An Architecture for Visualization of Industrial Automation Data

Guillaume Prévost, Jan Olaf Blech, Keith Foster, Heinrich W. Schmidt

2017

Abstract

We introduce a framework for visualization of data originating from industrial automation devices. Our framework uses cloud-based services to collect data from industrial automation controllers. Clients can subscribe to the data sources and visualize them in accordance with customer needs. Data from industrial automation facilities is associated with formal semantic models, such as a mathematical representation of the material flow in a production plant. The formal models are used to represent interdependencies between entities, their functionality and other descriptive elements. Ultimately this is used in the visualization and for reasoning about systems. In addition to the software framework we describe work on our demonstrator: an example factory with Raspberry Pi-based controllers that are interconnected via standard ethernet technology.

Paper Citation

in Harvard Style

Prévost G., Blech J., Foster K. and Schmidt H. (2017). An Architecture for Visualization of Industrial Automation Data . In Proceedings of the 12th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-250-9, pages 38-46. DOI: 10.5220/0006289700380046

in Bibtex Style

@conference{enase17,
author={Guillaume Prévost and Jan Olaf Blech and Keith Foster and Heinrich W. Schmidt},
title={An Architecture for Visualization of Industrial Automation Data},
booktitle={Proceedings of the 12th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2017},
pages={38-46},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006289700380046},
isbn={978-989-758-250-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - An Architecture for Visualization of Industrial Automation Data
SN - 978-989-758-250-9
AU - Prévost G.
AU - Blech J.
AU - Foster K.
AU - Schmidt H.
PY - 2017
SP - 38
EP - 46
DO - 10.5220/0006289700380046