathered into a single endpoint and be exposed from there, offering a more straightforward secure system.

The thesis goes through a basic description of OPC UA concepts necessary for a proper understanding of the goals of this work. A demonstration of mapping variables at the smallest scale is included. Although this seemed to work correctly, it is not enough to tell how suitable the software is in practice for large scale applications. A rule-based bulk mapping tool is being developed by Prediktor but could not be tested for this thesis.