Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0

TL;DR

This paper introduces a novel neuro-symbolic diffusion model for real-time anomaly detection in Industry 4.0, integrating formal industrial knowledge and enabling deployment on embedded systems for smarter manufacturing.

Contribution

It presents a new neuro-symbolic diffusion approach that incorporates industrial ontologies and uses Random Fourier Features for efficient deployment in manufacturing environments.

Findings

Effective real-time anomaly prediction demonstrated

Successful integration of industrial ontologies

Deployment feasibility on embedded systems

Abstract

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.