VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge
- URL: http://arxiv.org/abs/2409.14816v2
- Date: Thu, 26 Sep 2024 09:11:28 GMT
- Title: VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge
- Authors: Alessio Mascolini, Sebastiano Gaiardelli, Francesco Ponzio, Nicola Dall'Ora, Enrico Macii, Sara Vinco, Santa Di Cataldo, Franco Fummi,
- Abstract summary: This work presents 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.
- Score: 7.4646496981460855
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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