Detecting complex anomalies on massive amounts of data is a crucial task in Industry 4.0, best addressed by deep learning. However, available solutions are computationally demanding, requiring cloud architectures prone to latency and bandwidth issues. This work presents VARADE, a novel solution implementing a light autoregressive framework based on variational inference, which is best suited for real-time execution on the edge. The proposed approach was validated on a robotic arm, part of a pilot production line, and compared with several state-of-the-art algorithms, obtaining the best trade-off between anomaly detection accuracy, power consumption and inference frequency on two different edge platforms.
@article{arxiv.2409.14816,
title = {VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge},
author = {Alessio Mascolini and Sebastiano Gaiardelli and Francesco Ponzio and Nicola Dall'Ora and Enrico Macii and Sara Vinco and Santa Di Cataldo and Franco Fummi},
journal= {arXiv preprint arXiv:2409.14816},
year = {2024}
}