  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.
