VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge
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Abstract (arXiv)
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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References · 6
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2016Variational Inference: A Review for Statisticianscited by 1,466
- 2017A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Predictioncited by 8
- 2023TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoTcited by 5
- 2017Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Thingscited by 3
- 2021Demand Forecasting in Smart Grid Using Long Short-Term Memorycited by 2
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