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.

翻译：在工业4.0中，从海量数据中检测复杂异常是一项关键任务，深度学习为此提供了最佳解决方案。然而现有方法计算需求高，需依赖易受延迟和带宽限制的云架构。本文提出VARADE，这是一种基于变分推理的轻量自回归框架，特别适合在边缘设备上实时执行。该方法在试点生产线机械臂上得到验证，并与多种先进算法进行对比，在两个不同边缘平台上实现了异常检测精度、功耗与推理频率的最佳平衡。