轻量级变分自回归模型，实现在边缘设备上的实时异常检测。
VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge
- 基于变分推断的轻量自回归框架，适合边缘部署。
- 在两种边缘平台均实现最优检测精度、功耗与推理频率平衡。
- 适用于工业4.0中对延迟敏感的实时异常监测场景。
在工业4.0中，对海量数据进行复杂异常检测至关重要，深度学习是最佳解决方案。然而，现有方法计算开销大，依赖云端架构，易受延迟和带宽限制。本文提出VARADE，一种基于变分推断的轻量自回归框架，专为边缘设备实时运行设计。该方法在一条示范生产线中的机械臂上进行了验证，并与多个先进算法对比，在两种不同边缘平台上均实现了异常检测准确率、功耗与推理频率的最佳权衡。
原文摘要 · Abstract (English)
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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