In an industrial CPS scenario, the most crucial resource is the availability of data reflecting the different aspects of production. Detecting complex anomalies on massive amounts of data is a crucial Such data consist of multiple interdependent variables rapidly evolving task in Industry 4.0, best addressed by deep learning. However, over time, thus falling under the typical definition of Multivariate available solutions are computationally demanding, requiring cloud Time Series (MTS) [14]. After collection, the time series, originated architectures prone to latency and bandwidth issues. This work by heterogeneous sensors and data sources, are integrated presents VARADE, a novel solution implementing a light autoregressive through Industrial Internet of Things (IIoT) technologies and made framework based on variational inference, which is best available for anomaly detection, visualization, and analysis [27].

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before.