Year 2017
Ella Potka

Management of complex data sets in process industry

The idea of Internet of Things (IoT) is to connect all the devices into one network and to enable interoperability between them. Interoperability benefits also the process industry when the control devices and software can interoperate with management software. One part of the industrial IoT is being able to efficiently analyze the data from the field devices so that for example predictive maintenance can be achieved. Information modelling is needed to enable communication between the different software and to make analyzing data easier. This thesis examines the state of the IoT and the benefits of information modelling. The aim is to find the information modelling standard most suitable for the process industry and to figure out how standard conforming information models are created.

The literature part of this thesis studies the current state and the future of IoT. The focus is especially on the possibilities it brings for the oil and gas industry. A broad collection of information modelling standards is introduced. According to the comparison made, OPC UA was selected in this work as the most suitable standard for the needs of process industry.

In the experimental part the information modelling process is introduced and three OPC UA modelling tools are examined. Instructions for information modelling with OPC UA were created. An OPC UA standard conforming information model of a distillation column was created to be used to configure a soft sensor. The model was validated using expert knowledge. The model was also successfully connected to a data source that was in this case a DCS emulator.

Thesis electronical version can be downloaded from here

This info last modified 27 Apr 2024 by Jukka Kortela