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

Luigi CapogrossoDept. of Engineering for Innovation Medicine , University of Verona , Italy ,Alessio MascoliniDept. of Control and Computer Engineering , Polytechnic of Turin , Italy ,Federico GirellaDept. of Engineering for Innovation Medicine , University of Verona , Italy ,Geri SkenderiDept. of Engineering for Innovation Medicine , University of Verona , Italy ,Sebastiano GaiardelliDept. of Engineering for Innovation Medicine , University of Verona , Italy ,Nicola Dall’OraDept. of Engineering for Innovation Medicine , University of Verona , Italy ,Francesco PonzioEnrico FraccaroliDept. of Computer Science , University of North Carolina at Chapel Hill , USA,Santa Di CataldoSara VincoDept. of Control and Computer Engineering , Polytechnic of Turin , Italy ,Enrico MaciiFranco FummiDept. of Engineering for Innovation Medicine , University of Verona , Italy ,Marco CristaniDept. of Engineering for Innovation Medicine , University of Verona , Italy ,

2023en

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Annotatsiya

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.

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DOI: 10.48550/arxiv.2307.06975

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