Anomaly Detection in Industrial Production Products Using OPC-UA and Deep Learning

Henry Velesaca, Henry Velesaca, Doménica Carrasco, Dario Carpio, Juan A. Holgado-Terriza, Jose Gutierrez-Guerrero, Tonny Toscano, Angel Sappa, Angel Sappa

2024

Abstract

In the realm of industrial manufacturing, detecting defects in products is critical for maintaining quality. Traditional methods relying on human inspection are often error-prone and time-consuming. However, advancements in automation and computer vision have led to smarter industrial control systems. This paper explores a novel approach to identifying defects in industrial processes by integrating OPC-UA and YOLO v8. OPC-UA provides a secure communication standard, enabling seamless data exchange between devices, while YOLO v8 provides accurate object detection. By combining these technologies, manufacturers can monitor production lines in near real-time, analyze defects promptly, and take corrective actions. As a result, product quality and operational efficiency are improved. A case study involving tinplate lid defect detection demonstrates the effectiveness of the proposed approach. The system architecture, including PLC integration, image acquisition, and YOLO v8 implementation, is detailed, followed by the performance evaluation of the OPC-UA server and YOLO v8 model integration. Results indicate efficient communication with low Round Trip Times and End-to-End delay, highlighting the potential of this approach for defect detection. The code is available at GitHub: https://github.com/hvelesaca/OPC-UA-YOLOv8-Lid-Anomaly-Detection, facilitating further research.

Paper Citation

in Harvard Style

Velesaca H., Carrasco D., Carpio D., A. Holgado-Terriza J., Gutierrez-Guerrero J., Toscano T. and Sappa A. (2024). Anomaly Detection in Industrial Production Products Using OPC-UA and Deep Learning. In Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-707-8, SciTePress, pages 505-512. DOI: 10.5220/0012812600003756

in Bibtex Style

@conference{data24,
author={Henry Velesaca and Doménica Carrasco and Dario Carpio and Juan A. Holgado-Terriza and Jose Gutierrez-Guerrero and Tonny Toscano and Angel Sappa},
title={Anomaly Detection in Industrial Production Products Using OPC-UA and Deep Learning},
booktitle={Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2024},
pages={505-512},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012812600003756},
isbn={978-989-758-707-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Anomaly Detection in Industrial Production Products Using OPC-UA and Deep Learning
SN - 978-989-758-707-8
AU - Velesaca H.
AU - Carrasco D.
AU - Carpio D.
AU - A. Holgado-Terriza J.
AU - Gutierrez-Guerrero J.
AU - Toscano T.
AU - Sappa A.
PY - 2024
SP - 505
EP - 512
DO - 10.5220/0012812600003756
PB - SciTePress