An Efficient and Scalable Hyperdimensional Computing Framework for Anomaly Classification in Industrial Systems

Víctor Ortega, Soledad Escolar, Fernando Rincón, Jesús Barba, Julián Caba

2025

Abstract

This paper presents a hyperdimensional computing (HDC)-based framework for anomaly classification, designed to meet the specific demands of industrial systems. Inspired by cognitive processes, HDC employs high-dimensional representations to enable robust, low-complexity, and hardware-efficient computation. The proposed framework encompasses the entire processing pipeline, from data encoding to anomaly classification, and is optimized for efficient execution on both conventional computing platforms and resource-constrained devices. To assess its effectiveness, we conduct a case study based on a real-world scenario involving 118 emergency lighting devices that collect and transmit operational data to a central sink capable of detecting anomalous behavior. Experimental results demonstrate that the proposed approach achieves high classification accuracy and confirm its suitability for deployment in integrated industrial systems with limited computational resources.

Paper Citation

in Harvard Style

Ortega V., Escolar S., Rincón F., Barba J. and Caba J. (2025). An Efficient and Scalable Hyperdimensional Computing Framework for Anomaly Classification in Industrial Systems. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 429-436. DOI: 10.5220/0013669700003982

in Bibtex Style

@conference{icinco25,
author={Víctor Ortega and Soledad Escolar and Fernando Rincón and Jesús Barba and Julián Caba},
title={An Efficient and Scalable Hyperdimensional Computing Framework for Anomaly Classification in Industrial Systems},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2025},
pages={429-436},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013669700003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - An Efficient and Scalable Hyperdimensional Computing Framework for Anomaly Classification in Industrial Systems
SN - 978-989-758-770-2
AU - Ortega V.
AU - Escolar S.
AU - Rincón F.
AU - Barba J.
AU - Caba J.
PY - 2025
SP - 429
EP - 436
DO - 10.5220/0013669700003982
PB - SciTePress