SAFE: Secure anomaly detection edge AI system For critical Environments

Enhancing safety and efficiency in food, chemical and energy industries
Thematic area: Manufacture, Projects
Financing: ICSC Innovation Grants
Enabling Technology: Advanced Modeling and Simulation, Artificial Intelligence, Machine Learning, Multi-sensor control units

The project SAFE aims to enhance safety and efficiency in food, chemical, and energy industries by developing an Edge AI system for anomaly prediction in critical industrial processes. The system will employ sensors and Machine Learning algorithms for real-time malfunction identification. 

Italian Research Center on High Performance Computing Big Data and Quantum Computing (ICSC), project funded by European Union – NextGenerationEU – and National Recovery and Resilience Plan (NRRP) – Mission 4 Component 2. 

The goal

The goal is to enhance safety and efficiency in industries where a lots of variable could endanger the materials or affect productivity. This is particularly difficult in industries such as food, chemical and energy industries due to the amount of variables and possible dangers that have to be accounted for effective anomaly prediction.

The initial challenge

Considering the need for precise and safe monitoring of classified environments such as food, chemical and energy industries, the project will design a system to enhance efficiency. Variables around safety and efficiency are numerous, which implies that the process will have some degree of complexity.

The solution

Generative AI techniques will allow the development of synthetic data generation models that replicate operational anomalies. The Edge AI Model will be then trained on the model architecture best suited for the project needs.


Edge AI systems will be trained on a considerable amount of data, which will both account for existing registered anomalies and concur to create a relevant dataset. Moreover, different malfunction conditions will be tested first in controlled environments to ensure they are easily recognized and resolved. In general, the project will be scalable and made useful to all kinds of industries. 


Participating Spoke


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