VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910620749660160 |
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| author | Mascolini, Alessio Gaiardelli, Sebastiano Ponzio, Francesco Dall'Ora, Nicola Macii, Enrico Vinco, Sara Di Cataldo, Santa Fummi, Franco |
| author_facet | Mascolini, Alessio Gaiardelli, Sebastiano Ponzio, Francesco Dall'Ora, Nicola Macii, Enrico Vinco, Sara Di Cataldo, Santa Fummi, Franco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14816 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge Mascolini, Alessio Gaiardelli, Sebastiano Ponzio, Francesco Dall'Ora, Nicola Macii, Enrico Vinco, Sara Di Cataldo, Santa Fummi, Franco Machine Learning Artificial Intelligence 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. |
| title | VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2409.14816 |