Model-Driven Optimisation of Monitoring System Configurations for Batch Production

Andreas Margraf, Henning Cui, Simon Heimbach, Jörg Hähner, Steffen Geinitz, Stephan Rudolph

2023

Abstract

The increasing need to monitor asset health and the deployment of IoT devices have driven the adoption of non-desctructive testing methods in the industry sector. In fact, they constitute a key to production efficiency. However, engineers still struggle to meet requirements sufficiently due to the complexity and cross-dependency of system parameters. In addition, the design and configuration of industrial monitoring systems remains dependent on recurring issues: data collection, algorithm selection, model configuration and objective function modelling. In this paper, we shine a light on impact factors of machine vision and signal processing in industrial monitoring, from sensor configuration to model development. Since system design requires a deep understanding of the physical characteristics, we apply graph-based design languages to improve the decision and configuration process. Our model and architecture design method are adapted for processing image and signal data in highly sensitive installations to increase transparency, shorten time-to-production and enable defect monitoring in environments with varying conditions. We explore the potential of model selection, pipeline generation and data quality assessment and discuss their impact on representative manufacturing processes.

Paper Citation

in Harvard Style

Margraf A., Cui H., Heimbach S., Hähner J., Geinitz S. and Rudolph S. (2023). Model-Driven Optimisation of Monitoring System Configurations for Batch Production. In Proceedings of the 11th International Conference on Model-Based Software and Systems Engineering - Volume 1: MODELSWARD, ISBN 978-989-758-633-0, pages 176-183. DOI: 10.5220/0011688900003402

in Bibtex Style

@conference{modelsward23,
author={Andreas Margraf and Henning Cui and Simon Heimbach and Jörg Hähner and Steffen Geinitz and Stephan Rudolph},
title={Model-Driven Optimisation of Monitoring System Configurations for Batch Production},
booktitle={Proceedings of the 11th International Conference on Model-Based Software and Systems Engineering - Volume 1: MODELSWARD,},
year={2023},
pages={176-183},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011688900003402},
isbn={978-989-758-633-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Model-Based Software and Systems Engineering - Volume 1: MODELSWARD,
TI - Model-Driven Optimisation of Monitoring System Configurations for Batch Production
SN - 978-989-758-633-0
AU - Margraf A.
AU - Cui H.
AU - Heimbach S.
AU - Hähner J.
AU - Geinitz S.
AU - Rudolph S.
PY - 2023
SP - 176
EP - 183
DO - 10.5220/0011688900003402