The main objective of the project is to prevent failures and degradation in industrial equipment, optimizing reliability, resource use, and reducing the company’s carbon footprint.

The solution will consist of a predictive system that analyzes PLC data through a modular and interoperable architecture, integrating Fiware and machine learning for smart and adaptive operational management.

Key challenges addressed:

• Managing heterogeneous data
• Implementing a dynamic data model updated in real-time
• Supporting federated learning to train ML models on sensitive, locally stored data
• System interoperability through middleware for unified communication
• Centralized platform for data visualization and management

The final result will be the release of an alpha version of the integrated platform in a real-world production environment.

Evaluation parameters for the alpha version:

• Availability of all functionalities as defined in the platform architecture
• General progress of the platform development in line with initial user requirements
• Validation through testing with clients, internal staff, and other stakeholders