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Classification of PROFINET I/O Configurations utilizing Neural Networks

Fulltext:


Authors:

Bjarne Johansson, Björn Leander, Aida Causevic, Alessandro Papadopoulos, Thomas Nolte

Research group:

Complex Real-Time Embedded Systems

Publication Type:

Conference/Workshop Paper

Venue:

The 24th IEEE Conference on Emerging Technologies and Factory Automation

Abstract

In process automation installations, the I/O system connect the field devices to the process controller over a fieldbus, a reliable, real-time capable communication link with signal values cyclical being exchanged with a 10-100 millisecond rate. If a deviation from intended behaviour occurs, analyzing the potentially vast data recordings from the field can be a time consuming and cumbersome task for an engineer. For the engineer to be able to get a full understanding of the problem, knowledge of the used I/O configuration is required. In the problem report, the configuration description is sometimes missing. In such cases it is difficult to use the recorded data for analysis of the problem. In this paper we present our ongoing work towards using neural network models as assistance in the interpretation of an industrial fieldbus communication recording. To show the potential of such an approach we present an example using an industrial setup where fieldbus data is collected and classified. In this context we present an evaluation of the suitability of different neural net configurations and sizes for the problem at hand.

Bibtex

@inproceedings{Johansson5617,
author = {Bjarne Johansson and Bj{\"o}rn Leander and Aida Causevic and Alessandro Papadopoulos and Thomas Nolte},
title = {Classification of PROFINET I/O Configurations utilizing Neural Networks},
month = {September},
year = {2019},
booktitle = {The 24th IEEE Conference on Emerging Technologies and Factory Automation},
url = {http://www.es.mdu.se/publications/5617-}
}